Podcasts about generativeai

AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.Topics Include:Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AIDemandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel trainingGeographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulationsHighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluationsSBI framework applied to AI: Specific behavioral observations create better, more actionable sales coachingAI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinationsArcher handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scaleTwo compliance types explained: Operational (common across companies) versus business-specific regulatory requirementsEU AI Act adoption: US companies embracing European framework for responsible AI governanceHuman oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditableThe bigger AI risk: Companies face greater danger from AI inaction than AI adoptionAgentic AI security challenges: Data layers must enforce permissions before AI access, not afterAI agents need identity management: Same access controls apply whether human clicks or AI actsHuman oversight in high stakes: Chief compliance officers demand transparency and correction capabilitiesFuture challenge identified: 80% of enterprise data behind firewalls remains invisible to AI modelsParticipants:Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk ManagementUmberto Milletti - Chief R&D Officer, DemandbaseOliver Sharp - Co-Founder & Chief AI Officer, HighspotBrian Shadpour - General Manager, Security, Amazon Web ServicesFurther Links:Archer Integrated Risk Management: Website – LinkedIn – AWS MarketplaceDemandbase: Website – LinkedIn – AWS MarketplaceHighspot: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In a fascinating discussion, Rob McGrorty, Product Leader of Agents at Amazon AGI Lab, reveals how rapidly AI agents are evolving with corporate adoption exploding as companies race to deploy production agents and the challenges and advantages they're experiencing.Topics Include:GenAI adoption outpaces all previous tech waves, growing faster than computers or internetEarly adopters tackle complex tasks while newcomers still use basic text manipulation featuresAI models double their single-call task capabilities every seven months, exponentially increasing powerAccelerating progress makes yesterday's magic mundane, unlocking mass creativity and customer demandAgents represent natural evolution: chatbots answered questions, now agents autonomously accomplish tasksAmazon's browser agent finds apartments, maps distances, ranks options using multiple transit modesCorporate adoption exploded: 33% piloting agents in 2024, 67% moving to production nowTwo main agent types today: API calling with tool use, browser automationCurrent applications mirror "RPA 2.0" - form filling, data extraction, website QA testingFuture brings multi-agent systems, self-directing loops, and agent-to-agent negotiation scenariosMajor challenges: data privacy, oversight protocols, error responsibility, and ecosystem sustainabilityTechnical hurdles include real-time accuracy measurement, latency issues, and quality assurance frameworksParticipants:Rob McGrorty – Product Leader, Agents at Amazon AGI LabSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Gagan Singh of Elastic discuses how agentic AI systems reduce analyst burnout by automatically triaging security alerts, resulting in measurable ROI for organizationsTopics Include:AI breaks security silos between teams, data, and tools in SOCsAttackers gain system access; SOC teams have only 40 minutes to detect/containAlert overload causes analyst burnout; thousands of low-value alerts overwhelm teams dailyAI inevitable for SOCs to process data, separate false positives from real threatsAgentic systems understand environment, reason through problems, take action without hand-holdingAttack discovery capability reduces hundreds of alerts to 3-4 prioritized threat discoveriesAI provides ROI metrics: processed alerts, filtered noise, hours saved for organizationsRAG (Retrieval Augmented Generation) prevents hallucination by adding enterprise context to LLMsAWS integration uses SageMaker, Bedrock, Anthropic models with Elasticsearch vector database capabilitiesEnd-to-end LLM observability tracks costs, tokens, invocations, errors, and performance bottlenecksJunior analysts detect nation-state attacks; teams shift from reactive to proactive securityFuture requires balancing costs, data richness, sovereignty, model choice, human-machine collaborationParticipants:Gagan Singh – Vice President Product Marketing, ElasticAdditional Links:Elastic – LinkedIn - Website – AWS Marketplace See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Pete Rubio reveals how Rapid7 transformed to an AI-first platform that automates security investigations and accelerates results from hours to seconds.Topics Include:Pete Rubio introduces Rapid7's journey to becoming an AI-first cybersecurity platformCybersecurity teams overwhelmed by growing attack surfaces and constant alert fatigueCustomers needed faster response times, not just more alerts coming fasterLegacy tools created silos requiring manual triage that doesn't scale effectivelyAI must turn raw security data into real-time decisions humans can trustUnified data platform correlates infrastructure, applications, identity, and business context togetherAgentic AI automates investigative work, reducing analyst tasks from hours to secondsRapid7 evaluated multiple vendors, choosing AWS for performance, cost, and flexibilityNova models delivered unmatched performance for global scaling at controlled costsBedrock provided secure model deployment with governance and data privacy boundariesAWS partnership enabled co-development and rapid iteration beyond typical vendor relationshipsTransparent AI shows customers how models reach conclusions before automated actionsSOC analyst expertise continuously trains models with real-time security intelligenceGovernance frameworks and guardrails implemented from day one, not retrofitted laterFuture plans include customer AI integration and bring-your-own-model capabilitiesParticipants:Pete Rubio – Senior Vice President, Platform & Engineering, Rapid7Additional Links:Rapid 7 – LinkedIn - Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Panther CEO William Lowe explains how integrating Amazon Bedrock AI into their security platform delivered 50% faster alert resolution for enterprise customers while maintaining the trust and control that security practitioners demand.Topics Include:Panther CEO explains how Amazon partnership accelerates security outcomes for customersCloud-native security platform delivers 100% visibility across enterprise environments at scaleCustomers like Dropbox and Coinbase successfully replaced Splunk with Panther's solutionPlatform processes petabytes monthly with impressive 2.3-minute average threat detection timeCritical gap identified: alert resolution still takes 8 hours despite fast detectionSecurity teams overwhelmed by growing attack surfaces and severe talent burnoutConstant context switching across tools creates inefficiency and organizational collaboration problemsAI integration with Amazon Bedrock designed to accelerate security team decision-makingFour trust principles: verifiable actions, secure design, human control, customer data ownershipResults show 50% faster alert triage; future includes Slack integration and automationParticipants:· William H Lowe – CEO, PantherSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS executives reveal how generative AI is fundamentally reshaping ISV business models, from pricing strategies to go-to-market approaches, and provide actionable insights for software companies navigating this transformation.Topics Include:Alayna Broaderson and Andy Perkins introduce AWS Infrastructure Partnerships and ISV SalesGenerative AI profoundly changing how ISVs build, deliver and market software productsTwo ISV categories emerging: established SaaS companies versus pure gen AI startupsLegacy SaaS firms struggle with infrastructure modernization and potential revenue cannibalizationPure gen AI companies face scaling challenges, reliability issues and cost optimizationRevenue models shifting from subscription-based to consumption-based pricing per token/prompt/taskFuture-proofing architecture critical as technology evolves rapidly like F-35 fighter jetsData becoming key differentiator, especially domain-specific datasets in healthcare and legalBalancing cost, accuracy, latency and customer experience creates complex optimization challengesMultiple specialized models replacing single solutions, with agentic AI accelerating this trendHuman capital challenges include retraining engineering teams and finding expensive AI talentSecurity, compliance and explainability now mandatory - no more black box solutionsEnterprise customers struggle with data organization and quantifying clear gen AI ROIISV pricing models evolving with tiered structures and targeted vertical use casesTraditional SaaS playbooks failing in generative AI landscape due to ROI uncertaintyPOC-based go-to-market with free trials and case study selling proving most effectivePricing strategies include AI gates, credit systems and separate SKUs for servicesCustomer trust requires proactive security messaging and auditable, transparent AI solutionsModular architecture enables evolution as new technologies emerge in fast-changing marketAWS positioning as ultimate gen AI toolkit partner with ISV collaboration opportunitiesParticipants:Alayna Broaderson - Sr Manager, Infrastructure Technology Partnership, Amazon Web ServicesAndy Perkins - General Manager, US ISV, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Hear how PagerDuty and Zoom built successful AI products using Amazon Q-Index to solve real customer problems like incident response and meeting intelligence, while sharing practical lessons from their early adoption journey.Topics Include:David Gordon introduces AWS Q-Business partnerships with PagerDuty and ZoomMeet Everaldo Aguiar: PagerDuty's Applied AI leader with academia and enterprise backgroundPaul Magnaghi from Zoom brings AI platform scaling experience from SeattleQ-Business launched over a year ago as managed generative AI servicePlatform enables agentic experiences: content discovery, analysis, and process automationBuilt on AWS Bedrock with enterprise guardrails and data source integrationPartners wanted backend capabilities but preferred their own UI and modelsQ-Index provides vector database functionality for ISV partner integrationsEveraldo explains PagerDuty's evolution from traditional ML to generative AI solutionsHistorical challenges: alert fatigue, noise reduction using machine learning approachesNew gen AI opportunities: incident context, relevant data surfacing, automated postmortemsEngineering teams faced learning curve with agents and high-latency user experiencesPaul discusses Zoom's existing AI: virtual backgrounds and voice isolation technologyAI Companion strategy focused on simplicity during complex generative AI adoptionProblem identified: valuable meeting conversations disappear after Zoom calls endCustomer feedback revealed need for enterprise data integration beyond basic summariesGoal: combine unstructured conversations with structured enterprise data seamlesslyPagerDuty Advanced provides agentic AI for on-call engineers during incidentsQ-Index integration accesses internal documentation: Confluence pages, runbooks, proceduresDemo shows Slack integration pulling relevant incident response documentation automaticallyAccess control lists ensure users see only data they're authorized to accessZoom's AI companion panel enables real-time meeting questions and summariesExample use cases: decision tracking, incident analysis, action item identificationAdvice for starting: standardize practices and create internal development templatesSingle data access point reduces legal and security evaluation overheadCenter of excellence approach helps teams move quickly across product divisionsCut through generative AI buzzwords to focus on real user valueFederated AWS Bedrock architecture provides model choice and flexibility meeting customersCustomer trust alignment between Zoom conversations and AWS data handlingGetting started: PagerDuty Advance available now, Zoom AI free with paid add-onsParticipants:Everaldo Aguiar – Senior Engineering Manager, Applied AI, PagerDutyPaul Magnaghi – Head of AI & ISV Go To Market, ZoomDavid Gordon - Global Business Development, Amazon Q for Business. Amazon Web ServicesFurther Links:PagerDuty Website, LinkedIn & AWS MarketplaceZoom Website, LinkedIn & AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Teleport Co-Founder and CEO Ev Kontsevoy discusses the security vs productivity trade-off that plagues growing companies and how Teleport's trusted computing model protects against the exponential growth of cybersecurity threats.Topics Include:Teleport CEO explains how to make infrastructure "nearly unhackable" through trusted computingTraditional security vs productivity trade-off: high security kills team efficiencyCompanies buy every security solution but still get told they're at riskWhy "crown jewels" thinking fails: computers should protect everything at scaleModern infrastructure has too many access paths to enumerate and secureApple's PCC specification shows trusted computing working in real production environmentsAI revolutionizes both offensive and defensive cybersecurity capabilities for everyone80% of companies can't guarantee they've removed all ex-employee accessIdentity fragmentation across systems creates anonymous relationships and security gapsHuman error probability grows exponentially as companies scale in three dimensionsYour laptop already demonstrates trusted computing: seamless access without constant loginsApple ecosystem shows device trust at scale through secure enclavesAI agents need trusted identities just like humans and machinesAWS marketplace partnership accelerates deals and provides strategic account insightsHire someone who understands partnership dynamics before starting with AWSGenerative AI will make identity attacks cheaper and faster than everSecurity responsibility shifting from IT teams to platform engineering teamsTeleport's "steady state invariant": infrastructure locked down except during authorized workTemporary access granted through tickets, then automatically revoked after completionLegacy systems and IoT devices require extending trust models beyond cloud-nativeParticipants:Ev Kontsevoy – Co-Founder and CEO, TeleportFurther Links:Teleport WebsiteTeleport AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Raj Koo, CTO of DTEX Systems, discusses how their enterprise-grade generative AI platform detects and disarms insider threats and enables them to stay ahead of evolving risks.Topics Include:Raj Koo, CTO of DTEX Systems, joins from Adelaide to discuss insider threat detectionDTEX evolved from Adelaide startup to Bay Area headquarters, serving Fortune 500 companiesCompany specializes in understanding human behavior and intention behind insider threatsMarket shifting beyond cyber indicators to focus on behavioral analysis and detectionRecent case: US citizen sold identity to North Korean DPRK IT workersForeign entities used stolen credentials to infiltrate American companies undetectedDTEX's behavioral detection systems helped identify this sophisticated identity theft operationGenerative AI becomes double-edged sword - used by both threat actors and defendersBad actors use AI for fake resumes and deepfake interviewsDTEX uses traditional machine learning for risk modeling, GenAI for analyst interpretationGoal is empowering security analysts to work faster, not replacing human expertiseAWS GenAI Innovation Center helped develop guardrails and usage boundaries for enterpriseChallenge: enterprises must follow rules while hackers operate without ethical constraintsDTEX gains advantage through proprietary datasets unavailable to public AI modelsAWS Bedrock partnership enables private, co-located language models for data securityPrivate preview launched February 2024 with AWS Innovation Center acceleration supportSoftware leaders should prioritize privacy-by-design from day one of GenAI adoptionFuture threat: information sharing shifts from files to AI-powered data queriesMonitoring who asks what questions of AI systems becomes critical security concernDTEX contributes to OpenSearch development while building vector databases for analysisParticipants:Rajan Koo – Chief Technology Officer, DTEX SystemsFurther Links:DTEX Systems WebsiteDTEX Systems AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Half of the internet is fake, bot traffic according to reports. Then we talk about how most of the top Twitch streamers were BUYING viewers to bilk advertisers. The dead internet is here. Watch this podcast episode on YouTube and all major podcast hosts including Spotify. CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. D/REZZED News covers Pixels, Pop Culture, and the Paranormal! We're an independent, opinionated entertainment news blog covering Video Games, Tech, Comics, Movies, Anime, High Strangeness, and more. As part of Clownfish TV, we strive to be balanced, based, and apolitical. Get more news, views and reviews on Clownfish TV News - https://news.clownfishtv.com/ On YouTube - https://www.youtube.com/c/ClownfishTV On Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvg On Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629

Enterprise AI leaders from C3 AI, Resolve AI, and Scale AI reveal how Fortune 100 companies are successfully scaling agentic AI from pilots to production and share secrets for successful AI transformation.Topics Include:Panel introduces three AI leaders from Resolve AI, C3 AI, and Scale AIResolve AI builds autonomous site reliability engineers for production incident responseC3 AI provides full-stack platform for developing enterprise agentic AI workflowsScale AI helps Fortune 100 companies adopt agents with private data integrationMoving from AI pilots to production requires custom solutions, not shrink-wrap softwareSuccess demands working directly with customers to understand their specific workflowsAll enterprise AI solutions need well-curated access to internal data and resourcesSoftware engineering has permanently shifted to agentic coding with no going backAI agents rapidly improving in reasoning, tool use, and contextual understandingIndustry moving from simple co-pilots to agents solving complex multi-step problemsSpiros coins new concept: evolving from "systems of record" to "systems of knowledge"Democratized development platforms let enterprises declare their own agent workflowsSemantic business layers enable agents to understand domain-specific enterprise operationsTrust and observability remain major barriers to enterprise agent adoptionOversight layers essential for agents making longer-horizon autonomous business decisionsPerformance tracking and calibration systems needed like MLOps for reasoning chainsCEO-level top-down support required for successful AI transformation initiativesTraditional per-seat SaaS pricing models completely broken for agentic AI solutionsIndustry shifting toward outcome-based and work-completion pricing models insteadReal examples shared: agent collaboration in production engineering and sales automationParticipants:Nikhil Krishnan – SVP & Chief Technology Officer, Data Science, C3 AISpiros Xanthos – Founder and CEO, Resolve AIVijay Karunamurthy – Head of Engineering, Product and Design / Field Chief Technology Officer, Scale AIAndy Perkins – GM, US ISV Sales – Data, Analytics, GenAI, Amazon Web ServicesFurther Links:C3 – Website – AWS MarketplaceResolve AI – Website – AWS MarketplaceScale AI – Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

A version of this essay has been published by firstpost.com at https://www.firstpost.com/opinion/shadow-warrior-from-crisis-to-advantage-how-india-can-outplay-the-trump-tariff-gambit-13923031.htmlA simple summary of the recent brouhaha about President Trump's imposition of 25% tariffs on India as well as his comment on India's ‘dead economy' is the following from Shakespeare's Macbeth: “full of sound and fury, signifying nothing”. Trump further imposed punitive tariffs totalling 50% on August 6th allegedly for India funding Russia's war machine via buying oil.As any negotiator knows, a good opening gambit is intended to set the stage for further parleys, so that you could arrive at a negotiated settlement that is acceptable to both parties. The opening gambit could well be a maximalist statement, or one's ‘dream outcome', the opposite of which is ‘the walkway point' beyond which you are simply not willing to make concessions. The usual outcome is somewhere in between these two positions or postures.Trump is both a tough negotiator, and prone to making broad statements from which he has no problem retreating later. It's down-and-dirty boardroom tactics that he's bringing to international trade. Therefore I think Indians don't need to get rattled. It's not the end of the world, and there will be climbdowns and adjustments. Think hard about the long term.I was on a panel discussion on this topic on TV just hours after Trump made his initial 25% announcement, and I mentioned an interplay between geo-politics and geo-economics. Trump is annoyed that his Ukraine-Russia play is not making much headway, and also that BRICS is making progress towards de-dollarization. India is caught in this crossfire (‘collateral damage') but the geo-economic facts on the ground are not favorable to Trump.I am in general agreement with Trump on his objectives of bringing manufacturing and investment back to the US, but I am not sure that he will succeed, and anyway his strong-arm tactics may backfire. I consider below what India should be prepared to do to turn adversity into opportunity.The anti-Thucydides Trap and the baleful influence of Whitehall on Deep StateWhat is remarkable, though, is that Trump 2.0 seems to be indistinguishable from the Deep State: I wondered last month if the Deep State had ‘turned' Trump. The main reason many people supported Trump in the first place was the damage the Deep State was wreaking on the US under the Obama-Biden regime. But it appears that the resourceful Deep State has now co-opted Trump for its agenda, and I can only speculate how.The net result is that there is the anti-Thucydides Trap: here is the incumbent power, the US, actively supporting the insurgent power, China, instead of suppressing it, as Graham Allison suggested as the historical pattern. It, in all fairness, did not start with Trump, but with Nixon in China in 1971. In 1985, the US trade deficit with China was $6 million. In 1986, $1.78 billion. In 1995, $35 billion.But it ballooned after China entered the WTO in 2001. $202 billion in 2005; $386 billion in 2022.In 2025, after threatening China with 150% tariffs, Trump retreated by postponing them; besides he has caved in to Chinese demands for Nvidia chips and for exemptions from Iran oil sanctions if I am not mistaken.All this can be explained by one word: leverage. China lured the US with the siren-song of the cost-leader ‘China price', tempting CEOs and Wall Street, who sleepwalked into surrender to the heft of the Chinese supply chain.Now China has cornered Trump via its monopoly over various things, the most obvious of which is rare earths. Trump really has no option but to give in to Chinese blackmail. That must make him furious: in addition to his inability to get Putin to listen to him, Xi is also ignoring him. Therefore, he will take out his frustrations on others, such as India, the EU, Japan, etc. Never mind that he's burning bridges with them.There's a Malayalam proverb that's relevant here: “angadiyil thottathinu ammayodu”. Meaning, you were humiliated in the marketplace, so you come home and take it out on your mother. This is quite likely what Trump is doing, because he believes India et al will not retaliate. In fact Japan and the EU did not retaliate, but gave in, also promising to invest large sums in the US. India could consider a different path: not active conflict, but not giving in either, because its equations with the US are different from those of the EU or Japan.Even the normally docile Japanese are beginning to notice.Beyond that, I suggested a couple of years ago that Deep State has a plan to enter into a condominium agreement with China, so that China gets Asia, and the US gets the Americas and the Pacific/Atlantic. This is exactly like the Vatican-brokered medieval division of the world between Spain and Portugal, and it probably will be equally bad for everyone else. And incidentally it makes the Quad infructuous, and deepens distrust of American motives.The Chinese are sure that they have achieved the condominium, or rather forced the Americans into it. Here is a headline from the Financial Express about their reaction to the tariffs: they are delighted that the principal obstacle in their quest for hegemony, a US-India military and economic alliance, is being blown up by Trump, and they lose no opportunity to deride India as not quite up to the mark, whereas they and the US have achieved a G2 detente.Two birds with one stone: gloat about the breakdown in the US-India relationship, and exhibit their racist disdain for India yet again.They laugh, but I bet India can do an end-run around them. As noted above, the G2 is a lot like the division of the world into Spanish and Portuguese spheres of influence in 1494. Well, that didn't end too well for either of them. They had their empires, which they looted for gold and slaves, but it made them fat, dumb and happy. The Dutch, English, and French capitalized on more dynamic economies, flexible colonial systems, and aggressive competition, overtaking the Iberian powers in global influence by the 17th century. This is a salutary historical parallel.I have long suspected that the US Deep State is being led by the nose by the malign Whitehall (the British Deep State): I call it the ‘master-blaster' syndrome. On August 6th, there was indirect confirmation of this in ex-British PM Boris Johnson's tweet about India. Let us remember he single-handedly ruined the chances of a peaceful resolution of the Ukraine War in 2022. Whitehall's mischief and meddling all over, if you read between the lines.Did I mention the British Special Force's views? Ah, Whitehall is getting a bit sloppy in its propaganda.Wait, so is India important (according to Whitehall) or unimportant (according to Trump)?Since I am very pro-American, I have a word of warning to Trump: you trust perfidious Albion at your peril. Their country is ruined, and they will not rest until they ruin yours too.I also wonder if there are British paw-prints in a recent and sudden spate of racist attacks on Indians in Ireland. A 6-year old girl was assaulted and kicked in the private parts. A nurse was gang-raped by a bunch of teenagers. Ireland has never been so racist against Indians (yes, I do remember the sad case of Savita Halappanavar, but that was religious bigotry more than racism). And I remember sudden spikes in anti-Indian attacks in Australia and Canada, both British vassals.There is no point in Indians whining about how the EU and America itself are buying more oil, palladium, rare earths, uranium etc. from Russia than India is. I am sorry to say this, but Western nations are known for hypocrisy. For example, exactly 80 years ago they dropped atomic bombs on Hiroshima and Nagasaki in Japan, but not on Germany or Italy. Why? The answer is uncomfortable. Lovely post-facto rationalization, isn't it?Remember the late lamented British East India Company that raped and pillaged India?Applying the three winning strategies to geo-economicsAs a professor of business strategy and innovation, I emphasize to my students that there are three broad ways of gaining an advantage over others: 1. Be the cost leader, 2. Be the most customer-intimate player, 3. Innovate. The US as a nation is patently not playing the cost leader; it does have some customer intimacy, but it is shrinking; its strength is in innovation.If you look at comparative advantage, the US at one time had strengths in all three of the above. Because it had the scale of a large market (and its most obvious competitors in Europe were decimated by world wars) America did enjoy an ability to be cost-competitive, especially as the dollar is the global default reserve currency. It demonstrated this by pushing through the Plaza Accords, forcing the Japanese yen to appreciate, destroying their cost advantage.In terms of customer intimacy, the US is losing its edge. Take cars for example: Americans practically invented them, and dominated the business, but they are in headlong retreat now because they simply don't make cars that people want outside the US: Japanese, Koreans, Germans and now Chinese do. Why were Ford and GM forced to leave the India market? Their “world cars” are no good in value-conscious India and other emerging markets.Innovation, yes, has been an American strength. Iconic Americans like Thomas Edison, Henry Ford, and Steve Jobs led the way in product and process innovation. US universities have produced idea after idea, and startups have ignited Silicon Valley. In fact Big Tech and aerospace/armaments are the biggest areas where the US leads these days.The armaments and aerospace tradeThat is pertinent because of two reasons: one is Trump's peevishness at India's purchase of weapons from Russia (even though that has come down from 70+% of imports to 36% according to SIPRI); two is the fact that there are significant services and intangible imports by India from the US, of for instance Big Tech services, even some routed through third countries like Ireland.Armaments and aerospace purchases from the US by India have gone up a lot: for example the Apache helicopters that arrived recently, the GE 404 engines ordered for India's indigenous fighter aircraft, Predator drones and P8-i Poseidon maritime surveillance aircraft. I suspect Trump is intent on pushing India to buy F-35s, the $110-million dollar 5th generation fighters.Unfortunately, the F-35 has a spotty track record. There were two crashes recently, one in Albuquerque in May, and the other on July 31 in Fresno, and that's $220 million dollars gone. Besides, the spectacle of a hapless British-owned F-35B sitting, forlorn, in the rain, in Trivandrum airport for weeks, lent itself to trolls, who made it the butt of jokes. I suspect India has firmly rebuffed Trump on this front, which has led to his focus on Russian arms.There might be other pushbacks too. Personally, I think India does need more P-8i submarine hunter-killer aircraft to patrol the Bay of Bengal, but India is exerting its buyer power. There are rumors of pauses in orders for Javelin and Stryker missiles as well.On the civilian aerospace front, I am astonished that all the media stories about Air India 171 and the suspicion that Boeing and/or General Electric are at fault have disappeared without a trace. Why? There had been the big narrative push to blame the poor pilots, and now that there is more than reasonable doubt that these US MNCs are to blame, there is a media blackout?Allegations about poor manufacturing practices by Boeing in North Charleston, South Carolina by whistleblowers have been damaging for the company's brand: this is where the 787 Dreamliners are put together. It would not be surprising if there is a slew of cancellations of orders for Boeing aircraft, with customers moving to Airbus. Let us note Air India and Indigo have placed some very large, multi-billion dollar orders with Boeing that may be in jeopardy.India as a consuming economy, and the services trade is hugely in the US' favorMany observers have pointed out the obvious fact that India is not an export-oriented economy, unlike, say, Japan or China. It is more of a consuming economy with a large, growing and increasingly less frugal population, and therefore it is a target for exporters rather than a competitor for exporting countries. As such, the impact of these US tariffs on India will be somewhat muted, and there are alternative destinations for India's exports, if need be.While Trump has focused on merchandise trade and India's modest surplus there, it is likely that there is a massive services trade, which is in the US' favor. All those Big Tech firms, such as Microsoft, Meta, Google and so on run a surplus in the US' favor, which may not be immediately evident because they route their sales through third countries, e.g. Ireland.These are the figures from the US Trade Representative, and quite frankly I don't believe them: there are a lot of invisible services being sold to India, and the value of Indian data is ignored.In addition to the financial implications, there are national security concerns. Take the case of Microsoft's cloud offering, Azure, which arbitrarily turned off services to Indian oil retailer Nayara on the flimsy grounds that the latter had substantial investment from Russia's Rosneft. This is an example of jurisdictional over-reach by US companies, which has dire consequences. India has been lax about controlling Big Tech, and this has to change.India is Meta's largest customer base. Whatsapp is used for practically everything. Which means that Meta has access to enormous amounts of Indian customer data, for which India is not even enforcing local storage. This is true of all other Big Tech (see OpenAI's Sam Altman below): they are playing fast and loose with Indian data, which is not in India's interest at all.Data is the new oil, says The Economist magazine. So how much should Meta, OpenAI et al be paying for Indian data? Meta is worth trillions of dollars, OpenAI half a trillion. How much of that can be attributed to Indian data?There is at least one example of how India too can play the digital game: UPI. Despite ham-handed efforts to now handicap UPI with a fee (thank you, brilliant government bureaucrats, yes, go ahead and kill the goose that lays the golden eggs), it has become a contender in a field that has long been dominated by the American duopoly of Visa and Mastercard. In other words, India can scale up and compete.It is unfortunate that India has not built up its own Big Tech behind a firewall as has been done behind the Great Firewall of China. But it is not too late. Is it possible for India-based cloud service providers to replace US Big Tech like Amazon Web Services and Microsoft Azure? Yes, there is at least one player in that market: Zoho.Second, what are the tariffs on Big Tech exports to India these days? What if India were to decide to impose a 50% tax on revenue generated in India through advertisement or through sales of services, mirroring the US's punitive taxes on Indian goods exports? Let me hasten to add that I am not suggesting this, it is merely a hypothetical argument.There could also be non-tariff barriers as China has implemented, but not India: data locality laws, forced use of local partners, data privacy laws like the EU's GDPR, anti-monopoly laws like the EU's Digital Markets Act, strict application of IPR laws like 3(k) that absolutely prohibits the patenting of software, and so on. India too can play legalistic games. This is a reason US agri-products do not pass muster: genetically modified seeds, and milk from cows fed with cattle feed from blood, offal and ground-up body parts.Similarly, in the ‘information' industry, India is likely to become the largest English-reading country in the world. I keep getting come-hither emails from the New York Times offering me $1 a month deals on their product: they want Indian customers. There are all these American media companies present in India, untrammelled by content controls or taxes. What if India were to give a choice to Bloomberg, Reuters, NYTimes, WaPo, NPR et al: 50% tax, or exit?This attack on peddlers of fake information and manufacturing consent I do suggest, and I have been suggesting for years. It would make no difference whatsoever to India if these media outlets were ejected, and they surely could cover India (well, basically what they do is to demean India) just as well from abroad. Out with them: good riddance to bad rubbish.What India needs to doI believe India needs to play the long game. It has to use its shatrubodha to realize that the US is not its enemy: in Chanakyan terms, the US is the Far Emperor. The enemy is China, or more precisely the Chinese Empire. Han China is just a rump on their south-eastern coast, but it is their conquered (and restive) colonies such as Tibet, Xinjiang, Manchuria and Inner Mongolia, that give them their current heft.But the historical trends are against China. It has in the past had stable governments for long periods, based on strong (and brutal) imperial power. Then comes the inevitable collapse, when the center falls apart, and there is absolute chaos. It is quite possible, given various trends, including demographic changes, that this may happen to China by 2050.On the other hand, (mostly thanks, I acknowledge, to China's manufacturing growth), the center of gravity of the world economy has been steadily shifting towards Asia. The momentum might swing towards India if China stumbles, but in any case the era of Atlantic dominance is probably gone for good. That was, of course, only a historical anomaly. Asia has always dominated: see Angus Maddison's magisterial history of the world economy, referred to below as well.I am reminded of the old story of the king berating his court poet for calling him “the new moon” and the emperor “the full moon”. The poet escaped being punished by pointing out that the new moon is waxing and the full moon is waning.This is the long game India has to keep in mind. Things are coming together for India to a great extent: in particular the demographic dividend, improved infrastructure, fiscal prudence, and the increasing centrality of the Indian Ocean as the locus of trade and commerce.India can attempt to gain competitive advantage in all three ways outlined above:* Cost-leadership. With a large market (assuming companies are willing to invest at scale), a low-cost labor force, and with a proven track-record of frugal innovation, India could well aim to be a cost-leader in selected areas of manufacturing. But this requires government intervention in loosening monetary policy and in reducing barriers to ease of doing business* Customer-intimacy. What works in highly value-conscious India could well work in other developing countries. For instance, the economic environment in ASEAN is largely similar to India's, and so Indian products should appeal to their residents; similarly with East Africa. Thus the Indian Ocean Rim with its huge (and in Africa's case, rapidly growing) population should be a natural fit for Indian products* Innovation. This is the hardest part, and it requires a new mindset in education and industry, to take risks and work at the bleeding edge of technology. In general, Indians have been content to replicate others' innovations at lower cost or do jugaad (which cannot scale up). To do real, disruptive innovation, first of all the services mindset should transition to a product mindset (sorry, Raghuram Rajan). Second, the quality of human capital must be improved. Third, there should be patient risk capital. Fourth, there should be entrepreneurs willing to try risky things. All of these are difficult, but doable.And what is the end point of this game? Leverage. The ability to compel others to buy from you.China has demonstrated this through its skill at being a cost-leader in industry after industry, often hollowing out entire nations through means both fair and foul. These means include far-sighted industrial policy including the acquisition of skills, technology, and raw materials, as well as hidden subsidies that support massive scaling, which ends up driving competing firms elsewhere out of business. India can learn a few lessons from them. One possible lesson is building capabilities, as David Teece of UC Berkeley suggested in 1997, that can span multiple products, sectors and even industries: the classic example is that of Nikon, whose optics strength helps it span industries such as photography, printing, and photolithography for chip manufacturing. Here is an interesting snapshot of China's capabilities today.2025 is, in a sense, a point of inflection for India just as the crisis in 1991 was. India had been content to plod along at the Nehruvian Rate of Growth of 2-3%, believing this was all it could achieve, as a ‘wounded civilization'. From that to a 6-7% growth rate is a leap, but it is not enough, nor is it testing the boundaries of what India can accomplish.1991 was the crisis that turned into an opportunity by accident. 2025 is a crisis that can be carefully and thoughtfully turned into an opportunity.The Idi Amin syndrome and the 1000 Talents program with AIThere is a key area where an American error may well be a windfall for India. This is based on the currently fashionable H1-B bashing which is really a race-bashing of Indians, and which has been taken up with gusto by certain MAGA folks. Once again, I suspect the baleful influence of Whitehall behind it, but whatever the reason, it looks like Indians are going to have a hard time settling down in the US.There are over a million Indians on H1-Bs, a large number of them software engineers, let us assume for convenience there are 250,000 of them. Given country caps of exactly 9800 a year, they have no realistic chance of getting a Green Card in the near future, and given the increasingly fraught nature of life there for brown people, they may leave the US, and possibly return to India..I call this the Idi Amin syndrome. In 1972, the dictator of Uganda went on a rampage against Indian-origin people in his country, and forcibly expelled 80,000 of them, because they were dominating the economy. There were unintended consequences: those who were ejected mostly went to the US and UK, and they have in many cases done well. But Uganda's economy virtually collapsed.That's a salutary experience. I am by no means saying that the US economy would collapse, but am pointing to the resilience of the Indians who were expelled. If, similarly, Trump forces a large number of Indians to return to India, that might well be a case of short-term pain and long-term gain: urvashi-shapam upakaram, as in the Malayalam phrase.Their return would be akin to what happened in China and Taiwan with their successful effort to attract their diaspora back. The Chinese program was called 1000 Talents, and they scoured the globe for academics and researchers of Chinese origin, and brought them back with attractive incentives and large budgets. They had a major role in energizing the Chinese economy.Similarly, Taiwan with Hsinchu University attracted high-quality talent, among which was the founder of TSMC, the globally dominant chip giant.And here is Trump offering to India on a platter at least 100,000 software engineers, especially at a time when generativeAI is decimating low-end jobs everywhere. They can work on some very compelling projects that could revolutionize Indian education, up-skilling and so on, and I am not at liberty to discuss them. Suffice to say that these could turbo-charge the Indian software industry and get it away from mundane, routine body-shopping type jobs.ConclusionThe Trump tariff tantrum is definitely a short-term problem for India, but it can be turned around, and turned into an opportunity, if only the country plays its cards right and focuses on building long-term comparative advantages and accepting the gift of a mis-step by Trump in geo-economics.In geo-politics, India and the US need each other to contain China, and so that part, being so obvious, will be taken care of more or less by default.Thus, overall, the old SWOT analysis: strengths, weaknesses, opportunities and threats. On balance, I am of the opinion that the threats contain in them the germs of opportunities. It is up to Indians to figure out how to take advantage of them. This is your game to win or lose, India!4150 words, 9 Aug 2025 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit rajeevsrinivasan.substack.com/subscribe

Industry leaders from Automation Anywhere and AWS discuss how modern customer data collection has evolved, and practical strategies for implementing enterprise automation at scale.Topics Include:Automation Anywhere and AWS experts discuss modern enterprise automation strategiesTraditional profiting strategies may not work with today's changing business modelsCustomer data collection methods have evolved across multiple platforms significantlyModern verification processes include automated validation systems and streamlined timelinesBackground check automation is increasingly handled by AI-powered models and systemsStanford's "Wonder Bread" research paper introduced revolutionary enterprise process observation technologyWonder Bread demonstrated AI systems watching and automatically learning hospital workflowsThe technology can author workflows by observing real enterprise processesEnterprise Process Management built around observed behaviors shows promising resultsVerification challenges exist since Wonder Bread research isn't widely publicized yetProcess observation technology could transform how enterprises handle workflow creationSalesforce Wizard Interface dominates many current automation implementations in enterprisesSalesforce Agent Codes offer alternative approaches to traditional automation methodsAWS platform selection involves careful consideration of enterprise integration needsDemo implementations showcase real-world timeline expectations and deployment maturity levelsCurrent automation solutions have reached significant scale across various industriesWorkflow automation differs fundamentally from true agentic intelligence systems capabilitiesAgentic AI demonstrates autonomous decision-making beyond simple rule-based automation processesUnderstanding this distinction helps organizations choose appropriate technology approaches effectivelySession concludes with clarity on modern automation landscape and implementation strategiesParticipants:Pratyush Garikapati – Director of Products, Automation AnywhereSreenath Gotur – Snr Generative AI Specialist, Amazon Web ServicesFurther Links:Automation Anywhere websiteAutomation Anywhere – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Making Tech Work for SMBs with Steve Massaro | DailyCyber 271 In this episode of DailyCyber, I'm joined by Steve Massaro, IT consultant, MSP owner, and strategic technologist with over 20 years of experience helping businesses succeed through managed services and secure IT leadership.Steve shares insight from his years building MSPs in Hawaii and Kansas—from scaling tech teams to keeping SMB clients safe in today's evolving threat landscape.

On this episode, host Paul W. Grimm speaks with Professor Maura R. Grossman about the fundamentals of artificial intelligence and its growing influence on the legal system. They explore what AI is (and isn't), how machine learning and natural language processing work, and the differences between traditional automation and modern generative AI. In layman's terms, they discuss other key concepts, such as supervised and unsupervised learning, reinforcement training, and deepfakes, and other advances that have accelerated AI's development. Finally, they address a few potential risks of generative AI, including hallucinations, bias, and misuse in court, which sets the stage for a deeper conversation about legal implications on the next episode, "To Trust or Not to Trust: AI in Legal Practice." ABOUT THE HOSTJudge Paul W. Grimm (ret.) is the David F. Levi Professor of the Practice of Law and Director of the Bolch Judicial Institute at Duke Law School. From December 2012 until his retirement in December 2022, he served as a district judge of the United States District Court for the District of Maryland, with chambers in Greenbelt, Maryland. Click here to read his full bio.

AWS's Mark Relph draws fascinating parallels between today's AI revolution and the 1900s agricultural mechanization that delivered 2,000% productivity gains, while exploring how agentic AI will fundamentally reshape every aspect of software business models.Topics Include:Mark Relph directs AWS's data and AI partner go-to-market strategy teamHis role focuses on making ISV partners a force multiplier for customer successPreviously ran go-to-market for Amazon Bedrock, AWS's fastest growing service everCurrent AI adoption pace exceeds even the early cloud computing boom yearsHistorical parallel: 1900s agricultural mechanization delivered 2,000% productivity gains and 95% resource reductionFirst commercial self-propelled farming equipment revolutionized entire economies and never looked back500 machines formed the "Harvest Brigade" during WWII, harvesting from Texas to CanadaMark has spoken to 600+ AWS customers about GenAI over two yearsOrganizations range from AI pioneers to those still "fending off pirates" internallyGenAI has become a phenomenal assistant within organizations for content and automationAWS's AI stack has three layers: infrastructure, Bedrock, and applicationsBottom layer provides complete control over training, inference, and custom applicationsMiddle layer Bedrock serves as the "operating system" for generative AI applicationsTop layer offers ready-to-use AI through Q assistants and productivity toolsAI systems are rapidly becoming more complex with multiple model chainsMany current "agents" are just really, really long prompts (Mark's hot take)Task-specific models are emerging as one size won't fit all use casesEvolution moves from human-driven AI to agent-assisted to fully autonomous agentsAgent readiness requires APIs that allow software to interact autonomouslyTraditional UIs become unnecessary when agents interface directly with systemsCore competencies shift when AI handles the actual "doing" of tasksSales and marketing must adapt to agents delivering outcomes autonomouslyGo-to-market strategies need complete rethinking for an agentic worldThe agentic age is upon us and AWS partners should shape the futureParticipants:Mark Relph – Director – Data & AI Partner Go-To-Market, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this episode, hosts Lois Houston and Nikita Abraham, together with Senior Cloud Engineer Nick Commisso, break down the basics of artificial intelligence (AI). They discuss the differences between Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI), and explore the concepts of machine learning, deep learning, and generative AI. Nick also shares examples of how AI is used in everyday life, from navigation apps to spam filters, and explains how AI can help businesses cut costs and boost revenue.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Hello and welcome to the Oracle University Podcast. I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. I'm so excited about this one because we're going to dive into the world of artificial intelligence, speaking to many experts in the field. Nikita: If you've been listening to us for a while, you probably know we've covered AI from a bunch of different angles. But this time, we're dialing it all the way back to basics. We wanted to create something for the absolute beginner, so no jargon, no assumptions, just simple conversations that anyone can follow. 01:08 Lois: That's right, Niki. You don't need to have a technical background or prior experience with AI to get the most out of these episodes. In our upcoming conversations, we'll break down the basics of AI, explore how it's shaping the world around us, and understand its impact on your business. Nikita: The idea is to give you a practical understanding of AI that you can use in your work, especially if you're in sales, marketing, operations, HR, or even customer service.  01:37 Lois: Today, we'll talk about the basics of AI with Senior Cloud Engineer Nick Commisso. Hi Nick! Welcome back to the podcast. Can you tell us about human intelligence and how it relates to artificial intelligence? And within AI, I know we have Artificial General Intelligence, or AGI, and Artificial Narrow Intelligence, or ANI. What's the difference between the two? Nick: Human intelligence is the intellectual capability of humans that allow us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using language and understand non-verbal cues, such as facial expressions, tone variation, body language. We can handle objections and situations in real time, even in a complex setting. We can plan for short and long-term situations or projects. And we can create music, art, or invent something new or have original ideas. If machines can replicate a wide range of human cognitive abilities, such as learning, reasoning, or problem solving, we call it artificial general intelligence.  Now, AGI is hypothetical for now, but when we apply AI to solve problems with specific, narrow objectives, we call it artificial narrow intelligence, or ANI. AGI is a hypothetical AI that thinks like a human. It represents the ultimate goal of artificial intelligence, which is a system capable of chatting, learning, and even arguing like us. If AGI existed, it would take the form like a robot doctor that accurately diagnoses and comforts patients, or an AI teacher that customizes lessons in real time based on each student's mood, pace, and learning style, or an AI therapist that comprehends complex emotions and provides empathetic, personalized support. ANI, on the other hand, focuses on doing one thing really well. It's designed to perform specific tasks by recognizing patterns and following rules, but it doesn't truly understand or think beyond its narrow scope. Think of ANI as a specialist. Your phone's face ID can recognize you instantly, but it can't carry on a conversation. Google Maps finds the best route, but it can't write you a poem. And spam filters catch junk mail, but it can't make you coffee. So, most of the AI you interact with today is ANI. It's smart, efficient, and practical, but limited to specific functions without general reasoning or creativity. 04:22 Nikita: Ok then what about Generative AI?  Nick: Generative AI is a type of AI that can produce content such as audio, text, code, video, and images. ChatGPT can write essays, but it can't fact check itself. DALL-E creates art, but it doesn't actually know if it's good. Or AI song covers can create deepfakes like Drake singing "Baby Shark."  04:47 Lois: Why should I care about AI? Why is it important? Nick: AI is already part of your everyday life, often working quietly in the background. ANI powers things like navigation apps, voice assistants, and spam filters. Generative AI helps create everything from custom playlists to smart writing tools. And while AGI isn't here yet, it's shaping ideas about what the future might look like. Now, AI is not just a buzzword, it's a tool that's changing how we live, work, and interact with the world. So, whether you're using it or learning about it or just curious, it's worth knowing what's behind the tech that's becoming part of everyday life.  05:32 Lois: Nick, whenever people talk about AI, they also throw around terms like machine learning and deep learning. What are they and how do they relate to AI? Nick: As we shared earlier, AI is the ability of machines to imitate human intelligence. And Machine Learning, or ML, is a subset of AI where the algorithms are used to learn from past data and predict outcomes on new data or to identify trends from the past. Deep Learning, or DL, is a subset of machine learning that uses neural networks to learn patterns from complex data and make predictions or classifications. And Generative AI, or GenAI, on the other hand, is a specific application of DL focused on creating new content, such as text, images, and audio, by learning the underlying structure of the training data.  06:24 Nikita: AI is often associated with key domains like language, speech, and vision, right? So, could you walk us through some of the specific tasks or applications within each of these areas? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, extracting key phrases, and so on.  06:54 Lois: Ok, I get you. That's like translating text, where you can use a text translation tool, type your text in the box, choose your source and target language, and then click Translate. That would be an example of a text-related AI task. What about generative AI language tasks? Nick: These are generative, which means the output text is generated by the model. Some examples are creating text, like stories or poems, summarizing texts, and answering questions, and so on. 07:25 Nikita: What about speech and vision? Nick: Speech-related AI tasks can be audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech to text conversion, speaker recognition, or voice conversion, and so on. Generative AI tasks are generative, i.e., the output audio is generated by the model (for example, music composition or speech synthesis). Vision-related AI tasks can be image related or generative AI. Image-related AI tasks use an image as the input, and the output depends on the task. Some examples are classifying images or identifying objects in an image. Facial recognition is one of the most popular image-related tasks that's often used for surveillance and tracking people in real time. It's used in a lot of different fields, like security and biometrics, law enforcement, entertainment, and social media. For generative AI tasks, the output image is generated by the model. For example, creating an image from a textual description or generating images of specific style or high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of objects, such as machine, buildings, medications, people and landscapes, and so much more. 08:58 Lois: This is so fascinating. So, now we know what AI is capable of. But Nick, what is AI good at? Nick: AI frees you to focus on creativity and more challenging parts of your work. Now, AI isn't magic. It's just very good at certain tasks. It handles work that's repetitive, time consuming, or too complex for humans, like processing data or spotting patterns in large data sets.  AI can take over routine tasks that are essential but monotonous. Examples include entering data into spreadsheets, processing invoices, or even scheduling meetings, freeing up time for more meaningful work. AI can support professionals by extending their abilities. Now, this includes tools like AI-assisted coding for developers, real-time language translation for travelers or global teams, and advanced image analysis to help doctors interpret medical scans much more accurately. 10:00 Nikita: And what would you say is AI's sweet spot? Nick: That would be tasks that are both doable and valuable. A few examples of tasks that are feasible technically and have business value are things like predicting equipment failure. This saves downtime and the loss of business. Call center automation, like the routing of calls to the right person. This saves time and improves customer satisfaction. Document summarization and review. This helps save time for busy professionals. Or inspecting power lines. Now, this task is dangerous. By automating it, it protects human life and saves time. 10:48 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:30 Nikita: Welcome back! Now one big way AI is helping businesses today is by cutting costs, right? Can you give us some examples of this?  Nick: Now, AI can contribute to cost reduction in several key areas. For instance, chatbots are capable of managing up to 50% of customer queries. This significantly reduces the need for manual support, thereby lowering operational costs. AI can streamline workflows, for example, reducing invoice processing time from 10 days to just 1 hour. This leads to substantial savings in both time and resources. In addition to cost savings, AI can also support revenue growth. One way is enabling personalization and upselling. Platforms like Netflix use AI-driven recommendation systems to influence user choices. This not only enhances the user experience, but it also increases the engagement and the subscription revenue. Or unlocking new revenue streams. AI technologies, such as generative video tools and virtual influencers, are creating entirely new avenues for advertising and branded content, expanding business opportunities in emerging markets. 12:50 Lois: Wow, saving money and boosting bottom lines. That's a real win! But Nick, how is AI able to do this?  Nick: Now, data is what teaches AI. Just like we learn from experience, so does AI. It learns from good examples, bad examples, and sometimes even the absence of examples. The quality and variety of data shape how smart, accurate, and useful AI becomes. Imagine teaching a kid to recognize animals using only pictures of squirrels that are labeled dogs. That would be very confusing at the dog park. AI works the exact same way, where bad data leads to bad decisions. With the right data, AI can be powerful and accurate. But with poor or biased data, it can become unreliable and even misleading.  AI amplifies whatever you feed it. So, give it gourmet data, not data junk food. AI is like a chef. It needs the right ingredients. It needs numbers for predictions, like will this product sell? It needs images for cool tricks like detecting tumors, and text for chatting, or generating excuses for why you'd be late. Variety keeps AI from being a one-trick pony. Examples of the types of data are numbers, or machine learning, for predicting things like the weather. Text or generative AI, where chatbots are used for writing emails or bad poetry. Images, or deep learning, can be used for identifying defective parts in an assembly line, or an audio data type to transcribe a dictation from a doctor to a text. 14:35 Lois: With so much data available, things can get pretty confusing, which is why we have the concept of labeled and unlabeled data. Can you help us understand what that is? Nick: Labeled data are like flashcards, where everything has an answer. Spam filters learned from emails that are already marked as junk, and X-rays are marked either normal or pneumonia. Let's say we're training AI to tell cats from dogs, and we show it a hundred labeled pictures. Cat, dog, cat, dog, etc. Over time, it learns, hmm fluffy and pointy ears? That's probably a cat. And then we test it with new pictures to verify. Unlabeled data is like a mystery box, where AI has to figure it out itself. Social media posts, or product reviews, have no labels. So, AI clusters them by similarity. AI finding trends in unlabeled data is like a kid sorting through LEGOs without instructions. No one tells them which blocks will go together.  15:36 Nikita: With all the data that's being used to train AI, I'm sure there are issues that can crop up too. What are some common problems, Nick? Nick: AI's performance depends heavily on the quality of its data. Poor or biased data leads to unreliable and unfair outcomes. Dirty data includes errors like typos, missing values, or duplicates. For example, an age record as 250, or NA, can confuse the AI. And a variety of data cleaning techniques are available, like missing data can be filled in, or duplicates can be removed. AI can inherit human prejudices if the data is unbalanced. For example, a hiring AI may favor one gender if the past three hires were mostly male. Ensuring diverse and representative data helps promote fairness. Good data is required to train better AI. Data could be messy, and needs to be processed before to train AI. 16:39 Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the AI for You course. As you complete the course, you'll find skill checks that you can attempt to solidify your learning.  Lois: In our next episode, we'll dive deep into fundamental AI concepts and terminologies. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 17:05 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Building on last week's DigiMarCon 2025 recap, Leslie Richards and Isabelle Horan unpack the evolution from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO) and what that means for law firms today.

Industry leaders from Celonis and AWS explain why 2025 marks the inflection point for agentic AI and how early adopters are gaining significant competitive advantages in efficiency and innovation.Topics Include:AWS's Cristen Hughes and Celonis's Jeff Naughton discuss AI agent transformationAndy Jassy declares AI agents will fundamentally change how we workThree key trends make AI agents practical: smarter models, longer tasks, cheaper costsAI now beats humans on complex benchmarks for the first time everClaude 3.7 cracked graduate-level reasoning where humans previously dominated completelyAI evolved from brief interactions to managing sustained multi-step complex workflowsProcessing costs plummeted 99.7% making enterprise-grade AI economically viable at scaleWe're transitioning from 2023's adaptation era to 2025's human-AI collaboration eraBy 2028, AI will suggest actions to humans rather than vice versaAgents are autonomous software that plan, act, and reason independently with minimal interventionAgent workflow: receive human request, create plan, execute actions, review, adjust, deliverFour agent components: brain (LLM), memory (context), actions (tools), persona (role definition)AWS offers three building approaches: ready-made solutions, managed platform, DIY developmentKey enterprise applications: software development acceleration, customer care automation, knowledge work optimizationManual processes like accounts payable offer huge transformation opportunities through intelligent automationDeep process analysis is critical before deploying agents for maximum effectivenessCelonis pioneered process mining to help enterprises understand their actual workflow realitiesCompanies are collections of interacting processes that agents need proper context to navigateProcess intelligence provides agents with placement guidance, data feeds, monitoring, and workflow directionCelonis-AWS partnership demonstrates order management agents that automatically handle at-risk situationsParticipants:Jeff Naughton – SVP and Fellow, CelonisCristen Hughes – Solutions Architecture Leader, ISV, North America, Amazon Web ServicesFurther Links:Celonis WebsiteCelonis on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Justin DiPietro, Co-Founder & Chief Strategy Officer of Glia, shares how they are leveraging AI to enhance the customer experience in the highly regulated world of financial institutions.Topics Include:Glia provides voice, digital, and AI services for customer-facing and internal operationsBuilt on "channel-less architecture" unlike traditional contact centers that added channels sequentiallyOne interaction can move seamlessly between channels (voice, chat, SMS, social)AI applies across all channels simultaneously rather than per individual channel700 customers, primarily banks and credit unions, 370 employees, headquartered in New YorkTargets 3,500 banks and credit unions across the United States marketFocuses exclusively on financial services and other regulated industriesAI for regulated industries requires different approach than non-regulated businessesTraditional contact centers had trade-off between cost and quality of serviceAI enables higher quality while simultaneously decreasing costs for contact centersNumber one reason people call banks: "What's my balance?" (20% of calls)Financial services require 100% accuracy, not 99.999% due to trust requirementsUses AWS exclusively for security, reliability, and future-oriented technology accessReal-time system requires triple-hot redundancy; seconds matter for live callsWorks with Bedrock team; customers certify Bedrock rather than individual featuresShowed examples of competitors' AI giving illegal million-dollar loans at 0%"Responsible AI" separates probabilistic understanding from deterministic responses to customersUses three model types: client models, network models, and protective modelsTraditional NLP had 50% accuracy; their LLM approach achieves 100% understandingPolicy is "use Nova unless" they can't, primarily for speed benefitsParticipants:Justin DiPietro – Co-Founder & Chief Strategy Officer, GliaFurther Links:Glia WebsiteGlia AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ed Bailey, Field CISO at Cribl, shares how Cribl and AWS are helping customers rethink their data strategy by making it easier to modernize, reduce complexity, and unlock long-term flexibility.Topics Include:Ed Bailey introduces topic: bridging gap between security data requirements and budgetCompanies face mismatch: 10TB data needs vs 5TB licensing budget constraintsData volumes growing exponentially while budgets remain relatively flat year-over-yearIT security data differs from BI: enormous volume, variety, complexityMany companies discover 600+ data sources during SIEM migration projects50% of SIEM data remains un-accessed within 90 days of ingestionComplex data collection architectures break frequently and require excessive maintenanceTeams spend 80% time collecting data, only 20% analyzing for valueData collection and storage are costs; analytics and insights provide business valuePoor data quality creates operational chaos requiring dozens of browser tabsSOC analysts struggle with context switching across multiple disconnected systemsTraditional vendor approach: "give us all data, we'll solve problems" is outdatedData modernization requires sharing information widely across organizational business unitsData maturity model progression: patchwork → efficiency → optimization → innovationData tiering strategy: route expensive SIEM data vs cheaper data lake storageSIEM costs ~$1/GB while data lakes cost ~$0.15-0.20/GB for storageCompliance retention data should go to object storage at penny fractionsDecouple data retention from vendor tools to enable migration flexibilityCribl platform offers integrated solutions: Stream, Search, Lake, Edge componentsCustomer success: Siemens reduced 5TB to 500GB while maintaining security effectivenessParticipants:Edward Bailey – Field CISO, CriblFurther Links:Cribl WebsiteCribl on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

The Company designs, develops, and operatesdigital infrastructure that transforms surplus renewable energy into globalcomputing resources. Soluna's pioneering data centers are strategicallyco-located with wind, solar, or hydroelectric power plants to supporthigh-performance computing applications, including Bitcoin Mining, GenerativeAI, and other compute-intensive applications. Soluna's proprietary softwareMaestroOS(™) helps energize a greener grid while delivering cost-effective andsustainable computing solutions and superior returns. A leading developer of green data centers that convert excess renewableenergy into global computing resources. Soluna builds modular, scalable datacenters for computing intensive, batchable applications such as Bitcoin mining,AI, and machine learning. Soluna provides a cost-effective alternative tobattery storage or transmission lines. Up to 30% of the power of renewableenergy projects can go to waste. Soluna's data centers enable clean electricityasset owners to ‘Sell. Every. Megawatt.'

Leslie Richards and Isabelle Horan share key takeaways from the 2025 DigiMarCon Conference, including how law firms can move beyond basic content generation and instead use generative AI tools for advanced strategic applications.

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf's 76,000+ customers scale their device management operations.Topics Include:Sam Johnson introduces himself as Chief Customer Officer from Jamf companyJamf's 23-year mission: help organizations succeed with Apple device managementCompany manages 33+ million devices for 76,000+ customers worldwide from MinneapolisJamf has used AI since 2018 for security threat detectionReleased first customer-facing generative AI Assistant just last year in 2024Presentation covers why, how they built it, use cases, and future plansJamf serves horizontal market from small business to Fortune 500 companiesChallenge: balance powerful platform capabilities with ease of use and adoptionAI could help get best of both worlds - power and simplicityAI also increases security posture and scales user capabilities significantlyCustomers already using ChatGPT/Claude but wanted AI embedded in productBuilt into product to reduce "doorway effect" of switching digital environmentsCreated small cross-functional team to survey land and build initial trailRest of engineering organization came behind to build the production highwayTeam needed governance layer with input from security, legal, other departmentsEvaluated multiple providers but ultimately chose Amazon Bedrock for three reasonsAWS team support, large community, and integration with existing infrastructureUses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementationAI development required longer training/validation phase than typical product featuresReleased "AI Assistant" with three skills: Reference, Explain, and Search capabilitiesParticipants:Sam Johnson – Chief Customer Officer, JamfFurther Links:Jamf.comJamf on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Mark Stevens, SVP, Channels and Alliances, discusses how SecurityScorecard's strategic partnership with AWS enables them to scale their security solutions through cloud infrastructure, marketplace integration, and co-sell programsTopics Include:SecurityScorecard founded 10 years ago to understand third-party vendor security postureCompany has grown to 3,000 enterprise customers and 200+ partners globallyEvolved from ratings to "supply chain detection and response" over last yearSupply chain threats have doubled, creating extended attack surfaces for companiesMany organizations don't know their vendor count or vulnerabilities within supply chainsSecurityScorecard provides visibility into attack surfaces and management tools for controlGenerative AI is central to their ecosystem, leveraging AWS Bedrock extensivelyThey scan the entire internet every two days at massive scaleHave scored 12 million companies with security scorecards to dateAll workloads run on AWS cloud infrastructure as their primary platformAWS partnership provides necessary scale for managing hundreds of thousands of vendorsCase study: Identified vendor misconfigurations that could shut down 1,000 locationsOwn massive 10-year data lake with tens of millions of companiesNew managed service combines AI automation with human analysts for supportLarge organizations cannot fully automate supply chain security management yetQuality threat intelligence data now valuable to SOC teams, not just riskThird-party risk management and SOC teams are slowly converging for better securityAWS marketplace integration provides frictionless customer experience and larger dealsCo-sell programs with AWS enterprise sales teams create effective flywheel motionFuture expansion includes identity management, response actions, and internal signal managementParticipants:Mark Stevens – SVP, Channels and Alliances, SecurityScorecardFurther Links:SecurityScorecard.ioSecurityScorecard AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AI just took a major step forward, and lawyers need to be paying attention. In this episode, I break down the launch of new AI browsers from ChatGPT and Perplexity. These tools are designed to act on your behalf, reading files, scanning emails, analyzing data, and even taking action while running quietly in the background. If that sounds efficient, it also raises serious red flags for anyone who handles sensitive or confidential information. We'll cover: • What agentic AI browsers really do • Why this technology goes beyond typical search • What OpenAI's Sam Altman is saying about the risks • How ABA Model Rule 1.6 applies here • What actions firms should take right now to protect confidentiality This is not about avoiding new technology. It is about using it wisely and protecting your clients in the process.

Our U.S. Media & Entertainment Analyst Benjamin Swinburne discusses how GenAI is transforming content creation, distribution and also raising some serious ethical questions. Read more insights from Morgan Stanley.----- Transcript -----Welcome to Thoughts on the Market. I'm Ben Swinburne, Morgan Stanley's U.S. Media and Entertainment Analyst. Today – GenAI is poised to shake up the entertainment business. It's Wednesday, July 23, at 10am in New York.It's never been easier to create art for anyone – with a little help from GenerativeAI. You can transform photos of yourself or loved ones in the style of a popular Japanese movie studio or any era of visual art to your liking. You can create a short movie by simply typing in a few prompts. Even I can speak to youin several different languages. I can ask about the weather:Hvordan er været i dag?Wie ist das wetter heute?आज मौसम कैसा है? In the media and entertainment industry, GenAI is expected to bring about a seismic shift in how content is made and consumed. A recent production used AI to de-age actors and recreate the likeness of a deceased performer—cutting what used to take hundreds of VFX artists a year to just a few months with a small team. There are many other examples of how GenAI is revolutionizing how stories are told, from scriptwriting and editing to visual effects and dubbing. In music, GenAI is helping music labels identify emerging talent and generate new compositions. GenAI can even create songs using the voices of long-gone artists – potentially extending revenue far beyond an artist's lifetime. GenAI-driven tools have the potential to reduce TV and film production costs by 10–30 percent, with animation and post-production among the biggest savings opportunities. GenAI could also transform how content reaches audiences. Recommendation engines can become even more predictive, using behavioral data to serve up exactly what listeners want—sometimes before we know what we want. And there's more studios can achieve in post production. GenAI can already dub content in multiple languages, even syncing mouth movements to match the new dialogue. This makes global distribution faster, cheaper, and more culturally relevant. With better engagement comes better monetization. Platforms will use GenAI to introduce new pricing tiers, targeted advertising, and personalized superfan content that taps into niche audiences willing to pay more. But all this innovation brings up profound ethical concerns. First, there's the issue of consent and copyright. Can GenAI tools legally use an actor's name, likeness or voice? Then there's the question of authorship. If an AI writes a script or composes a song, who owns the rights? The creator or the GenAI model? Labor unions are understandably worried. In 2023, AI was a major sticking point in negotiations between Hollywood studios and writers' and actors' guilds. The fear? That AI could replace human jobs or devalue creative work. There are also legal battles. Multiple lawsuits are underway over whether AI models trained on copyrighted material without permission violate intellectual property laws. The outcomes of these cases could reshape the entire industry. But here's a big question no one can ignore: Will audiences care if content is AI-generated? Some consumers are fascinated by AI-created music or visuals, while others crave the emotional depth and authenticity that comes from human storytelling. Made-by-humans could become a premium label in itself. Now, despite GenAI's rapid rise, not every corner of entertainment is vulnerable. Live sports, concerts, and theater remain largely insulated from AI disruption. These experiences thrive on real-time emotion, unpredictability, and human connection—things AI can't replicate. In an AI-saturated world, the value of live events and sports rights will rise, favoring owners of sports rights and live platforms. So where do we go from here? By and large, we're entering an era where storytelling is no longer limited by budget or geography. GenAI is lowering the barriers to entry, expanding the creative class, and reshaping the economics of media. The winners in this new landscape will likely be companies that can scale—platforms with massive user bases, deep data pools, and the engineering talent to integrate GenAI seamlessly. But there's also room for agile newcomers who can innovate faster than the incumbents and disrupt the disrupters. No doubt, as the tools get better, the questions get harder. And that's where the real story begins. Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Chief Product Development Officer Mitchell Johnson discusses how Sonatype protects enterprise developers from malicious open source components while keeping them productive through AI.Topics Include:Sonatype provides software supply chain solutions for enterprises using open source componentsThey serve large enterprises, government agencies, and critical infrastructure providers globallyMain challenge: keeping developers productive while maintaining secure software supply chainsCybercrime and supply chain attacks are massive, growing industries threatening developersAI adoption is happening faster than expected, profoundly changing development workflowsBad actors evolved from waiting for vulnerabilities to creating malicious componentsMalicious open source components specifically target developer and DevOps toolchainsSonatype's security research team uses AI/ML to analyze every open source componentThey can predict and block malicious components before entering customer environmentsAWS partnership helps Sonatype meet customers where they want to do businessPartnership focuses on go-to-market alignment, not just technical integrationAWS sales teams should be treated as extensions of your own sales organizationUnderstanding AWS sales structure and incentives is crucial for successful partnershipsAI development is following same pattern as open source adoption twenty years ago"Shadow AI" parallels the earlier "shadow IT" trend with open source softwareAI speeds up code generation but security review processes haven't kept paceDevelopers need a "Hippocratic Oath" - taking responsibility for AI-generated code outputWithin 24 months, professionals not skilled in AI will struggle to stay relevantSonatype's culture encourages curiosity, experimentation, and accepts failure as part of innovationTheir core mission: help developers focus on innovation, not security choresParticipants:Mitchell Johnson – Chief Product Development Officer, SonatypeFurther Links:Sonatype WebsiteSonatype on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Founder and CTO Alex Rice discusses how HackerOne uses generative AI to automate security workflows and prioritizing accuracy over efficiency to achieve end-to-end outcomes.Topics Include:HackerOne uses ethical hackers and AI to find vulnerabilities before criminalsWhite hat hackers stress test systems to identify security weaknesses proactivelyGenerative AI plays a huge role in HackerOne's security operationsSecurity teams struggle with constant toil of finding and fixing vulnerabilitiesAI helps minimize toil through natural language interfaces and automationBoth good and bad actors have access to generative AI toolsSuccess requires measuring individual task inputs and outputs, not just aggregatesBreaking down workflows into granular tasks reveals measurable AI improvementsHackerOne deployed "Hive," their AI security agent to reduce customer toilInitial focus was on tasks where AI clearly outperformed humansStarted with low-hanging fruit before tackling more complex strategic workflowsAccuracy is the primary success metric, not just efficiency or speedSecurity requires precision; wrong fixes create bigger problems than inefficiencyCustomer acceptance and reduced time to remediation are north star metricsHumans remain the source of truth for validation and feedback loopsBreak down human jobs into granular AI tasks using systems thinkingBuild specific agents for individual tasks rather than entire job rolesKeep humans accountable for end-to-end outcomes to maintain customer trustAWS Bedrock chosen for security, confidentiality, and data separation requirementsMoving from efficiency improvements to entirely new AI-enabled capabilitiesParticipants:Alex Rice – Founder & CTO/CISO, HackerOneFurther Links:HackerOne WebsiteHackerOne on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

What if the real risk of AI isn't job loss but brain atrophy?Episode SummaryIf you've spent any time on social media recently, you'll be familiar with the flood of low-quality AI-generated sludge. And on this episode, I'm speaking to someone who is leading a one-woman campaign against it and in favour of human-generated content. Her name is Zsike Peter and she's the fiercely human founder of an agency called Vampire Digital; you'll hear why its called that on the show.  Zsike is also the author of a new book called Thinkbait: The Definitive Guide to Reclaiming Human Creativity in the Age of AI which seeks to highlight and combat the prevalence of AI-generated low-quality content.Her mission is a passionate defence of human creativity in an age where generative AI threatens to dull our minds and voices. Its a rallying cry for intentional, thoughtful use that keeps our agency intact. In a fascinating discussion, we explore her extraordinary backstory, from growing up in communist Transylvania to being arrested after escaping a toxic UK host family that hired her as an au pair. And you'll hear the remarkable story about how she went undercover in a brothel to win a journalism scholarship. These stories aren't just great anecdotes, they reveal a mindset of relentless curiosity, courage, and independence that informs her work today.What makes Zsike's objection to AI so compelling is that initially she embraced it.  But after having tried it out, she flipped from embracing generative AI to warning against its effects on our thinking. You'll hear her talk about the concept of Thinkbait as an alternative to clickbait; content that stimulates rather than stupefies. Along the way, we unpack how language creates culture, why writing is thinking, and what it means to preserve our humanness in a machine-saturated world.And much, much more.Guest Biography: Zsike Peter Zsike is the founder of Vampire Digital — a content agency with a “fiercely human heart,” known for producing sharp, human-written copy in a world drowning in AI sludge. She is also the author of Thinkbait: The Definitive Guide to Reclaiming Human Creativity in the Age of AI — a book that serves as both a practical framework and a philosophical manifesto. Her background in journalism, corporate communications, and digital marketing now powers a mission to help people reclaim their voices (and their thinking) in a world increasingly seduced by generative AI.LinksThinkbait  - https://thinkbait.co.uk/Vampire Digital, Zsike's agency - https://www.vampiredigital.biz/Zsike on LinkedIn - https://www.linkedin.com/in/zsike-peter/AI-Generated Timestamped Summary(yes, I know, ironic, given the subject!)[00:00:00] Introduction [00:02:00] Zsike's childhood in communist Transylvania and family escape story[00:13:00] Going undercover in a brothel to win a journalism competition[00:19:00] Her arrest and start in the UK after fleeing abuse[00:24:00] Building a career in communications and founding Vampire Digital[00:28:00] Why she chose the vampire brand and what it represents[00:31:00] How her agency captures authentic voice in client content[00:33:00] Her shift from embracing to warning against generative AI[00:36:00] The dangers of outsourcing thinking and writing to machines[00:41:00] Why individuality and voice matter in a world of sameness[00:44:00] Thinkbait as a framework, manifesto, and act of defiance[00:48:00] The bedtime story moment that triggered a rethink on AI[00:53:00] The rise of fake authority and automated engagement online[00:57:00] Language loss, writing in a third language, and cultural identity[01:03:00] How hardship shaped her creative drive and ethical stance[01:07:00] Final reflections

Spencer Herrick, Principal AI Product Manager of Asana and Oliver Myers of AWS demonstrate how their integration allows Asana's AI workflows to access enterprise data from Amazon Q Business, enabling seamless cross-application automation and insights.Topics Include:Oliver Myers leads Amazon Q Business go-to-market, Spencer Herrick manages Asana AI products.Session focuses on end user productivity challenges with generative AI technology implementations.End users face technology overload with doubled workplace application usage over five years.Data silos prevent getting maximum value from generative AI across fragmented enterprise systems.Workers spend 53% of time on "work about work" instead of strategic contributions.Ideal experience needs single pane of glass with cross-application insights and actions.Amazon Q Business launched as managed service with 40+ enterprise data connectors.Connectors maintain end-user permissions from source systems for enterprise security compliance.QIndex feature enables ISVs to access Q Business data via API calls.End users get answers enriched with multiple data sources without switching applications.Asana's work graph connects all tasks, projects, and portfolios to company goals.Phase 1 AI focused on narrow solutions like smart status updates.Phase 2 aimed for AI teammate capabilities requiring extensive contextual knowledge.AI Studio launched as no-code workflow automation builder within Asana platform.Q integration allows AI Studio to access cross-application context beyond Asana boundaries.SmartChat enhanced with Q can answer "what should I work on today?" holistically.Users returning from PTO can quickly understand goal risks across data sources.AI Studio workflows automate feature request processing across Asana, Drive, Slack, email.Partnership eliminates silos while maintaining enterprise security and permission controls.Integration creates connected ecosystem enabling true cross-application AI automation and insights.Participants:Spencer Herrick - Principal AI Product Manager, AsanaOliver Myers - Worldwide Head of Business Development, Amazon Web ServicesFurther Links:Asana.comAsana on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Product Officer Dan Brown explains how Celonis creates digital twins of business processes to power AI agents that automate operational improvements.Topics Include:Dan Brown introduces Celonis as the thought leader in process mining for over a decade.Celonis serves largest global companies across all industries seeking operational improvements.Companies have process diagrams but actual operations differ significantly from documentation.Celonis creates digital twins of business processes by analyzing system data flows.Process intelligence reveals how work actually happens versus how companies think it happens.Platform enables process normalization, improvement assessment, and automated corrective actions.Celonis vision: making processes work better for people, companies, and the planet.Process intelligence provides visibility into current operations and improvement strategies.Great AI requires great data, but most companies only have static views.Process intelligence graph shows real-time flow of orders, invoices, and opportunities.Agentic AI requires four capabilities: sensing, planning, executing, and governing operations.Process intelligence enables real-time detection of conformance problems and deviations.AWS partnership leverages Bedrock for agentic AI and infrastructure for data processing.Data ingestion, organization, and enrichment are core to process intelligence value.AI agents now handle process deviations with increasing autonomy and sophistication.Heavy equipment manufacturer uses agents to coordinate with third-party vendors automatically.Agents text and email vendors to confirm delivery dates, reducing manual work.Implementation challenges include data quality, conservative adoption, and governance concerns.Companies should start with achievable use cases and expand gradually across domains.Future involves enterprise-wide process visibility powering automated applications and continuous improvement.Participants:Dan Brown – Chief Product Officer, CelonisFurther Links:Celonis WebsiteCelonis on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS partners Automation Anywhere, Qlik, and Vectra.ai discuss revolutionizing customer experience through generative AI, sharing real-world implementations in automation, analytics, and cybersecurity applications. Topics Include:AWS Technology Partnerships panel on agentic AI implementationThree AWS partners share real-world AI deployment experiencesAutomation Anywhere automates end-to-end business processes with agentsVectra.ai uses autonomous agents for cybersecurity threat detectionQlik applies generative AI across their data platform portfolioCustomer service automation handles L1 support requests efficientlyUtility company processes 144,000 complaints annually using agentsRegulatory compliance improved through faster complaint resolution workflowsCybersecurity agents reduce threat detection time by 50-60%Triage, correlation, and prioritization handled by autonomous agentsSignal fatigue reduced through intelligent alert filtering systemsNatural language queries enable faster business decision makingSales AI agent provides competitive information during callsAWS Marketplace reduced 7,000 weekly tickets to zero2023 was proof-of-concept year, 2024 focuses production deploymentAWS Bedrock integration seamless with existing data repositoriesModel optionality crucial for different use case requirementsAgility most important capability in generative AI journeyCode abandonment becomes acceptable due to rapid innovationMaximum team size of 10 people maintains development agilityTargeted solutions outperform broad platform capabilities in adoptionImplementation expertise becomes bottleneck for customer scaling effortsNatural language interaction patterns completely shifted user behaviorKeywords searches replaced by conversational query approachesResponsible AI committees review decisions and establish principlesSecurity considerations balance speed with responsible deployment practicesBad actors adopt generative AI faster than defendersExplainability requirements slow feature rollout but ensure auditabilityMulti-modal deployments use different models for specific casesFuture trends include AI-powered business process outsourcingParticipants:Peter White – SVP, Emerging Products, Automation AnywhereRyan Welsh – Field CTO - Generative AI, QlikJohn Skinner – Vice President Corporate/Business Development, Vectra.aiChris Grusz – Managing Director for Technology Partnerships, AWSFurther Links:Automation Anywhere in AWS MarketplaceQlik in AWS MarketplaceVectra.ai in AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Zak Krider, Trellix's Director of Strategy and AI, shares how Trellix has successfully integrated generative AI into their security operations and democratized access to AI models across the organization.Topics Include:Trellix formed from McAfee Enterprise and FireEye mergerProvides full security stack visibility in single platformServes SMBs to Fortune 500 and government customersUsed machine learning for two decades with 30 modelsRecently pivoted to generative AI with Wwise platformAI finds critical events among thousands daily alertsIncorporates threat hunting knowledge into AI prompt structuresAWS Bedrock central to AI strategy for model flexibilityFormed small tiger team to investigate generative AIAnthropic Claude provided breakthrough "aha moments" for capabilitiesAdopted "fail fast, learn fast" innovation culture approachEnabled employee access to models through Bedrock APIConducted innovation jam sessions with VC-style pitchesAI decoded Base64 without prompting, identified benign activityJunior analysts elevated to level two with AICommon misconception: models train on customer data falselyEarly challenge: providing too much data overwhelmed modelsSmaller models hallucinated more with plausible-sounding responsesDesign partner programs help prioritize product developmentDemocratize AI access beyond just technical teamsTest multiple models for specific use casesLarge models work better than small ones initiallyPrompt engineering crucial for effective model communicationModel Context Protocol will gain traction next yearBackend data security remains largely unsolved challengeFederal customers require on-premises, air-gapped AI solutionsParticipants:Zak Krider – Director of AI and Innovation, TrellixFurther Links:Website: https://www.trellix.comTrellix on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

NVIDIA's Global Head of Partnerships & Cloud for Startups, Jen Hoskins, details their collaboration with AWS to support over 25,000 startups through their Inception program.Topics Include:AI transformation happening across all industries and verticalsNVIDIA evolved from GPU company to full-stack AI solutionsAccelerated computing requires complete stack re-engineering from chip upTraditional CPU scaling has reached its fundamental performance limitsNVIDIA-AWS partnership spans over 13 years of co-developmentDGX Cloud integrates seamlessly with AWS SageMaker and BedrockOver 26 NVIDIA solutions available in AWS MarketplaceNVIDIA AI Enterprise accelerates data science and deployment pipelinesNIM microservices streamline AI model development like Docker containersCodeway gaming startup saved 48% on compute costs using NVIDIAEternal improved marketing ROI by 30X with generative AIQuoto achieved 10X content length and 3X throughput improvementNOATech biotech scaled cancer research with small team efficientlyNVIDIA Inception program supports over 25,000 startups globallyProgram covers 100+ countries across all verticals and stagesStartups get AWS credits up to $100,000 through ActivateDeveloper program offers free access to hundreds of SDKsThree program pillars: Innovate, Build, and Grow stagesVC Alliance connects startups with over 1,000 investorsVenture Capital Connect directly links startups to funding opportunitiesParticipants:Jen Hoskins – Startups, Global Head of Cloud, Partnerships & Go to Market, NVIDIAFurther Links:Website: https://www.nvidia.comNVIDIA Inception ProgramNVIDIA on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Matt “Kix” Kixmoeller, Chief Marketing Officer of Glean, shares how Glean partners with AWS to deploy secure, scalable AI solutions that help companies move from basic productivity tools to transformative business intelligence.Topics Include:Introduction to GleanGlean targets Global 2000 companies for AI transformationEnterprise AI needs company context: data, people, processesBottom-up approach: deploy to all employees firstFocus on business results, not just productivity gainsGlean Assistant provides daily AI tool for employeesGlean Agents platform enables natural language agent buildingOpen APIs export context to enterprise systemsStarted as enterprise search, evolved to knowledge graphsKnowledge graphs map content, people, projects, and processesIndividual knowledge graphs created for each personGlean WorkAI platform includes search, protect, agentsGlean Protect ensures data security and AI governancePlatform integrates with existing enterprise tools nativelyMCP enables connection to various AI systemsStrong growth: $100M ARR, $700M+ funding raisedAWS partnership provides models, security, and deploymentParticipants:Matt “Kix” Kixmoeller – Chief Marketing Officer, GleanFurther Links:Website: https://www.glean.com/Glean on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

What is ambient AI, and why does it matter to lawyers, law firms, and legal marketing and business development professionals? In this episode of Legal Marketing Minutes, I define ambient AI in clear terms and explain how it's quietly showing up in tools professionals already use. You'll hear a real-world example from another profession that illustrates the potential for this technology, and I'll walk you through what this shift means for your work in legal. We'll also discuss OpenAI's collaboration with renowned iPhone designer Jony Ive, what he and Sam Altman are building behind the scenes, and how it could impact the way we interact with AI in the future. Topics covered include what ambient AI is, where it's showing up in the legal profession, why it matters, and what ethical questions and compliance considerations law firms need to keep in mind. Ambient AI isn't just coming. It's already here. The question is whether your firm is ready. If you are in a place where you can leave a comment, please do so, as I would love to hear from you. If not, feel free to email me at nancy@myrlandmarketing.com. Also, my website, where you can find all of my contact information, and my other podcast, Legal Marketing Moments, can be found at https://myrlandmarketing.com/podcasts/legalmarketingminutes.com Thanks for spending a few of your Legal Marketing Minutes with me! If I can help you in your AI discernment and strategy, please let me know. 

Kui Jia, Sumo Logic's Vice President of Engineering and Head of AI, shares how their AWS-powered AI agents transform chaotic security investigations into streamlined workflows.Topics Include:Kui Jia leads AI Engineering at Sumo LogicSREs and SOC analysts work under chaotic, high-pressure conditionsTeams constantly switch between different vendor tools and platformsInvestigation requires quick hypothesis formation and complex query writingSumo Logic processes petabytes of data daily across enterprisesCompany serves 2,000+ enterprise customers for 15 yearsPlatform focuses on observability and cybersecurity use casesInvestigation journey: discover, diagnose, decide, act, learn phasesData flows from ingestion through analytics to human insightsTraditional workflow relies heavily on tribal domain knowledgeSenior engineers create queries that juniors struggle to understandWar room situations demand immediate answers, not learning curvesContext switching between tools wastes time and creates frictionMultiple AI generations deployed: ML anomaly detection to GenAIAgentic AI enables reasoning, planning, tools, and evaluation capabilitiesMo Copilot launched at AWS re:Invent as AI agent suiteNatural language converts high-level questions into Sumo queriesSystem provides intelligent autocomplete and multi-turn conversationsInsight agents summarize logs and security signals automaticallyKnowledge integration combines foundation models with proprietary metadataAI generates playbooks and remediation scripts for automated actionsThree-tier architecture: Infrastructure, AI Tooling, and Application layersBuilt on AWS Bedrock with Nova models for performanceFocus on reusable infrastructure and AI tooling componentsData differentiation more important than AI model selectionGolden datasets and contextualized metadata are development challengesGuardrails and evaluation frameworks critical for enterprise deploymentAI observability enables debugging and performance monitoringEnterprise agents achievable within one year development timelineFuture vision: multiple AI agents collaborating with human investigatorsParticipants:Kui Jia – Vice President of AI Engineering, Head of AI, Sumo LogicFurther Links:Website: https://www.sumologic.com/Sumo Logic in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Wilson Patton, Solutions Architect for Trellix, demonstrates how their four-pillar Gen-AI framework transforms incident alerts into actionable intelligence.Topics Include:Wilson Patton: Trellix Solutions Architect, 20 years government experienceWitnessed evolution from basic firewalls to zero trust architecturesTrellix combines McAfee and FireEye heritage and capabilitiesAI integration isn't new - machine learning embedded for yearsPartnership with AWS Bedrock accelerates Gen-AI development capabilities2014: Developed Impossible Travel Analytic for anomaly detection2016: Launched Guided Investigations framework for SOC analysts2023: Introduced AI Guided Investigations with contextual understanding64% of public sector exploring AI adoption activelyOnly 21% have requisite data ready for trainingGen-AI won't magically clean up messy, siloed data74% of executives doubt AI information accuracy currentlyMonday morning alert queue: 76 high, 318 medium alertsAdversaries steal credentials 90 days before major incidentsCritical breadcrumbs hidden in low-priority informational alerts1000+ data-driven investigative questions developed over eight yearsSkilled analysts take too long reading all answersAutomate analysis, distill thousands down to ten critical alertsFour foundational pillars for effective, trustworthy Gen-AI implementationCybersecurity expertise essential - Gen-AI is just a toolFrameworks ensure reliability and consistent prompting for productionMultiple LLM models tested through AWS Bedrock platformQuality diverse datasets required for accurate question answeringGood prompts combine evidence, context, and comprehensive informationTesting shows order of magnitude price differences between modelsNova Micro provides cost-effective results for many scenariosPrompt engineering superior to fine-tuning for avoiding biasAgentic AI performs multi-step investigations with live dataStrategic model choice based on specific requirements and costsTransparent audit trails mandatory for government compliance requirements Participants:Wilson Patton – Solutions Architect, TrellixFurther Links:Website: https://www.trellix.comTrellix in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

GenAI is transforming testing with automation, flexibility, reliability, and speed. But anti-bias and resiliency standards remain essential.

Jeff Chow, Chief Product and Technology Officer at Miro, explores how harnessing AI — in addition to reshaping teams and workflows — accelerates the product development lifecycle. He also shares insight into how Miro is embracing new technology and ways of working to transform its Innovation Workspace.Topics Include:Platform & PartnershipMiro serves 250,000+ customers with 90+ million knowledge workers using their Innovation WorkspacePlatform supports discovery, definition, and delivery phases of innovation processReal-time multiplayer canvas enables team co-creation across multiple formats, including seamless transitions between structured and unstructured work.Three-tier AWS partnership: infrastructure backbone, AI services (Bedrock/Q), and joint customer solutionsInnovation Challenges & FrictionProduct development lifecycle bottlenecks: separate tools per function create process delays and collaborative frictionPain points include stalled product kickoffs, lengthy design ideation cycles, and process delays from engineering architecture discussions.Leadership struggles with project visibility and strategic alignment across initiativesAI TransformationAI fundamentally shifts workflows with universal knowledge access at fingertipsCraft democratization blurs traditional role boundaries (PMs prototyping, developers designing)Agentic workflows and agents collapse traditional development stack layersAI shortcuts enable one-button synthesis of workshops into product briefsProduct development lifecycle compression from 20 steps to 5 key phasesBedrock and Q services create significant business accelerationOrganizational DesignCommon organizational rhythms and rituals create shared working languageDriving maximum impact by aligning on big initiatives vs. distributed prioritiesCollaborating across all functions — product, engineering, design — and at all organizational levelsBottom-up innovation requiring clear problem communication throughout organizationInclusive environments welcoming ideas from junior and introverted team membersWorking backwards planning and PR FAQs adopted from Amazon methodologiesCreating the next big thing with MiroLarge enterprises use Miro for strategic planning, OKR planning, capacity planning, roadmappingVisual proof-of-concepts and live demos make abstract concepts tangibleSame-day product brief delivery improves team collaboration and ownershipVoice of customer integration: automated synthesis of feedback into feature developmentMiro uses Miro internally to build next-generation featuresEnhanced employee engagement alongside improved business outcomesCustomers consistently achieve 2-3x time-to-market improvementsParticipants:Jeff Chow – Chief Product and Technology Officer, MiroJohan Broman – EMEA ISV Head of Solutions Architecture, AWSFurther Links:Website: https://miro.com/page/product-leaders/Miro in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Andrea Malagodi, CTO of Sonar, discusses how the company successfully transitioned from on-premise to SaaS, leveraging AWS partnership and maintaining focus on developer-centric code quality and security solutions.Topics Include:Andrea Malagodi is CTO of Sonar, guest on podcastSonar founded 16+ years ago by three software engineersFounders wanted to help developers understand code quality issuesFocus on giving developers precise, actionable insights for improvementProducts include SonarQube Server, Cloud, and IDE versionsRecent acquisitions: ACR, Tidelift, and Structure 101 companiesSaaS journey began seven years ago with SonarQube CloudInitially targeted individual developers, then expanded to enterprisesNow multi-region with comprehensive enterprise features availableSeven million developers rely on Sonar's solutions globally400,000 organizations and 28,000 enterprise customers use SonarStarted SaaS to test market demand, not assumptionsEngaged customers early to understand migration requirements neededRecommends alpha versions with design customers for feedbackFree tier for open-source code enables quick trialEnterprise certifications (ISO 27001, SOC 2) build trustAWS partnership includes enterprise support and technical resourcesUsed CDK for infrastructure-as-code, experienced early adoption challengesMulti-region strategy should be considered from the beginningAWS Learning partnership certified all engineers in cloudCloud enables faster development cycles than traditional infrastructureRecommends avoiding architectural one-way doors during transitionConsider data residency requirements for global customer baseAI-generated code creates productivity gains but needs validationSonar provides deterministic rules for AI-generated code reviewWorking on MCP protocol and AI code quality solutionsSecurity approach is "start left" not "shift left"Advanced Security offering includes dependency scanning and vulnerabilitiesAvailable on sonarsource.com and AWS MarketplaceFree tier offers 50,000 lines of code analysisParticipants:Andrea Malagodi – Chief Technical Officer, SonarFurther Links:Website: www.sonarsource.comSonar in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Kanaiya Vasani, Chief Product Officer, explains how ExtraHop leverages AWS services and generative AI to help enterprise customers address the growing security challenges of uncontrolled AI adoption.Topics Include:ExtraHop reinventing network detection and response categoryPlatform addresses security, performance, compliance, forensic use casesBehavioral analysis identifies potential security threats in infrastructureNetwork observability and attack surface discovery capabilities includedApplication and network performance assurance built-in featuresTraditional IDS capability with rules and IOCs detectionPacket forensics for investigating threats and wire evidenceCloud-native implementations and compromised credential investigation supportExtraHop partnership with AWS spans 35-40 different servicesAWS handles infrastructure while ExtraHop focuses core competenciesExtraHop early adopter of generative AI in NDRNatural language interface enables rapid data access queriesEnglish questions replace complex query languages for usersAgentic AI experiments focus on SOC automation workflowsL1 and L2 analyst workflow automation improves productivityShadow AI creates major risk concern for customersUncontrolled chatbot usage risks accidental data leakageGovernance structures needed around enterprise gen AI usageVisibility required into LLM usage across infrastructure endpointsAI innovation pace challenges security industry keeping upModels evolved from billion to trillion parameters rapidlyTraditional security tools focus policies, miss real-time activity"Wire doesn't lie" - network traffic reveals actual behaviorExtraHop maps baseline behavior patterns across infrastructure endpointsAnomalous behavioral patterns flagged through network traffic analysisMCP servers enable LLM access through standardized protocolsStolen tokens allow adversaries unauthorized MCP server accessMachine learning identifies anomalous traffic patterns L2-L7 protocolsGen AI automates incident triage, investigation, response workflowsBest practices include clear policies, governance, monitoring, educationParticipants:Kanaiya Vasani – Chief Product Officer, ExtraHop NetworksSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/Notes:

Cribl's Field CISO Ed Bailey discusses how customers can manage the quality and quantity of data by providing intelligent controls between data sources and destinations.Topics Include:Cribl company name originCompany helps organizations screen data to find valuable insightsEd Bailey was Cribl's first customer back in 2018Data growth of 25% yearly created seven-figure cost increasesCEOs and CIOs complained about explosive data storage costsUsers demanded more data while budgets remained constrainedBailey discovered Cribl through a random Facebook advertisementCribl Stream sits between data sources and destinationsNo new agents required, uses existing infrastructure connectionsReduced data growth from 28% to 8% within yearDevelopment cycles shortened from six weeks to two weeksBailey managed global security and telemetry data systemsOperated large Splunk instance across forty different countriesTeam spent time collecting data instead of extracting valueCribl provided consistent data control plane for operationsSmart engineers could focus on machine learning solutionsMigrated from terrible SIEM to better security platformData strategy should focus on business requirements firstNot all data has the same business valueTier one: Critical data goes to expensive platformsTier two: Important data stored in cheaper lakesTier three: Compliance data in low-cost object storageSIEM costs around one dollar per gigabyte storedData lakes cost twelve to eighteen cents per gigabyteObject storage costs fractions of pennies per gigabyteAWS partnership provides scalable infrastructure for rapid growthEC2, EKS, and S3 are heavily utilized servicesCribl Search finds data directly in object storageAvoids costly data movement for search and analysisParticipants:Edward Bailey – Field CISO, CriblSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Saviynt Co-Founder Amit Saha discusses how their AWS partnership has enabled the identity security company to deliver comprehensive identity protection while minimizing organizational friction.Topics Include:Saviynt is leading identity security provider in marketSecures human, non-human, workforce, and privileged access identitiesEliminates friction while automating organizational access management processesBiggest challenge: reducing friction in new access processesSecond challenge: visibility into accumulated technical debt problemsLost business context makes access permissions difficult to unwindSaviynt provides quick visibility to prioritize identity risksShadow IT creates ungoverned workloads and cloud applicationsNeed integration with asset management and cloud providersMust derive intelligence from multiple disconnected information sourcesAWS partnership provides access to prolific customer baseAWS security owners are same buyers for SaviyntEleven-year AWS relationship with early security competencyISV Accelerate program connects with sellers and architectsRising Star program helps stand out in crowded marketplaceFind mutual customers for successful AWS partnership storiesGenAI in bad actors' hands compromises customer securityProduct engineering uses GenAI tools for better qualityAgentic AI creates new paradigm between human/non-human identitiesAgentic AI requires dynamic, fluid access management approachesAI agents can generate their own bots needing accessZero trust principles needed at broader scale for AINext twelve months: getting ahead of GenAI curveNew AWS services launch daily in GenAI spaceContributing to new standards like MCP and A2A protocolsAWS Marketplace simplifies procurement and buyer discovery processesEDP program and migration incentives benefit ISV transactionsAWS developer-friendly startup programs accelerate time to marketCloud-native approach enables predictable scaling and AWS integrationAWS-Saviynt partnership aims for once-in-generation security impactParticipants:Amit Saha – Co-Founder and Chief Growth Officer, SaviyntSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Architect Russell Leighton discusses how Panther's cloud platform revolutionizes security operations by treating detections as Python code and AI enabled alert vetting turning responses from hours into minutes. Topics Include:Panther is a cloud security monitoring tool (cloud SIEM)Works at massive scale, more cost-effective than legacy systemsKey differentiator: "detections as code" written in PythonBrings software engineering best practices to security operationsEnables unit testing and version control for security detectionsRecently adopted generative AI to improve security workflowsSOC burnout is renowned due to tedious ticket processingAI has intelligence of security engineer, works much fasterExample: Alert shows "Russ Leighton removed branch protection"Old way: Manual log analysis, checking user profiles manuallyTakes hours of squinting at detailed log dataNew AI way: Automatic vetting happens in minutesAI checks user profile in Okta or IDPDetermines engineer status, assesses typical behavior patternsProvides risk assessment based on historical alert dataLow risk for engineers, high risk for unusual usersExample: HR person accessing production code is escalatedCustomer quote: Takes vetting "from hours to seconds"Panther customers get dedicated AWS accounts for securityCompany can't see customer data, only self-reported metricsAI provides summaries, risk assessments, timelines, visualizationsAlso suggests remediations like human security engineer wouldInitial concerns about putting AI in production environmentCustomer feedback exceeded expectations with feature requestsAWS Bedrock integration addresses customer security concernsUses Anthropic Claude as base LLM through BedrockCustomers can enable additional Bedrock guardrails independentlyAI transparency prevents hallucination concerns through explanationsClaude's extended thinking mode shows reasoning processAI visualizes thinking with flowcharts explaining decision processParticipants:Russell Leighton – Chief Architect, PantherFurther Links:Website: Panther.comAWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Security leaders from Anthropic and AWS discuss how agentic AI is transforming cybersecurity functions to autonomously handle everything from code reviews to SOC operations.Topics Include:Agentic AI differs from traditional AI through autonomy and agencyTraditional AI handles single workflow nodes, agents collapse multiple stepsHigher model intelligence enables understanding of broader business contextsAgents make intelligent decisions across complex multi-step workflows processesEnterprise security operations are seeing workflow consolidation through GenAIOrganizations embedding GenAI directly into customer-facing production applicationsSoftware-as-a-service transitioning to service-as-software through AI agentsSecuring AI requires guardrails to prevent hallucinations in applicationsNew vulnerabilities appear at interaction points between system componentsAttackers target RAG systems and identity/authorization layers insteadLLMs hallucinate non-existent packages, attackers create malicious honeypotsGovernance frameworks must be machine-readable for autonomous agent reasoningAmazon investing in automated reasoning to prove software correctnessAnthropic uses Claude to write over 50% of codeAutomated code review systems integrated into CI/CD pipelinesSecurity design reviews use MITRE ATT&CK framework automationLow-risk assessments enable developers to self-approve security reviews40% reduction in application security team review workloadAnthropic eliminated SOC, replaced entirely with Claude-based automationIT support roles transitioning to engineering as automation replaces frontlineCompliance questionnaires fully automated using agentic AI workflowsISO 42001 framework manages AI deployment risks alongside securityExecutive risk councils evaluate AI risks using traditional enterprise processesAWS embeds GenAI into testing, detection, and user experienceFinding summarization helps L1 analysts understand complex AWS environmentsAmazon encourages teams to "live in the future" with AIInterview candidates expected to demonstrate Claude usage during interviewsSecurity remains biggest barrier to enterprise AI adoption beyond POCsVirtual employees predicted to arrive within next 12 monthsModel Context Protocol (MCP) creates new supply chain security risksParticipants:Jason Clinton – Chief Information Security Officer, AnthropicGee Rittenhouse – Vice President, Security Services, AWSHart Rossman – Vice President, Global Services Security, AWSBrian Shadpour – GM of Security and B2B Software Sales, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Spryker's Chief Product Officer, Elena Leonova, discusses the Spryker Business Intelligence platform and how working with AWS as a strategic advisor unlocked deeper opportunities for transformative growth.Topics Include:Elena Leonova introduces Spryker as digital commerce platformSpryker focuses on sophisticated B2B commerce transactionsTraditional industries: manufacturing, industrial goods, med techCustomers sell complex equipment like MRI machines, tractorsProducts are custom-built to order through procurement processesExtensive negotiation and aftermarket servicing are requiredCompetitors focus on fashion, food - not complex equipmentSpryker exclusively hosted on AWS cloud infrastructureAWS partnership enables new capabilities and customer innovationBusiness intelligence tools and AI capabilities now availableRicoh example: global manufacturer of industrial-grade printersRicoh sells through dealers and distributors worldwideS-Diverse: new automotive software marketplace partnership platformConnects automotive manufacturers with embedded software producersSpryker Business Intelligence powered by Amazon QuickSight launchedCommerce becoming more intelligent than traditional repeat purchasesComplex equipment buyers don't purchase MRI machines weeklyPlatform provides insights into customer portal navigation patternsCombines commerce data with search, CRM, competitive intelligenceHelps merchants identify revenue optimization signals from noiseBusiness intelligence integrated directly within Spryker platformCustomers should evaluate platform's future scalability and flexibilityRevenue optimization requires understanding what metrics to improveEasy-to-use data analysis prevents information overload problemsQuickSight's GenAI capabilities enable faster executive decision-makingAWS partnership provided cost optimization and innovation confidenceElena initially viewed AWS as just hosting providerBuilding shared vision with AWS unlocked deeper collaborationAWS became trusted advisor for strategy and partnershipsGenerative AI enables multi-persona communication across customer typesParticipants:Elena Leonova – Chief Product Officer, SprykerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

ActiveFence CEO Noam Schwartz discusses how his company evolved from protecting platforms against user-generated harmful content to helping companies deploy public-facing AI safely at scale.Topics Include:Noam Schwartz introduces himself as ActiveFence CEOFormer intelligence officer specializing in open source intelligenceMission: protect online experiences for everyone everywhereOnline platforms constantly hammered by various attacksAttacks include cybersecurity, abuse, hate speech, spamCompanies playing endless whack-a-mole game with violationsNeed scalable solution that works across languages/formatsDeveloped enterprise-grade technology for sophisticated companiesAmazon became customer and great partner early onGenerative AI introduction changed the game completelyLLMs non-deterministic unlike traditional programmed chatbotsSame input produces different outputs each timeAI deployed in customer support, healthcare, airlinesNew risks when models speak on company's behalfOne bad output creates legal and reputational damageCompanies need to deploy public-facing AI safelyTransition affects healthcare, finance, gaming, government sectorsBuilding on years of user-generated content expertiseNo specific ChatGPT moment triggered their AI pivotActiveFence was AI company since day oneModel companies like Amazon, Nvidia asked for helpRealized their expertise perfectly suited for AI safetyStaying on top of AI developments is impossibleFocus on customer adoption, not every new releaseMain enterprise challenge is trusting AI technologyUnrealistic expectations for 100% accuracy from AIMost companies will license existing models, not buildSecurity solutions remain independent like traditional cybersecurityParticipants:Noam Schwartz – CEO and Co-Founder, ActiveFenceOfer Oringher – Software and Technology Account Manager, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jeff Moncrief discusses Sonrai Security's Cloud Permissions Firewall, and the best practices for using AI-powered summaries and orchestration to ensure security at all points.Topics Include:Jeff Moncrief introduces Sonrai Security and Cloud Permissions FirewallFocus on achieving least privilege access in AWS quicklyLightweight orchestration layer secures IAM from inside outEliminates need to write hundreds of individual policiesCustomers struggle with identity risk in CNAP/CSPM toolsGenerative AI adoption driving top security use casesBedrock and AI agents mentioned daily by customersProduct managers should consider underlying platform security risksAI models have control over infrastructure they run onIdentity is fundamental infrastructure enabling AWS AI modelsSonrai uses Bedrock capability inside Cloud Permissions FirewallJust-in-time access provides temporary, time-boxed AWS accessBedrock generates session summaries from audit logs automaticallyPlain English insights show what happened during sessionsSession summaries improve audit compliance and incident responseCustomer with 1000 accounts manually deployed service controlsFriday afternoon deployment caused very bad weekend disasterPolicy inheritance issues broke child accounts and OUsPlanning and orchestration essential for scaling AI securitySonrai platform built 100% cloud-native on AWSCoordinates service control policies and resource control policiesJust-in-time access relies on IAM Identity CenterParticipates in ISV Accelerate and AWS MarketplaceSecurity best practices start with identity as foundation"Hackers don't hack, they just log in" philosophyEliminate standing privileges with just-in-time access patternsRestrict AI services by user, location, and accountReview over-permissioned or inactive third-party vendor accessActionable insights through useful logging and AI summarizationFuture focus on protecting new services and permissionsParticipants:Jeff Moncrief – Field CTO & Director of Sales Engineering, Sonrai SecurityLinks:Website – Sonraisecurity.comAWS Marketplace – Sonrai SecuritySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/