Resilient Cyber

Does restricting frontier AI in the name of safety actually make us less secure? Joshua Saxe joins me to make the case that it does, and that AI cybersecurity will be won through defender adoption, not restriction.Josh has spent 15 years at the intersection of AI and security. He built and ran the machine learning program at Sophos, then led security for Llama at Meta, covering security post training, evals, agent guardrails, and prompt injection prevention. He recently left to co-found a startup reimagining vulnerability and exposure management agentically. He also writes one of the most cited blogs on AI and cyber policy.In this episode:- Why restricting frontier model access harms defenders more than attackers- How monitored closed models put threat actors at a structural disadvantage- The jagged frontier, and why attackers don't need frontier models for most of their tradecraft- The national security and supply chain risks of pushing the world onto Chinese open weights models- Why exploits don't cause cyberattacks, and which attacker constituencies AI actually unblocks- The dual use ceiling on guardrails and classifiers- Where defenders should be adopting AI right now, from access management to SOC automation- Using agents to burn down the mountain of security technical debtChapters:0:00 Intro0:42 Josh's background, from blackhat teen to Llama security lead3:07 The case for diffusion over restriction6:14 Why restriction hurts defenders more than attackers10:19 The jagged frontier and what attackers actually use models for12:49 National security and the supply chain risk of Chinese open weights16:08 Exploits don't cause cyberattacks20:20 Where defenders should adopt AI right now24:20 Guardrails, classifiers, and the dual use problem27:34 Reimagining vulnerability management with agents32:17 The structural advantage defenders hold35:15 Policy wishes and the attacker's Claude Code momentFollow Josh:LinkedIn: https://www.linkedin.com/in/joshua-saxe-01845a1Substack: https://joshuasaxe181906.substack.comFollow Resilient Cyber:Substack: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners and leaders.#aisecurity #cybersecurity #vulnerabilitymanagement #aipolicy #opensourceai

Cybersecurity investor Sid Trivedi of Foundation Capital joins me to dig into AI SOC valuations, services-as-software, moats, and what founders should know heading into Black Hat.Sid is a Partner at Foundation Capital, where he invests at the seed and Series A stage with a focus on cybersecurity and IT infrastructure. This is our annual pre-Black Hat check-in, and a lot has moved since last year, from massive M&A to record-setting rounds in categories like the AI SOC.In this episode:- What has actually changed a year into the AI wave, and what hasn't- Services-as-software, the $4.6 trillion market thesis, and automating cyber workflows across the SOC, IR, pen testing, and threat intel- What AI means for cybersecurity jobs and how practitioners should adapt- Consolidation vs. best-of-breed after Palo Alto's $25B CyberArk deal and Alphabet's $32B Wiz acquisition- AI SOC valuations, including Seven AI's record Series A and Torq crossing a $1B valuation- The double-edged sword of big raises and why founders should be cautious about the valuations they accept- Why you can't simply spend your way to growth in cybersecurity- Moats and defensibility when frontier labs can push into your category- The Black Hat Innovator Investor Summit and the Startup Spotlight competitionChapters:0:00 Intro0:52 What's changed a year into the AI wave2:42 Services-as-software and the AI SOC9:38 AI adoption and forward deployed engineers10:57 M&A, platformization, and best-of-breed14:17 IT and security convergence, plus AI SOC valuations18:48 Seed-stage risk calculus vs. later-stage investors21:43 The double-edged sword of big raises26:20 Why you can't spend your way to growth29:05 Moats and defensibility in the frontier-lab era32:25 Deal flow, pricing, and staying disciplined37:06 Black Hat Innovator Investor Summit40:17 Startup Spotlight competition43:28 Wrap-upBlack Hat is offering listeners $500 off registration with code USA500Resilient.Connect with Sid:LinkedIn: https://www.linkedin.com/in/siddhanttrivedi/Foundation Capital: https://foundationcapital.comResilient Cyber: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners, founders, and leaders.

JJ of Gecko Security and former Disney and Costco CISO Ryan Knisley on why AppSec needs an AI security engineer, not another scanner.DescriptionAppSec has been stuck for years, drowning teams in noisy findings that never told them what was actually exploitable. JJ, co-founder and CEO of Gecko Security, and Ryan Knisley, former CISO at Disney and Costco, join Resilient Cyber to talk about what changes when an AI security engineer reasons across code, infrastructure, and design docs at once. We get into why business logic breaks traditional SAST, why attackers think in graphs while defenders think in lists, why MTTR is a broken metric, how Cal.com went closed source in the AI era, and where AI-driven AppSec consolidation lands over the next two years.Key takeawaysGecko is an AI security engineer, not another scanner. It reasons across code, infrastructure, and documentation, so a finding arrives already mapped to whether it is reachable in production and what data it touches.The context that tells you if a bug matters lives outside the code. Business logic, architecture, and runtime are where exploitability is decided, which is why scanning the code alone floods teams with noise.Business logic is why traditional SAST fails, and why an LLM alone will not fix it. The same endpoint with no auth check is a critical bug in a document store and expected behavior in a social app, and only design docs and architecture tell the two apart.Attackers think in graphs while defenders think in lists. A critical with a compensating control may not matter, while ten lows chained together can be the thing that actually reaches the asset you care about.Exploit development is being commoditized. JJ describes a near future where the whole internet becomes one big bug bounty scope with agents running campaign-level attacks, so the old severity-ranking lens no longer holds.Fix the class, not the ticket. Rather than patching bugs one by one, Gecko traces groups of findings back to the design decision that created them and eliminates every variant so the same issue never returns.MTTR is a broken metric. A variant of last week's bug returns with a fresh clock, so teams close tickets to look healthy while risk stays flat, which is why Gecko measures recurrence rate instead.Cal.com shows where open source is heading. After AI coding pushed its pull requests from about 30 a day to 100 with a one-person security team, being open source flipped from an advantage to a liability, so it went closed source and replaced four tools with one.Tool consolidation is a risk decision, not a cost exercise. Ryan's shiny object problem leaves teams stacking scanners nobody can fully staff, and collapsing the stack lets you cross-train people and reduce real complexity.The finding layer collapses, and human judgment moves up. When finding and fixing get cheap, the scarce work becomes deciding what is correct, whether to accept a risk on purpose, and owning the design decision for a whole class of bugs.Chapters00:00 Meet JJ and Ryan02:46 Why Gecko is an AI security engineer, not another scanner05:07 The trend of agentic and headless security tools05:53 Why business logic breaks traditional SAST06:27 The no-auth endpoint example and context outside the code09:06 Attackers think in graphs, defenders think in lists11:02 Commoditized exploit dev and the internet as one bug bounty13:55 Shift left and why MTTR is a broken metric15:07 Eliminating entire classes of vulnerabilities15:51 Recurrence rate and avoiding risky refactors18:22 The Cal.com case study and open source going closed20:48 Consolidation and the shiny object problem in security22:40 Where AI-driven AppSec lands in two years27:12 What it takes to trust an AI security engineer28:57 Where to find Gecko and the Black Hat talk

Every headline wants you to believe AI has rewritten the rules of cybersecurity. Eric Doerr, the Chief Product Officer at Tenable a Resilient Cyber Partner, is not so sure. After running security response at Microsoft and leading security products at Google Cloud, he came on to separate the genuine transformation from the noise, and his read is refreshingly grounded. The tools changed, but the fundamentals did not, and the teams that win are the ones who finally act on that.Why this conversation mattersEric sits at a rare intersection, having lived the post-breach world of the SOC and now building the pre-breach world of exposure management. That vantage makes him a sharp guide to what AI actually shifts for defenders, from why cheaper discovery makes prioritization more valuable to how AI becomes its own attack surface once agents start touching your data. If you own vulnerability or exposure management and you are trying to spend your next dollar well, this conversation is a practical map of where the real risk lives and what to automate first.Key takeawaysAttackers are ruthlessly economical. Eric calls bad actors the perfect capitalists, spending the least effort needed to hit their goal, which is why so many still get in through unpatched basics rather than anything AI-powered.AI has not rewritten the offense-defense balance. The attacker only ever had to be right once, layered defense and zero trust still hold, and the real lever is accelerating your program with fewer human loops rather than lamenting the asymmetry.Cheaper discovery makes context more valuable, not less. Reachability and exploitability mean most findings are not worth chasing, so as AI floods teams with more of them, telling the truly scary hundred from the theoretical ten thousand becomes the whole game.Being too small to target is a strategy on borrowed time. As automation drives the cost of attacks toward zero, the quiet bet that adversaries will hit weaker neighbors stops paying off, and Eric would move off that mentality now.Humans should not be the bottleneck on every fix. Getting the workflow and tooling right is most of the work, and the rest is the organizational willingness to let validated automation act, even when a business partner would feel better with a human in the loop.AI is special and not special at the same time. It is mostly just another attack surface, and Eric estimates 80 to 90 percent of securing it maps to patterns the industry already learned during the move to cloud.Shadow AI is the first surprise in almost every environment. When teams scan the endpoints they already interrogate for AI artifacts, nearly all of them find something they never sanctioned, which is why discovery has to come before control.The real AI risk is interconnection. A misconfigured database was a needle in a haystack until you wire it to an agent, and then a harmless question about the budget quietly returns data the asker should never see.Most breaches are not even CVEs. Citing the Verizon DBIR, Eric notes roughly two-thirds of breaches trace to misconfigurations, and since about a third of Tenable's findings are non-CVE, a third of your findings can carry two-thirds of your risk.Agentic automation is finally killing the toil. Early users are automating drudgery like asset tagging and full remediation workflows, with one manufacturing customer letting automation handle 80 to 90 percent and scheduling the rest for change windows with a human notified.Notable quotes“Bad actors are the most perfect representation of capitalism”Eric Doerr, on why attackers do the least work necessary and often skip AI entirely.“a third of their findings are two-thirds of their risk”Eric Doerr, on why misconfigurations, not CVEs, drive most breaches.“you're on the wrong side of history”Eric Doerr, on insisting a human eyeball every automated fix.

CVEs are on pace to hit nearly 70,000 in 2026, but Jerry Gamblin explains why the actual exploitable risk is staying surprisingly flat.DescriptionJerry Gamblin runs RogoLabs and built CVE.ICU, and he co-authored the FIRST mid-year vulnerability forecast that just put 2026 on pace for nearly 70,000 CVEs. He joins Resilient Cyber to separate the scary headline number from what actually matters for defenders. We get into why GitHub now publishes one in five CVEs, the rain versus flood distinction that explains why exploitable risk is flat even as raw volume explodes, what the NVD collapse means now that the CNAs have to step up, and how teams should really be triaging with EPSS and the CISA KEV catalog.Key takeawaysCVEs are on pace for nearly 70,000 in 2026, up more than 40 percent year over year. Much of the surge traces back to a single source, with GitHub now publishing one in five CVEs after scaling up its advisory team.The three drivers behind the surge are very different forces. AI-assisted discovery that nobody can definitively flag, a 449 percent jump in GitHub security advisories, and VulnCheck acting as a CNA of last resort all get lumped into one scary number.Rain versus flood is the frame that matters. Raw CVE volume is climbing fast, but once you filter for CISA KEV and EPSS the actionable, exploitable risk has stayed essentially flat.Most of the new findings are old human debt, not a new AI threat. The OWASP Top 10 has barely changed in 25 years, and tooling can now find those same mistakes at scale across mostly open source code.The AI moment is useful cover to finally patch. Jerry argues teams are using the AI hype cycle to win the time and resources to fix long-known issues, which is a genuinely good outcome.The NVD was the dam that fell. It was never fair to expect one small organization to enrich every CVE, so responsibility now shifts back to the CNAs and the large vendors that leaned on it for years.Treat CVE data as a product you pay for. Jerry's advice is to use procurement leverage, since demanding better CVE records before you renew a contract is one of the few real forcing functions available.What gets exploited has not really changed. VPN concentrators and the same old vulnerability classes still dominate, and the NSA's annual top 10 exploited bugs are reliably old, with no sign yet of AI driving widespread attacks.Asset inventory is still the real bottleneck. You cannot triage what you cannot see, and most organizations still cannot say with confidence whether they even run the software a given pile of CVEs affects.AI-accelerated exploitation is coming, but not as mass exploits. The bigger shift is a tireless attacker that loops on your network for days until it finds a way in, which is exactly what agents are best at.GuestJerry Gamblin, creator of CVE.ICU and founder of RogoLabs. Resources mentionedFIRST 2026 mid-year vulnerability forecastSubscribewww.resilientcyber.io

Niels Provos on why you don't need a frontier model to find zero days, why the Vulnpocalypse is overstated, and how security invariants change the game.DescriptionNiels Provos has spent twenty-five years in security, from writing bcrypt to running security at Google and Stripe, and he came on to push back on the panic around AI and vulnerabilities. He explains why finding zero days is an orchestration problem rather than a frontier-model problem, using his Iron Curtain runtime and an open-weight model to surface net-new bugs for the cost of a cheap scan. We get into security invariants and egress control, why remediation is the real bottleneck, why AI coding tools ignore the security abstractions you build, and why someone this technical keeps coming back to incentives over technology.Key takeawaysYou don't need a frontier model to find zero days. Niels used his Iron Curtain runtime and an open-weight model to surface net-new vulnerabilities, which is why he calls this an orchestration problem rather than a frontier-model problem.The Vulnpocalypse framing is overstated. Companies already sit on more vulnerabilities than they can manage, so more findings do not fundamentally change the picture, and the catchy panic mostly drives engagement.Security invariants beat patching one bug at a time. An invariant is an infrastructure guarantee enforced without ongoing human judgment, which makes entire classes of vulnerabilities irrelevant instead of chasing each one.Egress control is the canonical example. If a production service can only reach a few known domains, most vulnerabilities never get to fetch a second-stage payload, so the exploit chain stalls.The log4j story shows why it matters. As head of security at Stripe, egress control meant the malicious download could not execute, so the team had room to patch calmly instead of fighting an emergency.Remediation, not discovery, is the harder problem. The quality bar of not breaking working code in production is what keeps fixing slow, and AI has not solved that yet even as it makes finding cheap.AI coding tools ignore the security abstractions you build. When Niels asked Claude to add an endpoint to a carefully structured project, it bypassed his abstractions and wrote raw code, which is why frameworks need to be secure by default.The harness is the moat. A finite state machine that decomposes vulnerability finding into stages, each with a fresh context and a tight prompt, gets reliable results from weaker models that otherwise lose the plot.It is the incentives, not the technology. Companies do just enough security to avoid looking negligent, so without accountability shifting through something like Europe's NIS2, better tooling alone will not change outcomes.Open source maintainers need to be empowered. They often cannot afford the latest models or the tokens to run them, yet everyone builds on their free work, so helping them fix vulnerabilities has the broadest payoff in the ecosystem.

VulnCheck's Patrick Garrity on the NVD collapse, the first real AI disclosure wave, and why remediation, not finding bugs, is the bottleneck.DescriptionVulnerability management spent years as the chore everyone dreaded, and now it is one of the hottest topics in security because attackers made exploitation the number one way in. Patrick Garrity of VulnCheck rejoins the show to separate what is real from what is marketing. We get into the honest state of the NIST National Vulnerability Database after CISA pulled its funding, the new AI executive order that wants a clearinghouse for AI-discovered vulnerabilities, the first measurable wave of AI-assisted disclosures, and Patrick's audit of Anthropic's Glasswing ledger. We also dig into why cheap AI discovery makes the remediation bottleneck worse, how AI is raising the security poverty line, and whether the 90-day disclosure model still holds.Key takeawaysVulnerability management is hot again because attackers made it the top way in. As Patrick puts it, attention flows to wherever the attacker goes, and right now that is exploitation.The NIST NVD breakdown was worse than a backlog. A recent report confirmed CISA had stopped funding the NVD and NIST lost about half its funding, with no real plan to clear the backlog, which quietly hurts every defender who relies on enriched CVE data.A new AI executive order wants a clearinghouse for AI-discovered vulnerabilities, reportedly under Treasury. Patrick's reaction is that we already have a vulnerability database, the program is optional, and it may turn into a marketing race more than a coordination win.The first measurable AI disclosure wave is real. CVE volumes are up 563 percent for Chrome and GitHub advisories up 470 percent year to date, and Patrick separated genuine AI-assisted discovery from AI slop and from bugs that merely live in AI software by correlating researchers, domains, and email addresses across multiple advisory sources.Patrick audited Anthropic's Glasswing ledger and found the transparency lacking. He had around 80 vulnerabilities in his own database while the public ledger listed 27, several items had blown past their own 90-day disclosure window, and the ledger had not been updated in two weeks.Finding vulnerabilities is not the bottleneck, remediation is. AI makes discovery cheap, but the coordinated disclosure and fix process takes enormous human effort, and the median time to remediate even known exploited bugs is still measured in weeks.Exploitation looks like it is sustaining rather than surging. CISA KEV and VulnCheck KEV are tracking similar year-over-year volumes, partly because attackers already have more than enough to target and partly because you can only count the exploitation you can actually detect.AI is raising the security poverty line, at least for now. Token costs and access-restricted tools concentrate the most powerful discovery capabilities among well-funded teams, while smaller organizations lack the expertise to turn open-weight models into working vulnerability harnesses.The economics are circular. AI drives the surge in findings and attacker velocity, and AI is then sold as the fix, so teams pay to surface the problem and pay again to remediate it, all on consumption-based pricing against finite budgets.The 90-day disclosure norm mostly holds, though it may tighten. VulnCheck runs a strict 120-day policy with no exceptions and averages 45 to 48 days to fix and disclose, and for open source the fixing commit often makes the flaw public anyway.

For twenty years the security playbook started in the same place, find a vulnerability, prioritize it, and patch it. Doug Merritt, CEO of Aviatrix and former CEO of Splunk, thinks that playbook is quietly breaking, and his explanation has nothing to do with anyone being careless. The economics of offense changed underneath us, and most security programs are still funded as if they did not.Why this conversation mattersDoug has sat in two seats that give this argument weight. At Splunk he evangelized detect and respond, and now at Aviatrix he is arguing that detect and respond, while still important, is no longer enough on its own. That is not a vendor pivot so much as an honest reading of the incentives, and it lands differently coming from someone who built a business on the previous era. If you are a practitioner watching AI rewrite the attacker's cost curve, or a leader trying to defend a prevention-heavy budget to a board, this conversation reframes where the money should actually go.Key takeawaysOffense became a compute problem, and that is permanent. Finding and exploiting a vulnerability is a search task, and the cost per token has been deflating faster than Moore's Law. That is why this is a structural shift rather than a few headline demos, and why throwing compute at offense keeps getting cheaper and faster.Patching has a ceiling that offense does not. Every patch carries the risk of breaking something, so testing, deployment, and organizational friction cap how fast defenders can move. When vulnerability discovery scales freely and patching cannot, "find more and patch faster" turns into a race you are structurally set up to lose.The interesting question is not how they got in, it is where they went. Attackers increasingly arrive with valid credentials and move through the trust graph that runs across cloud services and CI/CD pipelines, including malware injected into trusted repositories. Once they look legitimate inside the environment, lateral movement and egress are where the real damage happens.Cloud rewarded velocity, and security paid the bill. Cloud providers made identity default-deny because someone has to own and pay for a workload, but they left networking wide open because their economic engine is developer velocity and security reads as friction. New agentic frameworks inherit that same wide-open default, connected to the internet with little oversight.A strong identity stance is necessary and not sufficient. Identity answers whether someone is allowed to act, not whether the action is an attack, which is why attackers log in rather than hack in. Human, agent, and workload identities are genuinely different, and workload identity in particular has been underserved.Containment is about blast radius, not about keeping everyone out. The mindset shift is to accept that breaches will occur and to govern every path a workload can take, so an incident stays local and recoverable. Done well, containment holds firm whether or not anyone has detected the attack yet.Blast radius has to become a boardroom metric. Doug's argument is that CISOs, CIOs, CEOs, and boards should be able to answer how reachable anything is from anything else, and treat that number as something to drive down deliberately rather than discover after an incident.AI is the reason containment is finally workable. The historic blocker to micro-segmentation was cognitive load across tens or hundreds of thousands of workloads. AI is strong at synthesis and pattern matching, which makes a staged path of observe, discover, monitor, and then enforce realistic, ideally starting with the internet-exposed workloads that have no filtering at all.

In this episode of Resilient Cyber, I sit down with Katie Norton, Research Manager for DevSecOps and Software Supply Chain Security at IDC, to unpack what application security looks like as AI moves from copilot to autonomous teammate across the software development lifecycle.We dive into:

In this episode, we sat down with Richa Gual, CEO of Complyance, the AI-first enterprise GRC platform that recently raised a $20M Series A led by GV (Google Ventures), to dig into how legacy GRC is finally being disrupted and what role AI agents play in that transformation.We discussed why GRC has lived in the dark ages for so long, stuck in static documents, snapshot-in-time assessments, system sampling, and self-attestations while the rest of IT moved to cloud, APIs, and automation. We unpacked the credibility crisis caused by commoditized compliance and rubber-stamp audits, the limits of the first wave of GRC automation, and what genuinely changes when agentic AI takes on evidence review, vendor risk, policy drafting, and customer trust workflows end-to-end.Richa shared Complyance's perspective on building agentic AI for the most sensitive data an organization holds, why explainability and isolation matter more in GRC than almost anywhere else, and how customers like Dropbox, CVS Health, and Major League Soccer are using AI agents to cut manual GRC work by 70% without lowering the assurance bar. We closed on what the next five years look like for the GRC workforce and whether the field can finally restore credibility to the phrase “compliance equals security.”

In this episode of Resilient Cyber, I sat down with Karl McGuinness — author of Control Plane and one of the sharpest voices working on identity in the agentic era — to unpack what most of the industry is still getting wrong about IAM for AI agents.Karl's thesis is a provocation: we spent two decades optimizing authentication and authorization, and we built that stack for human-paced execution. Agents remove the presence, pacing, and natural scope-limiting that made those controls work — and no amount of stronger credentials, tighter scopes, or faster JIT provisioning closes the structural gap. The real frontier isn't AuthN or AuthZ. It's delegation: how approved intent becomes bounded authority that stays governed across delegation chains, unfamiliar tools, consent expansion, revocation, and task termination.Chris and Karl dig into:↳ Why the industry optimized for the wrong question, and what changes when agents enter the loop ↳ The Execution Mandate — agents don't need your passport, they need your authority ↳ Why governing the stay matters more than governing the entry, and what continuous evaluation of authority looks like in practice ↳ Mission-Bound OAuth, including Karl's own pessimistic case against it ↳ AAuth vs. OAuth as the substrate for agentic identity, and what signal will tell us which one wins ↳ Why Mission Shaping is necessary but not sufficient when quiet expansion, headless execution, and stale state are in play ↳ Open-world OAuth, MCP, and first-contact trust — what the newer standards solve and the substrate gaps no draft is closing ↳ ID-JAG and Cross-App Access (XAA): why enterprise SaaS needs to abandon app-by-app OAuth islands ↳ The widening gap between IETF drafts and the "agentic IAM" being sold at RSA, and the minimum viable posture for teams running agents in production todayWhether you're a CISO, an identity architect, or a security leader trying to separate vendor narrative from substrate reality, this is a clear-eyed map of where agentic IAM actually is and where it has to go.

AI security feels fragile right now — and in this episode, Ron Bennatan, VP of Strategy, AI and Database Security at Varonis and founder of Guardium, JSonar, and AllTrue.ai, explains exactly why.Ron unpacks what "fragile" actually means in the context of AI: it's a black box that requires careful handling, is sensitive to pressure, and is being outpaced by change that isn't linear or polynomial — it's exponential. What took 30 years of AI development previously has been eclipsed by the last three months alone.Drawing on 30 years in data security, Ron walks through how his journey from Guardium (structured data) to Varonis (historically unstructured data) represents a reunion that was always inevitable — because the policies and security motions were always the same, even when the industry split the two apart. Now, with AI agents becoming the dominant access pattern in the enterprise — potentially replacing 99% of traditional human-driven data access — the data layer is emerging as the most durable signal in AI security.The conversation covers why the AllTrue.ai thesis — that consumability and bridging the governance/security divide are more important than the tools themselves — translated naturally into the Varonis platform. Ron also breaks down why least privilege is fundamentally harder with agents (the permissioning model can't be deterministic when the decision-making isn't), why agents being unaccountable — no salary, no fear of being fired — makes detective controls less effective, and why the industry must accelerate toward preventive controls and intent analysis operating at machine speed.Key topics covered:Why AI security is fragile: the black box problem and exponential rate of changeHow Varonis unifies structured and unstructured data security for the agentic eraLessons from AllTrue.ai on consumability, and collapsing AI governance and securityWhy 99% of enterprise data access will soon flow through AI agentsIntent analysis and chain-of-thought as the next frontier of data securityLeast privilege vs. least autonomy — and why the permissioning model must evolveWhy agents' lack of accountability breaks the detect-and-alert modelThe shift from monitoring to prevention and assurance at the data layer

Most organizations deploying AI today cannot answer a deceptively simple question. Which model is actually running in their environment?It is not a hypothetical concern. Model substitution, supply chain compromise, adversarial fine-tuning, and jurisdictional compliance gaps are all live risk vectors — and the industry has largely been relying on contractual guarantees from AI vendors rather than technical controls to address them.That gap is exactly what Project VAIL was built to close.In this episode I sat down with Manish Shah, Co-founder and CEO of Project VAIL (Verifiable Artificial Intelligence Layer). Manish is a repeat founder with 20+ years of company building experience, including as co-founder of LiveRamp, and he is now bringing that background to one of the most consequential unsolved problems in AI security, provably knowing and verifying which model is executing in your environment at runtime.VAIL's approach combines two core technologies. Behavioral fingerprinting creates a unique, verifiable identity for AI models based on how they actually behave during inference, without relying on access to model weights or architecture. ZkTorch, developed in collaboration with researchers at UIUC, brings zero-knowledge proofs to large generative AI models for the first time at practical scale, enabling cryptographic verification of model computations without exposing sensitive model internals.We covered a lot of ground in this conversation, including:Why behavioral fingerprinting is a fundamentally different and more resilient approach to model identification How model identity becomes a critical security primitive as agentic AI deployments expand Detecting prohibited and derivative models, including open-source models derived from Chinese-origin foundations like DeepSeek and Qwen Where frameworks like NIST AI RMF and the EU AI Act fall short on model verification requirements How verified model fingerprints fit into zero-trust architectures for AI systems and agentic workflows What standardization for verifiable AI needs to look like and which bodies should be driving itModel verification is not a niche research problem. It is becoming a foundational requirement for AI governance, compliance, and security in regulated industries and high-stakes deployments alike. This episode gives you both the technical grounding and the strategic context to understand why.

A new episode of the Resilient Cyber Show just dropped, and this one is a conversation I've been looking forward to for a long time.I sat down with Tanya Janca, better known to most of the AppSec world as SheHacksPurple. Tanya is the best-selling author of Alice and Bob Learn Application Security and Alice and Bob Learn Secure Coding, an OWASP Lifetime Distinguished Member, CEO of She Hacks Purple Consulting, and one of the most recognized voices in application security and developer education on the planet.The timing of this conversation is hard to overstate. The OWASP Top 10 2025 was announced at the Global AppSec Conference last year, with two new categories, Software Supply Chain Failures and Mishandling of Exceptional Conditions, and SSRF folded into Broken Access Control. Recently, Anthropic released the Claude Mythos Preview system card, documenting a model that has already found thousands of high-severity zero-day vulnerabilities autonomously, including bugs in every major operating system and web browser, and a 27-year-old vulnerability in OpenBSD.In other words, AppSec is at a hinge moment, and Tanya is exactly the right person to think out loud with about it.Here's what we get into:What the OWASP Top 10 2025 got right, what it missed, and how teams should actually use itAI-generated code, “vibe coding,” and Tanya's brand-new free prompt library for secure coding with AI assistants, SecureMyVibe.caWhat Mythos-class capabilities mean for the offense/defense asymmetry AppSec has always lived withHow AI is genuinely changing the SDLC, where it creates lift, where it creates noise, and where it creates entirely new attack surfaceArchitecting real defenses at the prompt layer, across MCP servers, and inside RAG pipelines, not just bolting content filters onto the front doorWhy developers are the new attack surface, and why a lot of what gets labeled as “supply chain attacks” lately is really a developer compromise that cascaded into the supply chainTanya's threat model, defense framework, and maturity model for protecting developers themselvesDevSec Station, Tanya's new podcast delivering 5–10 minute secure coding lessons in a format built for how developers actually consume contentWhat she'd change tomorrow about how AppSec programs are built and run if she could change just one thingThis is one of those conversations that ranges from the practical (what to do Monday morning) to the philosophical (what does it even mean to “secure software” when an AI can find more zero-days in a weekend than a Red Team finds in a year). Tanya brings the rare combination of deep technical chops, real teaching ability, and genuine warmth that makes a hard subject feel approachable.If you lead an AppSec program, write code for a living, run a security team trying to keep up with AI-assisted development, or you're just trying to figure out where this whole industry is heading, this is the episode for you.Resources from the episode:SecureMyVibeDevSec Station Podcast (Tanya's new show)She Hacks Purple ConsultingAlice and Bob Learn Application Security and Alice and Bob Learn Secure CodingOWASP Top 10 2025 — https://owasp.org/Top10/2025/Claude Mythos Preview System Card — AnthropicThanks for being here. If this episode landed for you, the best thing you can do is share it with one person on your team who'd find it useful, that's how this newsletter and show grow.

What happens to application security when AI agents start writing most of the code?Jack Cable knows both sides of this problem better than almost anyone. As a Senior Technical Advisor at CISA, he helped architect the Secure by Design initiative that challenged the entire software industry to stop shipping insecure products and expecting customers to clean up the mess. Now, as the founder of Corridor, he's building at the center of a question that didn't exist two years ago: how do you govern, secure, and trust code that no human wrote?In this episode, Jack walks us through the journey from federal cybersecurity policy to startup founder, and why he believes we're at an inflection point that makes everything before it look manageable. We talk about why a decade of shift-left never actually fixed the vulnerability backlog, and why the rise of coding agents, Cursor, Claude Code, Codex, and the internal tools enterprises are quietly building, is about to make that backlog look quaint.Jack makes the case for a new category he's helping define called Agentic Security Coding Management, and explains what separates it from the SAST tools and ASPM platforms security teams already have. We get into the uncomfortable duality of AI as both the source of the problem and the proposed solution, the frontier labs showing up in AppSec with unclear intentions, and the market confusion that's leaving CISOs struggling to tell real governance from repackaged scanning.We spend the back half of the conversation on the hard questions. What does real governance of AI-generated code actually look like when thousands of developers are running agents in parallel? Is it policy enforcement at the agent level, provenance tracking, runtime attestation, or something nobody has built yet? And drawing on his time at CISA, Jack shares where he sees regulation heading: liability frameworks, mandatory disclosure, and what happens if we get the policy either too heavy or too absent at the exact wrong moment.Whether you're a CISO trying to get ahead of this, a founder building in the space, or a developer watching your workflow transform in real time, this is the conversation that frames where AppSec goes from here.

When you fire up Claude Code, Cursor, or any AI coding agent, it launches with your full system permissions, your SSH keys, cloud credentials, browser passwords, every file on your machine. Most developers never think twice about it.Luke Hinds did. And then he built something about it.Luke is the creator of Sigstore, the cryptographic signing infrastructure now used by PyPI, Homebrew, GitHub, and Google as the industry standard for software supply chain security. In this episode, he joins Chris to talk about why he's watching the industry make the exact same mistake it made a decade ago, and what he built to try to stop it.We cover the full picture: why application-layer guardrails and system prompts fundamentally fail as security boundaries for AI agents (and what kernel-level enforcement actually means), the .md file as an emerging control plane attack surface, the OpenClaw wake-up call and what the skills marketplace ecosystem gets structurally wrong about trust and provenance, the approval fatigue problem and Anthropic's 17% false negative rate on Claude Code's auto-mode classifier, extending SLSA and Sigstore attestation frameworks to AI-generated code, and why LLM-as-a-judge may not be the silver bullet many are hoping for.Luke also makes a broader argument about where this is all heading — volumes of AI-generated code growing faster than human capacity to review it, junior engineers being priced out of the industry, and an aging cohort of engineers who can actually read and reason about code at depth. It's a candid, technically grounded conversation from someone who's been in open source security for 20+ years and has seen this movie before.nono is at nono.sh, one line to install, one line to run. No excuse not to

Show DescriptionJoe Levy is the CEO of Sophos and a 30-year cybersecurity veteran who has held technical and executive roles across some of the industry's most recognizable brands. In this episode, we dig into a stat that should reframe how the entire industry thinks about its mission: out of roughly 359 million businesses worldwide, fewer than 32,000 have a CISO. That's less than one in 10,000 organizations with a security strategy leader — and it's a number Joe worked with Cybersecurity Ventures to quantify for the first time.We explore what that structural gap means for how vendors build products, why the cybersecurity market is a 40-year-old market failure where spending goes up every year but outcomes don't improve, and how Sophos is betting that agentic AI can deliver CISO-level intuition to the hundreds of millions of organizations that could never conceive of hiring one. Joe breaks down where AI is genuinely delivering in security operations — and where the industry is overselling — drawing from Sophos's experience running the world's largest MDR service with 36,000 customers.We also get into Sophos's Pacific Rim disclosure, a five-year engagement with a Chinese nation-state actor targeting their firewalls that Joe calls the highest form of threat intelligence sharing. He walks through the calculus of going public with that story, including the kernel-level monitoring they deployed on a handful of devices to stay one step ahead of the attacker. Plus, we discuss the SecureWorks acquisition, the CTO-to-CEO transition, competing with hyperscalers like Microsoft, and what the next chapter looks like for a billion-dollar PE-backed security company approaching maturity with Thoma Bravo.Show NotesThe cybersecurity poverty line quantified: out of 359 million businesses worldwide, fewer than 32,000 have a CISO — less than one in 10,000 — and this leadership gap compounds with the skills shortage and what Joe calls an "AI-enhanced market for lemons" where information asymmetry between buyers and vendors is getting worseThe real problem isn't missing technology — most organizations already have endpoints and firewalls — it's misconfigurations, ignored alerts, undeployed agents, and no SOC to respond, which is why secure-by-default design and hybrid product-service models like MDR create more predictable outcomes than tools aloneAI in the SOC is overhyped but not hype: Sophos runs 36,000 MDR customers and says the vast majority of Tier 1 (triage, false positive management) and Tier 2 (investigation, response) can now be performed by agents — but the industry lacks standard vocabulary for metrics like MTTR, letting vendors be "intentionally opaque" about what "response" actually meansJoe introduces the concept of "humans as the accountability API" in an agentic world — AI can approximate analyst intuition, but someone still needs to be held accountable for remediation decisions, and a fully autonomous SOC may just be "a protection product with a very long data pipeline"The Pacific Rim story: Sophos spent five years engaged with a Chinese nation-state actor targeting their firewalls, deployed a kernel implant on fewer than a handful of attacker-controlled devices to observe exploit development in real time, and concealed targeted fixes among 150 other patches to avoid tipping off the adversarySophos's CISO Advantage program aims to deliver the intuitions of a skilled security leader to the hundreds of millions of organizations that could never hire one — Joe calls it fixing a 40-year-old market failure and says they're shipping it this year

Show DescriptionThe Zero Day Clock is ticking — and the numbers should make every security leader uncomfortable. In this episode, I sit down with Sergej Epp, CISO at a leading security firm, who built the Zero Day Clock after a weekend experiment using AI to discover vulnerabilities firsthand. What he found shocked him: with no professional vulnerability research background and just a few hours of work, he was successfully finding zero days across major security projects using AI models and basic scaffolding.Sergej breaks down his concept of the "Verifier's Law" — the idea that offense has the cheapest verifier in cybersecurity because feedback is binary and instant (you either popped a shell or you didn't), while defense operates in a space where validation is expensive, ambiguous, and slow. We dig into what this asymmetry means for the industry, why 20 years of warnings from Ross Anderson, Bruce Schneier, Halvar Flake, and others have gone unheeded, and whether coordinated disclosure models are broken now that AI can reverse engineer a patch into a working exploit in minutes.We also discuss the tension between regulation and deregulation playing out in the U.S. and EU, why the answer might be outcome-based accountability rather than prescriptive compliance, and what a realistic defensible posture actually looks like when the mean time to exploit for actively exploited vulnerabilities is under two days — while most organizations are still operating on 30-day patch cycles.Show NotesSergej shares how a weekend AI experiment led him to discover multiple zero days across major security projects with no professional vulnerability research experience — and why that should alarm the entire industryThe "Verifier's Law" explained: offense has cheap, deterministic validators (pop a shell, exfiltrate data, trigger an XSS) while defense faces expensive, ambiguous validation (parsing SIM alerts, measuring security posture), giving AI-accelerated offense a structural advantageThe Zero Day Clock synthesizes 3,500+ CVE-exploit pairs and shows the mean time to exploit for actively exploited vulnerabilities is now under two days — while organizations still operate on 14-to-30-day patch cycles20 years of ignored warnings: from Ross Anderson's 2001 economics paper through Bruce Schneier, Halvar Flake's "the patch is the advisory" insight, and DARPA's Cyber Grand Challenge — the industry has consistently failed to act on clear signalsAI can now reverse engineer patches to identify underlying flaws and generate working exploits in minutes, potentially breaking coordinated disclosure models and compressing the window between patch release and active exploitation to near zeroThe regulation paradox: the EU risks overregulating AI in ways that hamper defenders while attackers face no such constraints, while the U.S. is pushing deregulation that may remove the only forcing function for vendor accountability — Sergej and Chris discuss outcome-based regulation as a potential middle pathDefenders have a data advantage: by understanding their own environments, infrastructure, and processes, security teams can detect AI-driven attacks through behavioral anomalies like hallucinated API calls, non-existent user accounts, and other artifacts of AI-generated attack playbooksThe Zero Day Clock's real power is as a board-level communication tool — a single slide that translates the patching gap into a number executives and policymakers can't ignore, shifting the conversation from "are we compliant?" to "are we fast enough?"

Summary:In this conversation, Chris Hughes and Stanislav Fort discuss the transformative role of AI in cybersecurity, particularly in vulnerability management. Stanislav shares insights on how AI can discover zero-day vulnerabilities in widely used codebases, the challenges of balancing AI-driven discoveries with quality assurance, and the importance of proactive security measures. They also explore the economic sustainability of AI in cybersecurity, the burden on maintainers, and the ongoing arms race between defenders and attackers. The discussion emphasizes the potential for AI to significantly enhance software security and the aspiration towards achieving zero vulnerabilities in critical infrastructure.Takeaways:AI is revolutionizing vulnerability management in cybersecurity.The ability to find long-hidden vulnerabilities is unprecedented.AI can enhance both offensive and defensive security measures.Proactive security integration into development pipelines is essential.The quality of vulnerability reports is declining due to AI-generated noise.Maintainers face increasing burdens from rapid AI-driven discoveries.AI can help secure open source projects effectively.Sustainability in AI cybersecurity requires financial backing.The arms race between attackers and defenders is intensifying with AI.Achieving zero vulnerabilities is an aspirational yet achievable goal.Chapters00:00 Introduction to AI in Cybersecurity02:52 The Evolution of AI and Vulnerability Discovery05:45 AI's Impact on Software Development08:59 Discovering Zero-Day Vulnerabilities11:48 The Great Bifurcation in Security Research14:52 Balancing AI-Driven Discoveries and Quality17:59 Proactive Security Measures in Software Development20:53 The Role of AI in Securing Open Source Projects23:54 Sustainability of AI in Cybersecurity27:07 Addressing the Burden on Maintainers30:09 The Tension Between Autonomy and Security33:03 The Arms Race Between Defenders and Attackers36:12 Aiming for Zero Vulnerabilities38:58 Conclusion and Future Outlook

In this episode of Resilient Cyber I catch up with Momentum Cyber's Founder & CEO, Eric McAlpine.We will be unpacking 2025's M&A and capital market activities, using Momentum Cyber's 2025 Cybersecurity Almanac Report, as well as discussing some of the overlooked and untold details under the hood of cyber M&A, building world class teams and more.

In this episode of Resilient Cyber, we will be sat down with Ari Marzuk, the researcher who published "IDEsaster", A Novel Vulnerability Class in AI IDE's.We will be discussing the rise of AI-driven development and modern AI coding assistants, tools and agents, and how Ari discovered 30+ vulnerabilities impacting some of the most widely used AI coding tools and the broader risks around AI coding.Ari's background in offensive security — Ari has spent the past decade in offensive security, including time with Israeli military intelligence, NSO Group, Salesforce, and currently Microsoft, with a focus on AI security for the last two to three years.IDEsaster: a new vulnerability class — Ari's research uncovered 30+ vulnerabilities and 24 CVEs across AI-powered IDEs, revealing not just individual bugs but an entirely new vulnerability class rooted in the shared base IDE layer that tools like Cursor, Copilot, and others are built on."Secure for AI" as a design principle — Ari argues that legacy IDEs were never built with autonomous AI agents in mind, and that the same gap likely exists across CI/CD pipelines, cloud environments, and collaboration tools as organizations race to bolt on AI capabilities.Low barrier to exploitation — The vulnerabilities Ari found don't require nation-state sophistication to exploit; techniques like remote JSON schema exfiltration can be carried out with relatively simple prompt engineering and publicly known attack vectors.Human-in-the-loop is losing its effectiveness — Even with diff preview and approval controls enabled, exfiltration attacks still triggered in Ari's testing, and approval fatigue from hundreds of agent-generated actions is pushing developers toward YOLO mode.Least privilege and the capability vs. security trade-off — The same unrestricted access that makes AI coding agents so productive is what makes them vulnerable, and history suggests organizations will continue to optimize for utility over security without strong guardrails.Top defensive recommendations — Ari emphasized isolation (containers, VMs) as the single most important control, followed by enforcing secure defaults that can't be easily overridden, and applying enterprise-level monitoring and governance to AI agent usage.What's next — Ari is turning his attention to newer AI tools and attack surfaces but isn't naming targets yet. You can follow his work on LinkedIn, X, and his blog at makarita.com.

In this episode of Resilient Cyber, I sit down with VP, Product Marketing and Strategy for Protegrity, James Rice. We will be discussing how traditional approaches to security aren't solving the AI security challenge, the importance of data-centric approaches for secure AI implementation and addressing issues such as AI data leakage.James and I dove into a lot of great topics, including:Why traditional perimeter-based and infrastructure-centric security models are failing in the era of AI, and why organizations need to fundamentally rethink their approach to securing AI workloads.The concept of data-centric security — protecting the data itself rather than the systems surrounding it — and why this shift is critical as data flows across cloud platforms, AI models, and agentic workflows.The growing risk of AI data leakage and how sensitive information (PII, PHI, PCI, intellectual property) can inadvertently be exposed through AI training data, model outputs, prompt injection, and RAG pipelines.Why many organizations find themselves stuck in an "AI circularity" — wanting to leverage AI but unable to do so because of the complexity of securing critical business data throughout the AI lifecycle.The importance of embedding security controls inline within the AI pipeline — from data ingestion and model training to orchestration and output — rather than bolting security on after the fact.How data protection techniques such as tokenization, anonymization, dynamic masking, and format-preserving encryption can enable organizations to use realistic, context-rich data for AI while maintaining compliance and reducing risk.The challenge of securing agentic AI workflows, where autonomous agents continuously interact with enterprise data, making traditional access control models insufficient.How organizations can balance the need for AI innovation and data utility with regulatory compliance requirements across frameworks like GDPR, HIPAA, PCI DSS, and emerging AI-specific regulations.James's perspective on how security, risk, and compliance functions need to evolve to keep pace with the rapid productionization of AI across the enterprise.The role of semantic guardrails in governing AI inputs and outputs, ensuring that protection is applied contextually based on how data is being used — not just where it resides.About the GuestJames Rice is VP of Product Marketing and Strategy at Protegrity, a global leader in data-centric security. He brings over 20 years of experience in security, risk, and compliance, having provided solution engineering, value engineering, and implementation services to Fortune 1000 organizations across industries. Prior to Protegrity, James held leadership roles at Pathlock (formerly Greenlight Technologies), Accenture, and PricewaterhouseCoopers.About ProtegrityProtegrity is a data-centric security platform that protects sensitive data across hybrid, multi-cloud, and AI environments. Their approach embeds security directly into the data itself — enabling enterprises to unlock insights, accelerate innovation, and meet global compliance with confidence. Protegrity's solutions include data discovery and classification, tokenization, anonymization, dynamic masking, and semantic guardrails for AI and analytics workflows.Learn more at protegrity.com

In this episode of Resilient Cyber, I sit down with Jamieson O'Reilly, Security Researcher and Founder @ Dvuln.Jamieson recently went viral for his hacking activities demonstrating the vulnerabilities and exploitation of OpenClaw (previously ClawdBot and Moltbot), from exposed servers, backdooring skills and demonstrating how to perform potential account takeovers. Jamieson is now helping secure the OpenClaw project. We will walk through his findings, implications of the rise of Personal AI Assistants (PAI) and the various potential risks and security ramifications of insecure adoption and usage.

In this episode of Resilient Cyber, I sit down with longtime Cyber practitioners and leaders Helen Patton and Josiah Dykstra to dive into their latest book, "Switching to Cyber: The Mid-Career Guide to Launching a Cybersecurity Career".The book aims to help mid-career professionals pivot into the cyber career field and navigate finding their cyber niche, bridging skill gaps and conquering tech intimidation among more.

In this episode of Resilient Cyber I sit down with Anshuman Bhartiya to discuss AI-native AppSec. Anshuman is a Staff Security Engineer at Lyft, Host of the The Boring AppSec Community podcast, and author of the AI Security Engineer newsletter on LinkedIn. Anshuman has quickly become an AppSec leader I highly respect and find myself learning from his content and perspectives on AppSec and Security Engineering in the era of AI, LLMs and Agents.

In this episode of Resilient Cyber I'm joined by one of my favorite Vulnerability Researchers, Jerry Gamblin.Jerry recently published a comprehensive 2025 CVE retrospective, which we will dive into, as well as his thoughts around trends and patterns we may see emerge in the vulnerability management landscape moving into 2026 and beyond.

In this episode of Resilient Cyber, I sit down with my friend and the Founder of Return on Security (RoS),

In this episode I sit down with my friend and Vulnerability Researcher Patrick Garrity

In this episode of Resilient Cyber, I'm joined by Jesus Alejandro Cardenes Cabre, SVP of Product Architecture and John Xiaremba, Software Engineer, both from the VIA Knowledge Hub team to dig into all things post-quantum cryptography (PQC). This includes PQC standards, as well as practical steps developers must take today to mitigate future risks.

In this episode of Resilient Cyber, I sit down with Kamal Shah, Cofounder and CEO at Prophet Security, to discuss the State of AI in SecOps. There continues to be a tremendous amount of excitement and investment in the industry around AI and cybersecurity, with Security Operations (SecOps) arguably seeing the most investment among the various cybersecurity categories.Kamal and I will walk through the actual state of AI in SecOps, how AI is impacting the future of the SOC, what hype vs. reality is, and much more.

In this episode of Resilient Cyber, I sit down with longtime industry AppSec leader and Founder/CTO of Contrast Security, Jeff Williams, along with Contrast Security's Sr. Director of Product Security Naomi Buckwalter, to discuss all things Application Detection & Response (ADR), as well as the implications of AI-driven development.

In this episode, I sit down with a friend and ex-CIA Officer turned Cybersecurity leader, Ross Young over at CISO Tradecraft.We will be unpacking the topic of mastering the cybersecurity budget. This includes examining whether most cyber budgets are wasted, determining where and how to make investments, justifying spending, and more.Don't miss this chance to delve into an often-overlooked subject that many Cybersecurity leaders struggle with.

In this episode, I sit down with Mitchel Herckis, Global Head of Government Affairs at cloud security leader Wiz. We will be discussing all things public sector and cybersecurity, including the evolution of the FedRAMP program, modernizing vulnerability management, and the future of Continuous ATO (cATO).We covered a lot of ground, including:Mitch's background, both at Wiz and inside Government at roles such as OMBHow Wiz is working with Federal agencies and Defense Industrial Base (DIB) partners on Cloud Security, including the long-needed overhaul of FedRAMP with FedRAMP 20x's efforts.The move towards real Continuous Monitoring (ConMon) with real-time visibility of cloud environments, as well as the need for machine-readable artifacts, automations, and streamlined security control assessments.The modernization of vulnerability management, including factors such as attack paths, reachability, exploitability, known exploitation, and the importance of focusing on real risks versus noise.Moving away from paper-based compliance exercises and bridging the gap between security and compliance.Wiz's role as a CVE Numbering Authority (CNA) and the broader CVE program, including its importance for both the Government and industry when it comes to vulnerability management.To evolving usage of SBOMs and broader supply chain security.Disjointed efforts around the Government at both the Federal at State levels when it comes to Continuous ATO (cATO) and how we can move towards a more cohesive approach to modern system assessment and authorization.The importance of Government Affairs and bridging the divide between industry and Government, including bringing in tech leaders into Government, influencing policy, and improving outcomes for citizens and warfighters alike.The dual-edged sword that is AI adoption in the public sector.

In this episode of Resilient Cyber, I sit down with Founder & CEO of Paramify, Kenny Scott, to unpack the evolution of the FedRAMP program, FedRAMP 20x, and discuss what the public sector cloud compliance looks like moving into the future.Kenny and I dove into a lot of topics, including:What FedRAMP is and why it mattersWhat FedRAMP 20x is and what longstanding challenges associated with FedRAMP and public sector cloud and compliance it is addressingThe various aspects of FedRAMP 20x, including its phased rolloutChanges via FedRAMP 20x when it comes to Key Security Indicators (KSI), and how they differ from “controls”FedRAMP's modern vulnerability management approach and how it changes from the way vulnerability was historically handled under FedRAMPThe importance of automated assessments, machine-readable artifacts, real Continuous Monitoring (ConMon), and more for practical GRC EngineeringThe role of GRC platforms when it comes to modernizing GRCWhat are the implications of FedRAMP 20x for other public sector compliance programs, such as DoD's SWFT, SRG, and RMFSubscribe now

In this episode of Resilient Cyber, I sit down with repeat guest Snehal Antani, who serves as the Co-Founder & CEO of Autonomous Pen Testing leader Horizon3.ai.We will discuss the latest developments in AI and Autonomous Pen Testing, as well as the tremendous growth and success of Horizon3.ai, as Snehal balances technical topics with business-centric hard won wisdom of growing an industry leading organization.

In this episode of Resilient Cyber, I sit down with Astrix Security Co-Founder and CEO Alon Jackson to discuss the need for secure agentic adoption across the enterprise. This includes Astrix's approach, which involves enabling enterprises to discover, secure, and deploy AI agents responsibly at scale.

In this episode of Resilient Cyber, I sit down with Binalyze Founder/CEO Emre Tinaztepe.We will discuss how AI and automation are impacting the future of the SOC and the role that forensics-level data can play in incident response and recovery, as well as proactive threat hunting.

In this episode, I sit down with Andy Ellis, a longtime industry security leader who has turned investor, advisor, and mentor. We will discuss how security vendors can build effective marketing and sales teams and Andy's experience identifying and investing in industry-leading security startups. Don't miss this chance to hear from an industry legend who has worn multiple hats and excelled as an operating, investor, and overall security leader.

- One of the biggest SaaS security incidents recently of course is the Salesloft Drive/Salesforce incident, which impacted hundreds of organizations and involved compromised OAuth tokens. Can you tell us a bit about the incident and the fallout?- In an AppOmni blog on the incident, you all discuss attackers taking advantage of persistent OAuth access, over-permissive access, limited monitoring, and unsecured secrets. Why do these problems continue to plague organizations despite incidents like this?This is part of a broader trend of increased SaaS supply chain attacks. What makes these attacks so enticing for malicious actors and challenging for organizations to prevent entirely?You recently published your State of SaaS Security Report, which projects SaaS to grow 20% YoY between 2025 and 2032. This is despite 75% of organizations reporting a SaaS security incident in the past year. Why do you think we're seeing continued growth in adoption but still lagging in SaaS security to accompany the adoption?The report discusses the rise of NHIs and GenAI and how this will exacerbate problems around SaaS Access and incidents. Can you unpack that for us?I was shocked to see the report find that just 13% of organizations use SSPM tooling despite SaaS's widespread adoption. When you talk to enterprises, for example, nearly everyone is doing some CSPM activity for IaaS. Why are so many neglecting hygiene and posture for their SaaS footprint?

In this episode of Resilient Cyber, I sit down with the SANS Institute's Chief of Research (COR) & Chief AI Officer (CAIO), Rob T. Lee to discuss AI's impact on cybersecurity and the workforce. We will discuss SANS Critical AI Security Guidelines, the opportunities and obstacles AI presents for cybersecurity, and how practitioners should navigate AI's impact on the workforce.

In this episode of Resilient Cyber, I sit down with Gianna Whitver and Maria Velasquez to chat about the state of marketing in the cybersecurity industry, as well as their popular event "Cyber Marketing Con"In this episode, we discussed:The background of the CyberMarketingCon and what led Gianna and Maria to co-found the event and communityWhere marketers typically fall short and what can be done to drive more effective marketing and selling to security practitioners and leadersWhat practitioners can learn their marketing peers when it comes to communication, empathy, story telling, and building relationshipsThe importance of marketing, brand and broader GTM for security vendors to stand out from their competitorsWhat to keep an eye out for at the upcoming CyberMarketingCon in December in Austin Texas

In this episode I sit down with Michael Bargury, Co-Founder and CTO at Zenity to discuss all things AI Agent Security. Michael and the Zenity team have recently disclosed various AI agent risks, vulnerabilities and threats.

In this episode, I sit down with Andrew Carney, Program Manager for DARPA's AI Cyber Challenge (AIxCC). DARPA's AIxCC recently concluded at Black Hat, and it brought together the industry's leading experts on AI and Cybersecurity with a focus on securing software that is critical to all Americans.Teams had to create novel AI systems to secure critical code, include software involved in critical infrastructure.

In this episode we sit down with Sid Trivedi, Partner at venture capital firm Foundational Capital and host of the Inside the Network podcast. Sid brings great insights around cybersecurity market trends, industry events such as Black Hat and the impact that AI is having on the startup and venture capital ecosystem.

In this episode, I sit down with Daniel Bardenstein, CTO & Co-Founder of Manifest Cyber.We discussed the AI supply chain security, including open source risks, AIBOMs, best practices for CISOs, and regulatory approaches in the U.S. and EU.We dove into:What is the same and different between the risks AI introduces across the enterprise compared to open source software, and where and how the two converge.The rise of an “AIBOM” and why it is becoming a critical part of enterprise risk management in the AI EraThe work Daniel and others are doing as part of a Tiger Team defining “SBOM-for-AI-Use Cases”.Why is it so difficult for organizations to gain visibility into their AI models' internals, especially training data, model provenance, and pipeline dependencies?Where CISOs and security teams can get started when it comes to understanding where and how AI is being used and avoiding some mistakes.Gaps among the current waves of AI security startups and how they contrast with the approach Manifest is taking when managing AI supply chain risks.Real-world insights and examples of how organizations operationalize SBOM for risk reduction.Key differences between the U.S. and EU regarding regulatory approaches to AI and supply chain security risks.

In this episode, we sit down with Christian Posta, the Field CTO at Solo.io and an industry author and leader on topics such as Microservices, AI, and IAM.We will explore the rise of Agentic AI and its supporting protocols, such as MCP and A2A, and the broader challenges and considerations of Identity security in the age of LLMs.

In this episode, we sit down with Jim Manico, a longtime industry AppSec Leader, Educator, and Innovator, to discuss enhancing software security in the era of AI.This includes covering recent talks Jim has given about using AI as a force multiplier for software development, the importance of security-centric prompting, and the overall impact of AI on the field of AppSec.We discussed:A recent talk Jim gave where he discussed transforming secure software creation with AI, doing the work of teams of people on his own, and what used to take tens of thousands of hours through the use of agents and various frontier models and offerings.The importance of security-centric prompting and guidance for models to produce secure code and the impact on vulnerability velocity by doing so.The risks of the broader developer community leaning into these tools without adding security-centric prompts and guidance, but the opportunity for prompt libraries and enterprise controls to lead to systemic secure software development within the enterprise.The workforce implications of AI-driven development and the need to upskill to stay relevant (and employable).Where Jim sees opportunity beyond just AppSec when it comes to AI and Cybersecurity, in other areas such as GRC and SecOps as well.

In this episode, we sat down with AJ Yawn, Author of the upcoming book GRC Engineering for AWS and Director of GRC Engineering at Aquia, to discuss how GRC engineering can transform compliance.We discussed the current pain points and challenges in Governance, Risk, and Compliance (GRC), how GRC has failed to keep up with software development and the threat landscape, and how to leverage cloud-native services, AI, and automation to bring GRC into the digital era.We dove into:What the phrase “GRC Engineering” means and how it differs from traditional Governance, Risk and ComplianceWhat some of the major issues are with traditional compliance in the age of DevSecOps, Cloud, API's, Automation and now AISpecific examples of GRC Engineering, including the use of automation, API's and cloud-native services to streamline security control implementation, assessment and reportingThe promise and potential of AI in GRC, and how AJ is using various models for control assessments, artifact creation and more, and how GRC practitioners should be leveraging AI as a force multiplierAJ's new book “GRC Engineering For AWS: A Hands-On Guide to Governance, Risk and Compliance Engineering”

In this episode of Resilient Cyber, we chat with Patrick Duffy, Product Manager at Material Security, on Securing the Modern Workspace. The conversation will include discussions about the increased adoption of cloud office suites, limitations of traditional security approaches, and a deep dive into how Material Security is tackling issues such as securing email and data, identity threat detection, and posture management.Stepping back a bit before we get too specific, we've seen major fundamental shifts in the way organizations work and operate today, including widespread adoption of Cloud Office Suites (e.g., Google Workspaces, Microsoft 365, etc.). How have these shifts changed the threat landscape, and what sort of issues are we seeing with traditional security practices when it comes to securing these environments?We know phishing and email attacks are common and critical to protect against, but what about challenges around visibility of accounts/activity, sensitive data, and secure configurations and posture?Getting more specific to Material, can you help us understand how you all approach this problem space from a platform and offering perspective? What are some key features and abilities Material Security customers utilize to secure their cloud office suite environments, and what threats do they help against?What are some key differentiators for Material compared to some of the other vendors working on this problem, or even how do you all differ from some of the native security capabilities of environments such as M365 or Google Workspace?This space continues to evolve, both in terms of the cloud workspace environments and their usage by organizations and the relevant threats. How is Material preparing for these changes, whether it's the widespread adoption of AI, increased complexity, and so onIt's always great to hear some first-hand use cases and applications. Can you share some examples where Material Security has found success with specific customers and users of the solution?We've covered everything from the pitfalls and shortcomings of traditional security approaches to cloud office suites to where the market is headed. Where can folks learn more about Material, and what should we keep an eye out for next?

In this episode, I sit down with longtime industry researcher Wade Baker to dive into Cyentia's latest IRIS report. The report provides a data-driven look at incident trends, impacts, costs, and more.Are cyber incidents becoming more or less frequent? Are specific industries doing better than others? What does the average incident impact actually look like?Tune in to learn the answers, along with many other interesting insights!The report found that the number of security incidents continue to climb YoY, which isn't a surprise, although there has been peaks and valleys throughout various periods, note the huge uptick in 2021~Similar to recent reports such as DBIR and M-Trends, application exploitation (e.g., system intrusion) is climbing. In contrast, methods such as physical threat and others have declined due to increased cloud adoption, virtual infrastructure, and so on.One finding that may surprise some is that the proportion of incidents is going down for some organizations, particularly the largest enterprises, while it is going up for SMBs and smaller organizations. This ties to concepts such as the cybersecurity poverty line, which I have discussed in other articles, such as with Ross Haleliuk in our article “Lifting the world out of cybersecurity poverty.”This is likely due to factors such as large enterprise organizations having robust security teams, larger budgets, being able to afford the latest security tooling and more, while SMB's often fail to have many of these and deal with resource constraints in both dollars and expertise.We also see sectors which had historically low incidents now climbing, likely due to factors such as increased adoption of software and being digitally connected, as well as being a previously untapped sector for attackers

In this episode, I sit down with SAIC Chief Technology Officer (CTO) and longtime Federal/Defense leader Bob Ritchie to discuss his experience securing public sector digital modernization, including everything from large multi-cloud environments to zero trust, identity, and where things are headed with AI.Bob starts discussing SAIC and his background there. He went from intern to CTO over 20 years with this public sector industry leader, including a brief stint with Capital One on the commercial side.We covered the current state of the federal cloud community across multiple clouds (e.g., Azure, AWS, and GCP) and some of the challenges and opportunities on the security front.We often hear phrases such as “identity is the new perimeter,” but the perimeter is porous and problematic, especially in large, disparate environments such as the Federal/Defense ecosystem. Bob touched on the current state of identity security in this ecosystem, where progress is being made and what challenges still need to be tackled.The government is doing a big push towards Zero Trust, with the Cyber EO 14028, Federal/Defense ZT strategies, and more. But how much progress is being made on ZT, and where can we look for examples of innovation and success?We dove into the rise of excitement and adoption of AI, GenAI, Agentic AI, and protocols such as MCP, A2A, and where the public sector community can lean into Agentic AI for use cases ranging from SecOps, AppSec, GRC, and more.Bob explains how he balances a good business focus while staying deep in the weeds and proficient in relevant emerging technologies and nuances required as a CTO.I've known Bob for several years, and you would be hard pressed to find a more competent technology leader. This is not one to miss!