Cloud Security Podcast by Google

No description provided.

No description provided.

Guest: Matt Gregson, Principal - PwC Cyber Security Topics: What is the state of the art of "agentic SOC" in 2026? Can you describe the most agentic SOC you've seen so far? In your experience, what are the main measurable benefits of AI agents in a SOC and IR? Imagine a 2030 SOC, what do humans do? Tell us more about how you judge if a client SOC is ready for AI and agents? What is the "Ouch" moment where most organizations realize their data isn't ready for that level of autonomy? Should we be more afraid of "AI hallucinations" or "Human fatigue" in the SOC? If a team has an agentic teammate making its own decisions based on emergent reasoning, how do you audit its "thought process"? Everyone loves to talk about "Time Saved," but in an agentic SOC, we care about "Decision Quality." What is the one metric PwC uses to prove that a SOC agent deployment is actually reducing risk? We often hear about "human-agent teaming." Are they still looking at alerts, or are they just approving "Action Plans" generated by the AI? Resources: Video version EP236 Accelerated SIEM Journey: A SOC Leader's Playbook for Modernization and AI EP252 The Agentic SOC Reality: Governing AI Agents, Data Fidelity, and Measuring Success EP264 Measuring Your (Agentic) SOC: Two Security Leaders Walk into a Podcast All SOC and SIEM episodes

Episode co-host: Marina Kaganovich, Enterprise Trust Lead, Office of the CISO, Google Cloud Guest: James Sherer, Partner at BakerHostetler Topics Is AI just an emerging technology or something bigger, deeper and different? Is this another emerging technology or a fundamental shift? How to effectively govern something that is rapidly changing at unprecedented velocity? We navigated the governance of the Internet and SaaS. What makes AI governance fundamentally different from the "Classic IT" or Data Governance models of the past? As we move toward Agentic AI, the line between tool and teammate blurs. Should we be governing AI agents through the lens of Technical Controls or Human Resources and behavioral contracts? What if we hand even more responsibility to AI? Where are the tipping points as we shift from assistance to autonomy? How to avoid unintended, negative consequences when setting policy, contrasting risk-based vs. rights-based regulation and regulatory expectations Give us some practical takeaways for a defensible AI program - if an organization had to defend its AI program to a regulator or a judge tomorrow? Related episodes: Video version EP235 The Autonomous Frontier: Governing AI Agents from Code to Courtroom EP161 Cloud Compliance: A Lawyer - Turned Technologist! - Perspective on Navigating the Cloud EP237 Making Security Personal at the Speed and Scale of TikTok

Guests: Alexander Pabst, Global Deputy CISO, Allianz SE Michael Sinno, Director of D&R, Google Topics: We've spent decades obsessed with MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond). As AI agents begin to handle the bulk of triage at machine speed, do these metrics become "vanity metrics"? If an AI resolves an alert in seconds, does measuring the "mean" still tell us anything about the health of our security program, or should we be looking at "Time to Context" instead? You mentioned the Maturity Triangle. Can you walk us through that framework? Specifically, how does AI change the balance between the three points of that triangle—is it shifting us from a "People-heavy" model to something more "Engineering-led," and where does the "Measurement" piece sit? Google is famous for its "Engineering-led" approach to D&R. How is Google currently measuring the success of its own internal D&R program? Specifically, how are you quantifying "Toil Reduction"? Are we measuring how many hours we saved, or are we measuring the complexity of the threats our humans are now free to hunt? Toil reduction is a laudable goal for the team members, what are the metrics we track and report up to document the overall improvement in D&R for Google's board? When you talk to your board about the success of AI in your security program, what are the 2 or 3 "Golden Metrics" that actually move the needle for them? How do you prove that an AI-driven SOC is actually better, not just faster? We often talk about AI as an "assistant," but we're moving toward Agentic SOCs. How should organizations measure the "unit economics" of their SOC? Should we be tracking the ratio of AI-handled vs. Human-handled incidents, and at what point does a high AI-handle rate become a risk rather than a success? Resources: Video version EP252 The Agentic SOC Reality: Governing AI Agents, Data Fidelity, and Measuring Success EP238 Google Lessons for Using AI Agents for Securing Our Enterprise EP91 "Hacking Google", Op Aurora and Insider Threat at Google EP236 Accelerated SIEM Journey: A SOC Leader's Playbook for Modernization and AI EP189 How Google Does Security Programs at Scale: CISO Insights EP75 How We Scale Detection and Response at Google: Automation, Metrics, Toil The SOC Metrics that Matter…or Do They? blog An Actual Complete List Of SOC Metrics (And Your Path To DIY) blog Achieving Autonomic Security Operations: Why metrics matter (but not how you think) blog

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: 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