Podcast
Resilient Cyber
Hosted by Chris Hughes · EN
Resilient Cyber brings listeners discussions from a variety of Cybersecurity and Information Technology (IT) Subject Matter Experts (SME) across the Public and Private domains from a variety of industries. As we watch the increased digitalization of our society, striving for a secure and resilient ecosystem is paramount.
30episodes
Episodes
Newest firstAll episodes
Securing the Agentic SDLC
5d ago00:49:24Tap to summarizeIn 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:🤖 AI's accelerating impact on AppSec and the SDLC – and the productivity-versus-risk equation now that agentic coding tools are shipping code at machine speed💥 The "Vulnpocalypse" – the explosion of CVEs, AI-generated code, and the widening gap between vulnerability discovery and remediation capacity🛠️ Whether legacy AppSec categories like SAST, DAST, SCA, and ASPM can keep pace – or are being fundamentally reinvented for an agentic world🎯 The rise of autonomous pen testing and offensive security agents (XBOW, Project Naptime, Project VAIL) and what it means when offense scales faster than defense🔗 How agentic development is reshaping software supply chain risk – from hallucinated packages to MCP server integrity and the provenance of code no human ever wrote🏛️ Governance models for AI-generated code, the evolving AppSec team of the future, and what CISOs should be prioritizing right now📈 Katie's predictions for where AppSec, software supply chain security, and the SDLC are heading over the next 18-24 monthsWhether you're an AppSec practitioner, security leader, developer, or just trying to make sense of how AI is reshaping software security – this conversation is packed with insights you won't want to miss.🔔 Subscribe for more conversations on cybersecurity, AI security, and the future of resilient software.#Cybersecurity #AppSec #AISecurity #DevSecOps #AgenticAI #SoftwareSupplyChain #ResilientCyber
Transcribe →The Agentic GRC Revolution
2w ago00:32:11Tap to summarizeIn 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.”
Transcribe →Identity as Infrastructure in the Agentic Era
3w ago00:33:30Tap to summarizeIn 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.🔗 Karl's writing: https://notes.karlmcguinness.com/ 🔗 Subscribe to Resilient Cyber on Substack: https://www.resilientcyber.io/ 🔗 Follow Chris on LinkedIn: https://www.linkedin.com/in/resilientcyber/
Transcribe →Why AI Security Feels So Fragile
May 100:23:17Tap to summarizeAI 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
Transcribe →You Can't Trust What You Can't Verify — The Case for AI Model Identity
Apr 2800:01:03Tap to summarizeMost 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.
Transcribe →Securing the Vibe: Tanya Janca on AI-Generated Code, Mythos, and the New AppSec Reality
Apr 2700:38:24Tap to summarizeA 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.
Transcribe →AI and the Future of Secure Coding
Apr 1600:23:51Tap to summarizeWhat 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.
Transcribe →Your AI Agent Is Running As Root
Apr 800:44:32Tap to summarizeWhen 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
Transcribe →The 350 Million Problem: Securing the Businesses No One Else Will
Mar 1700:45:03Tap to summarizeShow 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
Transcribe →Before the Breach: The Zero Day Clock and the Race Against Exploitation
Mar 1100:05:17Tap to summarizeShow 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?"
Transcribe →