Cloud Security Podcast by Google

Guest: Alex Shulman-Peleg, Global CISO at Kraken Topics: You mentioned that centralized security can't work anymore. Can you elaborate on the key changes—driven by cloud, SaaS, and AI—that have made this traditional model unsustainable for a modern organization? Why do some persist at centralized, top down approach to security, despite that? What do you mean by "Freedom, Responsibility and distributed security"? Can you explain the difference between "centralized security" and what you define as "security with distributed ownership"? Is this the same "federated"? In our conversation you mentioned "cloud and AI- native", what do you mean by this (especially "AI-native") and how is this changing your approach to security? You introduce the concept of "Security as quality" suggesting that a security-unaware developer is essentially a bad software developer. How do you shift the culture and internal metrics to make security an inherent quality standard, rather than a separate, compliance-driven checklist? You likened the central security team's new role to a "911 emergency service." Beyond incident response, what stays central no matter what, and how does the central team successfully influence the security posture of the entire organization without being directly responsible for the day-to-day work. Resources: Video version EP129 How CISO Cloud Dreams and Realities Collide EP258 Why Your Security Strategy Needs an Immune System, Not a Fortress with Royal Hansen EP212 Securing the Cloud at Scale: Modern Bank CISO on Metrics, Challenges, and SecOps

Guest: Dennis Chow, Director of Detection Engineering at UKG Topics: We ended our season talking about the AI apocalypse. In your opinion, are we living in the world that the guests describe in their apocalypse paper? Do you think AI-powered attacks are really here, and if so, what is your plan to respond? Is it faster patching? Better D&R? Something else altogether? Your team has a hybrid agent workflow: could you tell us what that means? Also, define "AI agent" please. What are your production use cases for AI and AI agents in your SOC? What are your overall SOC metrics and how does the agentic AI part play into that? It's one thing to ask a team "hey what did y'all do last week" and get a good report - how are you measuring the agentic parts of your SOC? How are you thinking about what comes next once AI is automatically writing good (!) rules for your team out of research blog posts and TI papers? Resources: Video version Agentic AI in the SOC: Build vs Buy Lessons EP255 Separating Hype from Hazard: The Truth About Autonomous AI Hacking EP256 Rewiring Democracy & Hacking Trust: Bruce Schneier on the AI Offense-Defense Balance EP252 The Agentic SOC Reality: Governing AI Agents, Data Fidelity, and Measuring Success EP236 Accelerated SIEM Journey: A SOC Leader's Playbook for Modernization and AI EP242 The AI SOC: Is This The Automation We've Been Waiting For? Google Cloud Skill Boost

Guest: Vishwas Manral, CEO at Precize.ai Topic: Why is agent security so different from "just" LLM security? Why now? Agents are coming, sure, but they are - to put it mildly - not in wide use. Why create a top 10 list now and not wait for people to make the mistakes? It sounds like "agents + IAM" is a disaster waiting to happen. What should be our approach for solving this? Do we have one? Which one agentic AI risk keeps you up at night? Is there an interesting AI shared responsibility angle here? Agent developer, operator, downstream system operator? We are having a lot of experimentation, but sometimes little value from Agents. What are the biggest challenges of secure agentic AI and AI agents adoption in enterprises? Resources: Top 10 threats and mitigation for AI Agents Past podcast AI episodes Cloud CISO Perspectives: How Google secures AI Agents (and paper) Top AI Risks from SAIF CoSAI From turnkey to custom: Tailor your AI risk governance to help build confidence

Guest: Elie Burstein, Distinguished Scientist, Google Deepmind Topics: What is Sec-Gemini, why are we building it? How does DeepMind decide when to create something like Sec-Gemini? What motivates a decision to focus on something like this vs anything else we might build as a dedicated set of regular Gemini capabilities? What is Sec-Gemini good at? How do we know it's good at those things? Where and how is it better than a general LLM? Are we using Sec-Gemini internally? Resources: Video version EP238 Google Lessons for Using AI Agents for Securing Our Enterprise EP255 Separating Hype from Hazard: The Truth About Autonomous AI Hacking EP168 Beyond Regular LLMs: How SecLM Enhances Security and What Teams Can Do With It EP171 GenAI in the Wrong Hands: Unmasking the Threat of Malicious AI and Defending Against the Dark Side Big Sleep, CodeMender blogs

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