Guest:

• Royal Hansen, VP of Engineering at Google, former CISO of Alphabet

Topics:

• The "God-Like Designer" Fallacy: You've argued that we need to move away from the "God-like designer" model of security—where we pre-calculate every risk like building a bridge—and towards a biological model. Can you explain why that old engineering mindset is becoming risky in today's cloud and AI environments?
• Resilience vs. Robustness: In your view, what is the practical difference between a robust system (like a fortress that eventually breaks) and a resilient system (like an immune system)? How does a CISO start shifting their team's focus from creating the former to nurturing the latter?
• Securing the Unknown: We're entering an era where AI agents will call other agents, creating pathways we never explicitly designed. If we can't predict these interactions, how can we possibly secure them? What does "emergent security" look like in practice?
• Primitives for Agents: You mentioned the need for new "biological primitives" for these agents—things like time-bound access or inherent throttling. Are these just new names for old concepts like Zero Trust, or is there something different about how we need to apply them to AI?
• The Compliance Friction: There's a massive tension between this dynamic, probabilistic reality and the static, checklist-based world of many compliance regimes. How do you, as a leader, bridge that gap? How do you convince an auditor or a board that a "probabilistic" approach doesn't just mean "we don't know for sure"?
• "Safe" Failures: How can organizations get comfortable with the idea of designing for allowable failure in their subsystems, rather than striving for 100% uptime and security everywhere?

Resources:

• Video version
• EP189 How Google Does Security Programs at Scale: CISO Insights
• BigSleep and CodeMender agents
• "Chasing the Rabbit" book
• "How Life Works: A User's Guide to the New Biology" book

Guest:

• Chris Sistrunk, Technical Leader, OT Consulting, Mandiant

Topics:

• When we hear "attacks on Operational Technology (OT)" some think of Stuxnet targeting PLCs or even backdoored pipeline control software plot in the 1980s. Is this space always so spectacular or are there less "kaboom" style attacks we are more concerned about in practice?
• Given the old "air-gapped" mindset of many OT environments, what are the most common security gaps or blind spots you see when organizations start to integrate cloud services for things like data analytics or remote monitoring?
• How is the shift to cloud connectivity - for things like data analytics, centralized management, and remote access - changing the security posture of these systems? What's a real-world example of a positive security outcome you've seen as a direct result of this cloud adoption?
• How do the Tactics, Techniques, and Procedures outlined in the MITRE ATT&CK for ICS framework change or evolve when attackers can leverage cloud-based reconnaissance and command-and-control infrastructure to target OT networks? Can you provide an example?
• OT environments are generating vast amounts of operational data. What is interesting for OT Detection and Response (D&R)?

Resources:

• Video version
• Cybersecurity Forecast 2026 report by Google
• Complex, hybrid manufacturing needs strong security. Here's how CISOs can get it done blog
• "Security Guidance for Cloud-Enabled Hybrid Operational Technology Networks" paper by Google Cloud Office of the CISO
• DEF CON 23 - Chris Sistrunk - NSM 101 for ICS
• MITRE ATT&CK for ICS

Guest:

• Bruce Schneier

Topics:

• Do you believe that AI is going to end up being a net improvement for defenders or attackers? Is short term vs long term different?
• We're excited about the new book you have coming out with your co-author Nathan Sanders "Rewiring Democracy". We want to ask the same question, but for society: do you think AI is going to end up helping the forces of liberal democracy, or the forces of corruption, illiberalism, and authoritarianism?
• If exploitation is always cheaper than patching (and attackers don't follow as many rules and procedures), do we have a chance here?
• If this requires pervasive and fast "humanless" automatic patching (kinda like what Chrome does for years), will this ever work for most organizations?
• Do defenders have to do the same and just discover and fix issues faster? Or can we use AI somehow differently?
• Does this make defense in depth more important?
• How do you see AI as changing how society develops and maintains trust?

Resources:

• "Rewiring Democracy" book
• "Informacracy Trilogy" book
• Agentic AI's OODA Loop Problem
• EP255 Separating Hype from Hazard: The Truth About Autonomous AI Hacking
• AI and Trust
• AI and Data Integrity
• EP223 AI Addressable, Not AI Solvable: Reflections from RSA 2025
• RSA 2025: AI's Promise vs. Security's Past — A Reality Check

Guest:

• Heather Adkins, VP of Security Engineering, Google

Topic:

• The term "AI Hacking Singularity" sounds like pure sci-fi, yet you and some other very credible folks are using it to describe an imminent threat. How much of this is hyperbole to shock the complacent, and how much is based on actual, observed capabilities today?
• Can autonomous AI agents really achieve that "exploit - at - machine - velocity" without human intervention for the zero-day discovery phase?
• On the other hand, why may it actually not happen?
• When we talk about autonomous AI attack platforms, are we talking about highly resourced nation-states and top-tier criminal groups, or will this capability truly be accessible to the average threat actor within the next 6-12 months? What's the "Metasploit" equivalent for AI-powered exploitation that will be ubiquitous?
• Can you paint a realistic picture of the worst-case scenario that autonomous AI hacking enables? Is it a complete breakdown of patch cycles, a global infrastructure collapse, or something worse?
• If attackers are operating at "machine speed," the human defender is fundamentally outmatched. Is there a genuine "AI-to-AI" counter-tactic that doesn't just devolve into an infinite arms race? Or can we counter without AI at all?
• Given that AI can expedite vulnerability discovery, how does this amplified threat vector impact the software supply chain? If a dependency is compromised within minutes of a new vulnerability being created, does this force the industry to completely abandon the open-source model, or does it demand a radical, real-time security scanning and patching system that only a handful of tech giants can afford?
• Are current proposed regulations, like those focusing on model safety or disclosure, even targeting the right problem?
• If the real danger is the combinatorial speed of autonomous attack agents, what simple, impactful policy change should world governments prioritize right now?

Resources:

• "Autonomous AI hacking and the future of cybersecurity" article
• EP20 Security Operations, Reliability, and Securing Google with Heather Adkins
• Introducing CodeMender: an AI agent for code security
• EP251 Beyond Fancy Scripts: Can AI Red Teaming Find Truly Novel Attacks?
• Daniel Miessler site and podcast
• "How SAIF can accelerate secure AI experiments" blog
• "Staying on top of AI Developments" blog

Guest:

• Caleb Hoch, Consulting Manager on Security Transformation Team, Mandiant, Google Cloud

Topics:

• How has vulnerability management (VM) evolved beyond basic scanning and reporting, and what are the biggest gaps between modern practices and what organizations are actually doing?
• Why are so many organizations stuck with 1990s VM practices?
• Why mitigation planning is still hard for so many?
• Why do many organizations, including large ones, still rely on unauthenticated scans despite the known importance of authenticated scanning for accurate results?
• What constitutes a "gold standard" vulnerability prioritization process in 2025 that moves beyond CVSS scores to incorporate threat intelligence, asset criticality, and other contextual factors?
• What are the primary human and organizational challenges in vulnerability management, and how can issues like unclear governance, lack of accountability, and fear of system crashes be overcome?
• How is AI impacting vulnerability management, and does the shift to cloud environments fundamentally change VM practices?

Resources:

• EP109 How Google Does Vulnerability Management: The Not So Secret Secrets!
• EP246 From Scanners to AI: 25 Years of Vulnerability Management with Qualys CEO Sumedh Thakar
• EP248 Cloud IR Tabletop Wins: How to Stop Playing Security Theater and Start Practicing
• How Low Can You Go? An Analysis of 2023 Time-to-Exploit Trends
• Mandiant M Trends 2025
• EP204 Beyond PCAST: Phil Venables on the Future of Resilience and Leading Indicators
• Mandiant Vulnerability Management

Guests:

• Sivanesh Ashok, bug bounty hunter
• Sreeram KL, bug bounty hunter

Topics:

• We hear from the Cloud VRP team that you write excellent bugbounty reports - is there any advice you'd give to other researchers when they write reports?
• You are one of Cloud VRP's top researchers and won the MVH (most valuable hacker) award at their event in June - what do you think makes you so successful at finding issues?
• What is a Bugswat?
• What do you find most enjoyable and least enjoyable about the VRP?
• What is the single best piece of advice you'd give an aspiring cloud bug hunter today?

Resources:

• EP220 Big Rewards for Cloud Security: Exploring the Google VRP
• Cloud Vulnerability Reward Program Rules
• Insights from BugSWAT
• Google Cloud's Vulnerability Reward Program
• Critical Thinking Podcast

Guests:

• Alexander Pabst, Deputy Group CISO, Allianz
• Lars Koenig, Global Head of D&R, Allianz

Topics:

• Moving from traditional SIEM to an agentic SOC model, especially in a heavily regulated insurer, is a massive undertaking. What did the collaboration model with your vendor look like?
• Agentic AI introduces a new layer of risk - that of unconstrained or unintended autonomous action. In the context of Allianz, how did you establish the governance framework for the SOC alert triage agents?
• Where did you draw the line between fully automated action and the mandatory "human-in-the-loop" for investigation or response?
• Agentic triage is only as good as the data it analyzes. From your perspective, what were the biggest challenges - and wins - in ensuring the data fidelity, freshness, and completeness in your SIEM to fuel reliable agent decisions?
• We've been talking about SOC automation for years, but this agentic wave feels different. As a deputy CISO, what was your primary, non-negotiable goal for the agent? Was it purely Mean Time to Respond (MTTR) reduction, or was the bigger strategic prize to fundamentally re-skill and uplevel your Tier 2/3 analysts by removing the low-value alert noise?
• As you built this out, were there any surprises along the way that left you shaking your head or laughing at the unexpected AI behaviors?
• We felt a major lack of proof - Anton kept asking for pudding - that any of the agentic SOC vendors we saw at RSA had actually achieved anything beyond hype! When it comes to your org, how are you measuring agent success? What are the key metrics you are using right now?

Resources:

• EP238 Google Lessons for Using AI Agents for Securing Our Enterprise
• EP242 The AI SOC: Is This The Automation We've Been Waiting For?
• EP249 Data First: What Really Makes Your SOC 'AI Ready'?
• EP236 Accelerated SIEM Journey: A SOC Leader's Playbook for Modernization and AI
• "Simple to Ask: Is Your SOC AI Ready? Not Simple to Answer!" blog
• "How Google Does It: Building AI agents for cybersecurity and defense" blog
• Company annual report to look for risk
• "How to Win Friends and Influence People" by Dale Carnegie
• "Will It Make the Boat Go Faster?" book

Guest:

• Ari Herbert-Voss, CEO at RunSybil

Topics:

• The market already has Breach and Attack Simulation (BAS), for testing known TTPs. You're calling this 'AI-powered' red teaming. Is this just a fancy LLM stringing together known attacks, or is there a genuine agent here that can discover a truly novel attack path that a human hasn't scripted for it?
• Let's talk about the 'so what?' problem. Pentest reports are famous for becoming shelf-ware. How do you turn a complex AI finding into an actionable ticket for a developer, and more importantly, how do you help a CISO decide which of the thousand 'criticals' to actually fix first?
• You're asking customers to unleash a 'hacker AI' in their production environment. That's terrifying. What are the 'do no harm' guardrails? How do you guarantee your AI won't accidentally rm -rf a critical server or cause a denial of service while it's 'exploring'?
• You mentioned the AI is particularly good at finding authentication bugs. Why that specific category? What's the secret sauce there, and what's the reaction from customers when you show them those types of flaws?
• Is this AI meant to replace a human red teamer, or make them better? Does it automate the boring stuff so experts can focus on creative business logic attacks, or is the ultimate goal to automate the entire red team function away?
• So, is this just about finding holes, or are you closing the loop for the blue team? Can the attack paths your AI finds be automatically translated into high-fidelity detection rules? Is the end goal a continuous purple team engine that's constantly training our defenses?
• Also, what about fixing? What makes your findings more fixable?
• What will happen to red team testing in 2-3 years if this technology gets better?

Resource:

• Kim Zetter Zero Day blog
• EP230 AI Red Teaming: Surprises, Strategies, and Lessons from Google
• EP217 Red Teaming AI: Uncovering Surprises, Facing New Threats, and the Same Old Mistakes?
• EP68 How We Attack AI? Learn More at Our RSA Panel!
• EP71 Attacking Google to Defend Google: How Google Does Red Team