AWS for Software Companies Podcast

Dean Teffer of Arctic Wolf reveals how they process 8 trillion weekly security observations to find "a needle in a stack of needles," and breaks down real-world GenAI lessons learned.Topics Include:Dean Teffer, VP of AI at Arctic Wolf, discusses company's GenAI journeyArctic Wolf: decade-old security operations company serving mid-market customers globallyOperates massive security operation center, now launching AI-powered productsAI agent recently identified Black Basta ransomware attack, enabling rapid containmentDean's 15+ years in cybersecurity: traditional ML focused on detectionGenAI breakthrough allows natural language interaction with security modelsArctic Wolf processes 8 trillion weekly observations, correlating suspicious activitiesChallenge: finding specific threats in "stack of needles," not haystackSuccess measured by making human analysts faster, more consistent, scalableEvolved from treating GenAI like traditional ML to integrated workflowsKey misconception: GenAI isn't magic, needs proper data and reasoningAdvice: start with existing challenges, build flexible systems for adaptationGenAI excels at summarizing information and supporting complex decisionsFuture vision: AI handles routine threats, humans focus on creativityDemocratizing machine learning capabilities to broader range of subject expertsParticipants:Dean Teffer – Vice President of AI, Arctic WolfFurther Links:Arctic Wolf: 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/

CyberArk's technology leader discusses their strategy for securing against AI threats, protecting agentic AI systems, and their vision for the future in an increasingly AI-driven cybersecurity landscape.Topics Include:CyberArk celebrates recent exciting news while discussing their incredible cybersecurity journeyFounded in 1999, CyberArk pioneered privilege access management and expanded into comprehensive identity securityCompany executed textbook SaaS transformation from perpetual licensing to subscription-based cloud modelLeadership set clear customer expectations, framing SaaS shift as faster innovation deliveryAddressed customer concerns about cost predictability, security compliance, and data residency requirementsTechnical team implemented lift-and-shift architecture with AWS RDS and multi-tenant improvementsCorporate initiative tracked weekly metrics and milestones throughout full development lifecycle processCustomer Success evolved from transactional support to strategic partnership embedded in security journeysAWS partnership fundamental to cloud journey with 25+ integrations and Marketplace collaborationAI strategy focuses on three pillars: using AI, securing against AI threatsFuture 12-24 months: continue securing all identities while expanding AI capabilities and solutionsAWS partnership expanding in 2025 leveraging machine identity leadership and GenAI advancesParticipants:Peretz Regev – Chief Product & Technology Officer, CyberArkBoaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web ServicesFurther Links:· CyberArk: 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/

Three leading ISV executives from Coveo, DTEX Systems and Honeycomb, reveal how companies with proprietary datasets are gaining unbeatable competitive advantages in the AI era and share real-world strategies how you have similar outcomes.Topics Include:Panel introduces three ISV leaders discussing data platform transformation for AIDTEX focuses on insider threats, Coveo on enterprise search, Honeycomb on observabilityCompanies with proprietary datasets gain strongest competitive advantage in AI transformationData gravity concept: LLMs learning from unique datasets create defensible business positionsCoveo maintains unified enterprise index with real-time content and access rights syncHoneycomb enables subsecond queries for analyzing logs, traces, and metrics at scaleMulti-tenant architectures balance shared infrastructure benefits with single-tenant data separationCoveo deployed 140,000 times last year using mostly multi-tenant, some single-tenant componentsDTEX scaled from thousands to hundreds of thousands endpoints after architectural transformationCapital One partnership taught DTEX how to break monolithic architecture into servicesApache Iceberg and open table formats enable interoperability without data duplicationHoneycomb built custom format following similar patterns with hot/cold storage tiersBusiness data catalogs become critical for AI agents understanding dataset contextMCP servers allow AI systems to leverage structured cybersecurity datasets effectivelyDTEX used Cursor with their data to identify North Korean threat actorsReal-time AI data needs balanced with costs using right models for jobsCaching strategies and precise context reduce expensive LLM inference calls unnecessarilySearch remains essential for enterprise AI to prevent hallucination and access informationROI measurement focuses on cost reduction, analyst efficiency, and measurable business outcomesKey takeaway: invest in data structure early, context is king, AI is just softwareParticipants:Sebastien Paquet - Vice President of AI Strategy, CoveoRajan Koo - CTO, DTEX SystemsPatrick King - Head of Data, Honeycomb.ioKP Bhat - Sr Solutions Architecture Leader- Analytics & AI, Amazon Web ServicesFurther Links:Coveo: Website – LinkedIn – AWS MarketplaceDTEX Systems: Website – LinkedIn – AWS MarketplaceHoneycomb.io: 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/

Okta's CTO Bhawna Singh discusses AI adoption, innovation and the four critical identity patterns needed to build the trust that accelerates AI implementation.Topics Include:AI innovation races ahead while adoption lags due to trust and security concernsResearch shows 82% plan AI deployment but 61% of customers demand trust firstAI coding tools dramatically reduce development time, accelerating software delivery cyclesAI interaction evolved from ChatGPT conversations to autonomous headless agents working independentlyFuture envisions millions of agents making decisions and communicating without human oversightComplex data relationships emerge as agents access multiple dynamic sources simultaneouslyTrust fundamentally starts with identity - the foundation for all AI securityFour critical identity patterns needed: authentication, API security, user confirmation, and authorizationAuthentication ensures legitimate agents while token vaults enable secure agent-to-agent communicationAsynchronous user approval prevents rogue decisions like the recent database deletion incidentIndustry standards like MCP protocol establish minimum security guardrails for interoperabilityTrust accelerates AI adoption through security, accountability, and collaborative standard-building effortsParticipants:Bhawna Singh – CTO, Customer Identity, OktaSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

A panel discussion with AI industry leaders revealing how enterprises are scaling AI today, with predictions on coming breakthroughs for AI and the impact on Fortune 500 companies and beyond.Topics Include:Three technical leaders discuss production challenges: security, interoperability, and scaling agentic systemsPanelists represent Enkrypt (security), Anyscale (infrastructure), and CrewAI (agent orchestration platforms)Industry moving from flashy demos to dependable agents with real business outcomesBreakthrough examples include 70-page IRS form processing and multimodal workflow automationMultimodal data integration becoming crucial - incorporating video, audio, screenshots into decisionsLess than 10% of future applications expected to be text-onlyCompanies shifting from experimenting with individual models to deploying agent networksNeed for governance frameworks as enterprises scale to hundreds of agentsGrowing software stack complexity requires specialized infrastructure between applications and GPUsSecurity teams need centralized visibility across fragmented agent deployments across enterprisesExisting industry regulations apply to AI services - no special AI laws neededInteroperability standards debate: MCP gaining adoption while A2A seems premature solutionMCP shows higher API reliability than OpenAI tool calling for implementationsMultimodal systems more vulnerable to attacks but value proposition too high ignoreFortune 500 company automated price operations approval process using 630 brands data87% of enterprise customers deploy agents in private VPCs or on-premises infrastructureSpecialized AI systems needed to oversee other agents at machine speed scalesCost optimization through model specialization rather than always using most powerful modelsFuture learning may happen through context/prompting rather than traditional weight fine-tuningPredictions include AI meeting moderators and agents working autonomously for hoursParticipants:Robert Nishihara - Co-founder, AnyscaleJoão Moura - CEO, CrewAISahil Agarwal - Co-Founder & CEO, Enkrypt AIJillian D'Arcy - Sr. ISV Sales Leader, Amazon Web ServicesFurther Links:Anyscale – Website | LinkedIn | AWS MarketplaceCrewAI - Website | LinkedIn | AWS MarketplaceEnkrypt AI - 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/

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/

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/

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/

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/

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/

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/

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/

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/

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/

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/

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.Topics Include:Akanksha runs go-to-market team for Amazon at IntelPersonal and business devices transformed how we communicateForrester predicts 500 billion connected devices by 20265,000 billion sensors will be smartly connected online40% of machines will communicate machine-to-machineWe're living in a world of data delugeAI and Gen AI help make data effectiveGoal is making businesses more profitable and effectiveVarious industries need Gen AI and data transformationIntel advises companies as partners with AWSThree factors determine which Gen AI use cases adoptFactor one: availability and ease of use casesHow unique and important are they for business?Does it have enough data for right analytics?Factor two: purchasing power for Gen AI adoption70% of companies target Gen AI but lack clarityLeaders must ensure capability and purchasing power existFactor three: necessary skill sets for implementationNeed access to right partnerships if lacking skillsIntel and AWS partnered for 18 years since inceptionIntel provides latest silicon customized for Amazon servicesEngineer-to-engineer collaboration on each processor generation92% of EC2 runs on Intel processorsIntel powers compute capability for EC2-based servicesIntel ensures access to skillsets making cloud aliveAWS services include Bedrock, SageMaker, DLAMIs, KinesisPerformance is the top three priorities for successNot every use case requires expensive GPU acceleratorsCPUs can power AI inference and training effectivelyEvery GPU has a CPU head node component Participants:Akanksha Bilani – Global Sales Director, IntelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Victoria Chin of Asana and Michael Horn of AWS demonstrate how Amazon Q integrates with Asana to enable AI-powered workflows while dramatically reducing manual work and improving cross-functional collaboration.Topics Include:Victoria Chin introduces herself as Asana's CPO Chief of StaffMichael Horn from AWS discusses customer feedback on generative AIAI agents limited by quality of data pulled into themAmazon Q Business created to analyze information and take actionHundreds of customers using Q Business across various industries dailyAWS hosts most business applications, ideal for AI journeyAmazon Q has most built-in, managed, secure data connectors availableQ Index creates comprehensive, accessible index of all company dataSecurity permissions automatically pulled in, no manual configuration neededSupports both structured and unstructured data from multiple sourcesVictoria returns to discuss Asana's integration with Q IndexBillions invested in integrations, but usage still lags behindTeams switch between apps 1000 times daily, missing connectionsRoot problem: no reliable way to track who/what/when/whyContent platforms store work but don't manage or coordinateAsana bridges content and communication for effective teamwork scalingAI disrupting software, but questions remain about real valueSoftware must provide structured framework to guide LLMs effectivelyAI needs data AND structure to separate signal from noiseAsana Work Graph maps how work actually gets done organizationallyWork Graph visualized as interconnected data, not rows and columnsMost strategic work is cross-functional, requiring multiple teams collaboratingTraditional integrations require manual setup and knowing when to useQ Index gives Asana access to 40+ different data connectorsUsers can ask questions, get answers with cross-application contextAI Studio enables no-code building of workflows with AI agentsProduct launch example shows intake, planning, execution, and reporting stagesAI can surface relevant documents, research, and updates automaticallyChat is tip of iceberg; real power comes from embedded workflowsIntegration evolves from feature-level to AI-powered product-level connectionsParticipants:Victoria J. Chin – Chief of Staff / Product Strategy, AI, AsanaMichael Horn – Principal Head of Business Development – Artificial Intelligence & Machine Learning, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS leaders commemorate the podcast's 100th episode while looking ahead to expanded coverage of technology partners and continued focus on generative AI, modern data strategies, agentic AI solutions and more!Topics Include:Episode 100 celebrates milestone of AWS software companies podcastWeekly podcast shares ISV stories, best practices, guidanceToday features AWS leader thoughts on ISV communityArym Diamond heads North America data and AI salesSpecialist team helps win deals, create happy customersISV customers do cutting-edge work on AWS platformISVs create force multiplier effect for entire companyBuilding community through podcast video and audio contentKristen Backeberg leads global ISV partner marketing at AWSPodcast featured 157 ISV leaders from 121 companiesReached over 30,000 listeners across 90+ countries worldwideISV partners drive cloud innovation across all industriesAWS supports growth from startups to enterprise leadersAPN network designed to help partners succeed, scaleOlawale Oladehin directs ISV solutions architecture in North AmericaPodcast shares customer insights, journeys, and innovationsAWS technology continues evolving to meet customer needsCarol Potts leads North America ISV sales at AWSPodcast started less than two years agoFirst episode titled "Data the Engine for Growth"Customer obsession drives everything AWS does for ISVsDeep collaboration focused on joint ISV success partnershipsVishal Sanghvi heads ISV marketing for North AmericaISVs face pressure delivering products at generative AI paceModern data strategy foundational for ISV product successFavorite episodes include Snowflake, Wiz, Coupang discussionsAWS offers programs for every ISV persona typeFuture episodes focus on generative AI, cybersecurity, dataAgentic AI becoming important for production phase evolutionPodcast expanding scope to include technology partnersParticipants:Kristen Backeberg – Director, Global ISV, Solutions Enterprise and Alliance Partner Marketing, Amazon Web ServicesArym Diamond – Director, US ISV Specialists, Amazon Web ServicesOlawale Oladehin – Director, ISV, Solutions Architecture, North America, Amazon Web ServicesCarol Potts – GM, ISV Sales Segment, North America, Amazon Web ServicesVishal Sanghvi - Head of ISV Field Marketing, North America, 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/

Executive leaders from UneeQ and Zeta Global discuss the revolutionary impact of AI technologies that enable enhanced customer experiences and improved sales performances.Topics Include:Dave Cristini introduces panel on AI in advertising and marketing.Panel explores personalized experiences at scale with privacy focus.UneeQ creates AI-powered digital humans for brand interactions.Zeta Global uses AI to optimize customer messaging.LLMs combined with traditional ML empowers marketers to create models.Marketers can now build models without needing data scientists.AI agents integrated into systems can take action, not just respond.Agent chaining orchestrates sophisticated marketing actions automatically.AWS Bedrock provides tools to shape AI marketing future.Hyper-personalization becoming more achievable through AI automation.Ethics requires authenticity in brand AI representation.Transparency about data usage builds customer trust.Win-win approach: AI should augment teams, not just reduce costs.Integration difficulties remain a major challenge for AI implementation.AI agents have limited context windows and memory.Solution: Create specialized agents with persistent viewpoints.Companies need strong integration capabilities before implementing AI.Privacy regulations impact AI use in global marketing.Highly regulated industries require careful AI implementation strategies.Generative AI creates compliance challenges with unpredictable outputs.Digital humans eliminate judgment, revealing new customer insights.Banking clients discovered customers didn't understand financial terminology.Zeta improved onboarding with AI agents for data mapping.AI data mapping increased NPS scores and accelerated monetization.CMOs and CIOs increasingly collaborating on AI initiatives.Tension exists between marketing (quick wins) and IT (security).Strategic alignment with approved infrastructure enables scaling AI solutions.CEOs have critical role in aligning AI goals across departments.Internal AI use case: practicing sales with digital humans.Sales teams achieved 500% higher sales through AI role-playing.Participants:Danny Tomsett – Chief Executive Officer, UneeQRoman Gun – Vice President, Product, Zeta GlobalDavid Cristini – Director, ISV Sales, North America – Business Applications, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ash Pembroke, Portfolio CTO of Caylent, discusses the critical balance of data accuracy in the era of Gen AI for the benefit of boosting innovation.Topics Include:Ash Pembroke, Portfolio CTO of Caylent, self-identifies as a "recovering data scientist."Caylent is an AWS native services company.Data quality remains an issue despite Gen AI.Contrasts legalism versus mysticism in data quality.Legalism: accurate data when applications need it.Mysticism: insights that help decision-making.Traditional data foundations approach is being challenged weekly.Gen AI developments force rethinking of solution architectures.Teams share solutions through giant Slack threads.Example: Vector databases questioned after model context protocol.Still do traditional data assessments, but stay flexible.Integration and data processing constantly get abstracted.Data strategy equals architecture strategy equals business strategy.Traditional approach: standardize data across engineering teams.New approach: allow business users to innovate.Bring valuable techniques back to the organization.Case study: North Sea wind turbine alerts.Initially seen as data quality issue, revealed new predictive failure signal.Gen AI enables local experimentation by business users.Blurring lines between enterprise enablement and software building.BrainBox AI case study: energy optimization across buildings.Architecture decisions impact ability to scale products.Work with business edges rather than looking for patterns.Gen AI can process information from these working groups.Think about data as a product, not asset.Redimensionalize dependencies across your organization.Now's a good time to attack data quality.New tools help visualize complexity across organizations.Participants:· Ash Pembroke – Portfolio CTO, CaylentSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

New Relic's Head of AI and ML Innovation, Camden Swita discusses their four-cornered AI strategy and envisions a future of "agentic orchestration" with specialized agents.Topics Include:Introduction of Camden Swita, Head of AI at New Relic.New Relic invented the observability space for monitoring applications.Started with Java workloads monitoring and APM.Evolved into full-stack observability with infrastructure and browser monitoring.Uses advanced query language (NRQL) with time series database.AI strategy focuses on AI ops for automation.First cornerstone: Intelligent detection capabilities with machine learning.Second cornerstone: Incident response with generative AI assistance.Third cornerstone: Problem management with root cause analysis.Fourth cornerstone: Knowledge management to improve future detection.Initially overwhelmed by "ocean of possibilities" with LLMs.Needed narrow scope and guardrails for measurable progress.Natural language to NRQL translation proved immensely complex.Selecting from thousands of possible events caused accuracy issues.Shifted from "one tool" approach to many specialized tools.Created routing layer to select right tool for each job.Evaluation of NRQL is challenging even when syntactically correct.Implemented multi-stage validation with user confirmation step.AWS partnership involves fine-tuning models for NRQL translation.Using Bedrock to select appropriate models for different tasks.Initially advised prototyping on biggest, best available models.Now recommends considering specialized, targeted models from start.Agent development platforms have improved significantly since beginning.Future focus: "Agentic orchestration" with specialized agents.Envisions agents communicating through APIs without human prompts.Integration with AWS tools like Amazon Q.Industry possibly plateauing in large language model improvements.Increasing focus on inference-time compute in newer models.Context and quality prompts remain crucial despite model advances.Potential pros and cons to inference-time compute approach.Participants:Camden Swita – Head of AI & ML Innovation, Product Management, New RelicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Jon Yoo, CEO of Suger, shares how his company automates the complex & challenging workflows of selling software through cloud marketplaces like AWS.Topics Include:Jon Yoo is co-founder/CEO of Suger.Suger automates B2B marketplace workflows.Handles listing, contracts, offers, billing for marketplaces like AWS.Co-founder previously led Confluent's marketplace enablement product.Confluent had 40-50% revenue through cloud marketplaces.Required 10-20 engineers working solely on marketplace integration.Engineers prefer core product work over marketplace integration.Product/engineering leaders struggle with marketplace deployment requirements.Marketplace customers adopt without marketing, creating unexpected management needs.Version control is challenging for marketplace-deployed products.License management through marketplace creates engineering challenges.Suger helps sell, resell, co-sell through AWS Marketplace.Marketplace integration isn't one-time; requires ongoing maintenance.Business users constantly request marketplace automation features.Suger works with Snowflake, Intel, and AI startups.Data security concerns drive self-hosted AI deployments.AI products increasingly deploy via AMI/container solutions.AI products use usage-based pricing, not seat-based.Usage-based pricing creates complex billing challenges.AI products are tested at unprecedented rates.Two deployment options: vendor cloud or customer cloud.SaaS requires reporting usage to marketplace APIs.Customer-hosted deployment simplifies some billing aspects.Marketplaces need integration with ERP systems.Version control particularly challenging for AI products.Companies need automated updates for marketplace-deployed products.License management includes scaling up/down and expiration handling.Suger aims to integrate with GitHub for automatic updates.Participants:· Jon Yoo – CEO and Co-founder, SugerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

SentinelOne's Ric Smith shares how Purple AI, built on Amazon Bedrock, helps security teams handle increasing threat volumes while facing budget constraints and talent shortages.Topics Include:Introduction of Ric Smith, President of Product Technology and OperationsSentinelOne overview: cybersecurity company focused on endpoint and data securityCustomer range: small businesses to Fortune 10 companiesProducts protect endpoints, cloud environments, and provide enterprise observabilityRic oversees 65% of company operationsPurple AI launched on AWS BedrockPurple AI helps security teams become more efficient and productiveSecurity teams face budget constraints and talent shortagesPurple AI helps teams manage increasing alert volumesTop security challenge: increased malware variants through AIAI enables more convincing spear-phishing attemptsIdentity breaches through social engineering are increasingVoice deepfakes used to bypass security protocolsFuture threats: autonomous AI agents conducting orchestrated attacksSentinelOne helps with productivity and advanced detection capabilitiesSentinelOne primarily deployed on AWS infrastructureUsing SageMaker and Bedrock for AI capabilitiesBest practice: find partners for AI training and deploymentCustomer insight: Purple AI made teams more confident and creativeAI frees security teams from constant anxietySentinelOne's hyper-automation handles cascading remediation tasksMultiple operational modes: fully automated or human-in-the-loopAgent-to-agent interactions expected within 24 monthsCommon misconception: generative AI is infallibleAI helps with "blank slate problem" providing starting frameworksAI content still requires human personalization and reviewAWS partnership provides cost efficiency and governance benefitsParticipants:· Ric Smith – President – Product, Technology and Operations, SentinelOneSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Sam Gantner, Chief Product Officer of Nexthink, reveals how DEX is moving IT from reactive firefighting to proactive problem prevention and transforming enterprise productivity.Topics Include:DEX stands for Digital Employee ExperienceDEX eliminates IT issues preventing employee productivityShifts IT from reactive to proactive problem-solvingEmployees often serve as IT problem alerting systemsBest IT is transparent to employeesDEX solves device sluggishness and slow application issuesNetwork problems consistently appear across organizationsIT teams often lack visibility into employee experiencesMany organizations waste money on unused software licensesDEX Score measures comprehensive employee IT experienceSurveys capture subjective aspects of technology experienceReduction of actual problems differs from ticket reductionNexthink uses lightweight agents on employee devicesBrowser monitoring essential as browsers become application platformsEmployee engagement metrics capture real-time feedbackNexthink rebuilt as cloud-native platform using AWS servicesCompany deploys across 10+ global AWS regions30% of engineering resources dedicated to AI developmentOne customer eliminated 50% of IT ticketsAnother recovered 37,000 productivity hours worth $3M annuallyA third saved $1.3M by identifying unused licensesAI implementation requires dedicated employee trainingGood AI now better than perfect AI neverTechnology adoption is the next DEX frontierDigital dexterity becoming critical for maximizing IT investmentsParticipants:Samuele Gantner – Chief Product Officer, NexthinkSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/