Join your hosts, Anton Chuvakin and Timothy Peacock, as they talk with industry experts about some of the most interesting areas of cloud security. If you like having threat models questioned and a few bad puns, please tune in!
Harmonic Security focuses on securing generative AI in use. Can you walk us through a real, anonymized example of a data leak caused by employee AI usage that your platform has identified?
AI governance gets thrown around a lot. What does this mean in the context of Shadow AI? How should organizations be thinking about governing AI in light of upcoming AI regulations in the US and in the EU?
If we generally agree that employees are using AI tools before they are sanctioned, how can organizations control this? Network, API, endpoint?
Many organizations struggle with the "ban vs. embrace" debate for generative AI. Based on your experience, what's a compelling argument for moving from a blanket ban to a managed, secure adoption model? Can you share a success story where this approach demonstrably reduced risk?
The term "shadow AI" is often used interchangeably with "shadow IT" (but for AI-powered applications) but you've highlighted that AI is a different beast. What is the single biggest distinction between managing the risk of unsanctioned AI tools versus unsanctioned IT applications?
Looking forward, where do you see the biggest risks in the evolution of shadow AI? For instance, will the next threat be from highly specialized AI agents trained on proprietary data, or from the rapid proliferation of new, unmonitored open-source models?
Given the speed of change in this space, what's one piece of advice you'd give to a CISO today who is just beginning to get a handle on their organization's shadow AI problem?
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?
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?
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.
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?
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?
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?
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)?
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?
