Cloud Security Podcast by Google

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:
Daniel Lyman, VP of Threat Detection and Response, Fiserv
Topics:
What is the right way for people to bridge the gap and translate executive dreams and board goals into the reality of life on the ground?
How do we talk to people who think they have "transformed" their SOC simply by buying a better, shinier product (like a modern SIEM) while leaving their old processes intact?
What are the specific challenges and advantages you've seen with a federated SOC versus a centralized one? What does a "federated" or "sub-SOC" model actually mean in practice?
Why is the message that "EDR doesn't cover everything" so hard for some people to hear? Is this obsession with EDR a business decision or technology debt?
How do you expect AI to change the calculus around data centralization versus data federation?
What is your favorite example of telemetry that is useful, but usually excluded from a SIEM?
What are the Detection and Response organizational metrics that you think are most valuable?
Is the continued use of Excel an issue of tooling, laziness, or just because it is a fundamentally good way to interact with a small database?
Resources:
Video version
"In My Time of Dying" book
EP258 Why Your Security Strategy Needs an Immune System, Not a Fortress with Royal Hansen
EP197 SIEM (Decoupled or Not), and Security Data Lakes: A Google SecOps Perspective
The Gravity of Process: Why New Tech Never Fixes Broken Process and Can AI Change It? 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:
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