Guest: 

• Anna Gressel, Partner at Paul, Weiss, one of the AI practice leads

Episode co-host:

• Marina Kaganovich, Office of the CISO, Google Cloud

Questions:

• Agentic AI and AI agents, with its promise of autonomous decision-making and learning capabilities, presents a unique set of risks across various domains. What are some of the key areas of concern for you?
• What frameworks are most relevant to the deployment of agentic AI, and where are the potential gaps? 
• What are you seeing in terms of how regulatory frameworks may need to be adapted to address the unique challenges posed by agentic AI?
• How about legal aspects - does traditional tort law or product liability apply?
• How does the autonomous nature of agentic AI challenge established legal concepts of liability and responsibility?
• The other related topic is knowing what agents “think” on the inside. So what are the key legal considerations for managing transparency and explainability in agentic AI decision-making?

Resources:

• Paul, Weiss Waking Up With AI (Apple, Spotify)
• Cloud CISO Perspectives: How Google secures AI Agents
• Securing the Future of Agentic AI: Governance, Cybersecurity, and Privacy Considerations

Guest:

• Svetla Yankova, Founder and CEO, Citreno

Topics:

• Why do so many organizations still collect logs yet don’t detect threats? In other words, why is our industry spending more money than ever on SIEM tooling and still not “winning” against Tier 1 ... or even Tier 5 adversaries? 
• What are the hardest parts about getting the right context into a SOC analyst’s face when they’re triaging and investigating an alert? Is it integration? SOAR playbook development? Data enrichment? All of the above?
• What are the organizational problems that keep organizations from getting the full benefit of the security operations tools they’re buying?
• Top SIEM mistakes? Is it trying to migrate too fast? Is it accepting a too slow migration? In other words, where are expectations tyrannical for customers? Have they changed much since 2015?
• Do you expect people to write their own detections? Detecting engineering seems popular with elite clients and nobody else, what can we do?
• Do you think AI will change how we SOC (Tim: “SOC” is not a verb?) in the next 1- 3 -5 years? 
• Do you think that AI SOC tech is repeating the mistakes SOAR vendors made 10 years ago? Are we making the same mistakes all over again? Are we making new mistakes? 

Resources:

• EP223 AI Addressable, Not AI Solvable: Reflections from RSA 2025
• EP231 Beyond the Buzzword: Practical Detection as Code in the Enterprise
• EP228 SIEM in 2025: Still Hard? Reimagining Detection at Cloud Scale and with More Pipelines
• EP202 Beyond Tiered SOCs: Detection as Code and the Rise of Response Engineering
• “RSA 2025: AI’s Promise vs. Security’s Past — A Reality Check” blog
• Citreno, The Backstory
• “Parenting Teens With Love And Logic” book (as a management book)
• “Security Correlation Then and Now: A Sad Truth About SIEM” blog (the classic from 2019)

Guest:

• Cristina Vintila, Product Security Engineering Manager, Google Cloud

Topic:

• Could you share insights into how Product Security Engineering approaches at Google have evolved, particularly in response to emerging threats (like Log4j in 2021)?
• You mentioned applying SRE best practices in detection and response, and overall in securing the Google Cloud products. How does Google balance high reliability and operational excellence with the needs of detection and response (D&R)? 
• How does Google decide which data sources and tools are most critical for effective D&R?
• How do we deal with high volumes of data?

Resources:

• EP215 Threat Modeling at Google: From Basics to AI-powered Magic
• EP117 Can a Small Team Adopt an Engineering-Centric Approach to Cybersecurity?
• Podcast episodes on how Google does security
• EP17 Modern Threat Detection at Google
• EP75 How We Scale Detection and Response at Google: Automation, Metrics, Toil
• Google SRE book
• Google SRS book

Guest:

• Sarah Aoun, Privacy Engineer, Google

Topic:

• You have had a fascinating career since we [Tim] graduated from college together – you mentioned before we met that you’ve consulted with a literal world leader on his personal digital security footprint. Maybe tell us how you got into this field of helping organizations treat sensitive information securely and how that led to helping keep targeted individuals secure? 
• You also work as a privacy engineer on Fuschia, Google’s new operating system kernel. How did you go from human rights and privacy to that? 
• What are the key privacy considerations when designing an operating system for “ambient computing”? How do you design privacy into something like that?
• More importantly, not only “how do you do it”, but how do you convince people that you did do it?
• When we talk about "higher risk" individuals, the definition can be broad. How can an average person or someone working in a seemingly less sensitive role better assess if they might be a higher-risk target? What are the subtle indicators?
• Thinking about the advice you give for personal security beyond passwords and multi-factor auth, how much of effective personal digital hygiene comes down to behavioral changes versus purely technical solutions?
• Given your deep understanding of both individual security needs and large-scale OS design, what's one thing you wish developers building cloud services or applications would fundamentally prioritize about user privacy?

Resources:

• Google privacy controls
• Advanced protection program

Guest:

• David French, Staff Adoption Engineer, Google Cloud

Topic:

• Detection as code is one of those meme phrases I hear a lot, but I’m not sure everyone means the same thing when they say it. Could you tell us what you mean by it, and what upside it has for organizations in your model of it?
• What gets better for security teams and security outcomes when you start managing in a DAC world? What is primary, actual code or using SWE-style process for detection work?
• Not every SIEM has a good set of APIs for this, right? What’s a team to do in a world of no or low API support for this model? 
• If we’re talking about as-code models, one of the important parts of regular software development is testing. How should teams think about testing their detection corpus? Where do we even start? Smoke tests? Unit tests? 
• You talk about a rule schema–you might also think of it in code terms as a standard interface on the detection objects–how should organizations think about standardizing this, and why should they?
• If we’re into a world of detection rules as code and detections as code, can we also think about alert handling via code? This is like SOAR but with more of a software engineering approach, right? 
• One more thing that stood out to me in your presentation was the call for sharing detection content. Is this between vendors, vendors and end users? 

Resources:

• Can We Have “Detection as Code”?
• Testing in Detection Engineering (Part 8)
• “So Good They Can't Ignore You: Why Skills Trump Passion in the Quest for Work You Love” book
• EP202 Beyond Tiered SOCs: Detection as Code and the Rise of Response Engineering
• EP181 Detection Engineering Deep Dive: From Career Paths to Scaling SOC Teams
• EP123 The Good, the Bad, and the Epic of Threat Detection at Scale with Panther
• Getting Started with Detection-as-Code and Google SecOps
• Detection Engineering Demystified: Building Custom Detections for GitHub Enterprise
• From soup to nuts: Building a Detection-as-Code pipeline
• David French - Medium Blog
• Detection Engineering Maturity Matrix

Guest:

• Daniel Fabian, Principal Digital Arsonist, Google

Topic:

• Your RSA talk highlights lessons learned from two years of AI red teaming at Google. Could you share one or two of the most surprising or counterintuitive findings you encountered during this process?
• What are some of the key differences or unique challenges you've observed when testing AI-powered applications compared to traditional software systems?
• Can you provide an example of a specific TTP that has proven effective against AI systems and discuss the implications for security teams looking to detect it?
• What practical advice would you give to organizations that are starting to incorporate AI red teaming into their security development lifecycle?
• What are some initial steps or resources you would recommend they explore to deepen their understanding of this evolving field?

Resources:

• Video (LinkedIn, YouTube)
• Google's AI Red Team: the ethical hackers making AI safer
• EP217 Red Teaming AI: Uncovering Surprises, Facing New Threats, and the Same Old Mistakes?
• EP150 Taming the AI Beast: Threat Modeling for Modern AI Systems with Gary McGraw
• EP198 GenAI Security: Unseen Attack Surfaces & AI Pentesting Lessons
• Lessons from AI Red Teaming – And How to Apply Them Proactively  [RSA 2025]

Guest:

• Alex Pinto,  Associate Director of Threat Intelligence, Verizon Business, Lead the Verizon Data Breach Report

Topics:

• How would you define “a cloud breach”? Is that a real (and different) thing? 
• Are cloud breaches just a result of leaked keys and creds?
• If customers are responsible for 99% of cloud security problems, is cloud breach really about a customer being breached?
• Are misconfigurations really responsible for so many cloud security breaches? How are we still failing at configuration?
• What parts of DBIR are not total “groundhog day”?
• Something about vuln exploitation vs credential abuse in today’s breaches–what’s driving the shifts we’re seeing? DBIR
• Are we at peak ransomware? Will ransomware be here in 20 years? Will we be here in 20 years talking about it?
• How is AI changing the breach report, other than putting in hilarious footnotes about how the report is for humans to read and and is written by actual humans? 

Resources:

• Video (LinkedIn, YouTube)
• Verizon DBIR 2025
• EP222 From Post-IR Lessons to Proactive Security: Deconstructing Mandiant M-Trends
• EP205 Cybersecurity Forecast 2025: Beyond the Hype and into the Reality
• EP112 Threat Horizons - How Google Does Threat Intelligence
• EP223 AI Addressable, Not AI Solvable: Reflections from RSA 2025

Guest

• Alan Braithwaite, Co-founder and CTO @ RunReveal

Topics:

• SIEM is hard, and many vendors have discovered this over the years. You need to get storage, security and integration complexity just right. You also need to be better than incumbents. How would you approach this now?
• Decoupled SIEM vs SIEM/EDR/XDR combo. These point in the opposite directions, which side do you think will win?
• In a world where data volumes are exploding, especially in cloud environments, you're building a SIEM with ClickHouse as its backend, focusing on both parsed and raw logs. What's the core advantage of this approach, and how does it address the limitations of traditional SIEMs in handling scale? 
• Cribl, Bindplane and “security pipeline vendors” are all the rage. Won’t it be logical to just include this into a modern SIEM?
• You're envisioning a 'Pipeline QL' that compiles to SQL, enabling 'detection in SQL.' This sounds like a significant shift, and perhaps not to the better? (Anton is horrified, for once) How does this approach affect detection engineering?
• With Sigma HQ support out-of-the-box, and the ability to convert SPL to Sigma, you're clearly aiming for interoperability. How crucial is this approach in your vision, and how do you see it benefiting the security community?
• What is SIEM in 2025 and beyond?  What’s the endgame for security telemetry data? Is this truly SIEM 3.0, 4.0 or whatever-oh?

Resources:

• EP197 SIEM (Decoupled or Not), and Security Data Lakes: A Google SecOps Perspective
• EP123 The Good, the Bad, and the Epic of Threat Detection at Scale with Panther
• EP190 Unraveling the Security Data Fabric: Need, Benefits, and Futures
• “20 Years of SIEM: Celebrating My Dubious Anniversary” blog
• “RSA 2025: AI’s Promise vs. Security’s Past — A Reality Check” blog
• tl;dr security newsletter
• Introducing a RunReveal Model Context Protocol Server!
• MCP: Building Your SecOps AI Ecosystem
• AI Runbooks for Google SecOps: Security Operations with Model Context Protocol

Guests:

• Eric Foster, CEO of Tenex.AI
• Venkata Koppaka, CTO of Tenex.AI

 Topics:

• Why is your AI-powered MDR special? Why start an MDR from scratch using AI?
• So why should users bet on an “AI-native” MDR instead of an MDR that has already got its act together and is now applying AI to an existing set of practices? 
• What’s the current breakdown in labor between your human SOC analysts vs your AI SOC agents? How do you expect this to evolve and how will that change your unit economics? 
• What tasks are humans uniquely good at today’s SOC? How do you expect that to change in the next 5 years?
• We hear concerns about SOC AI missing things –but we know humans miss things all the time too. So how do you manage buyer concerns about the AI agents missing things? 
• Let’s talk about how you’re helping customers measure your efficacy overall. What metrics should organizations prioritize when evaluating MDR? 

Resources:

• Video
• EP223 AI Addressable, Not AI Solvable: Reflections from RSA 2025 (quote from Eric in the title!)
• EP10 SIEM Modernization? Is That a Thing?
• Tenex.AI blog
• “RSA 2025: AI’s Promise vs. Security’s Past — A Reality Check” blog
• The original ASO 10X SOC paper that started it all (2021)
• “Baby ASO: A Minimal Viable Transformation for Your SOC” blog
• “The Return of the Baby ASO: Why SOCs Still Suck?” blog
• "Learn Modern SOC and D&R Practices Using Autonomic Security Operations (ASO) Principles" blog

Guest:

• Christine Sizemore, Cloud Security Architect, Google Cloud

 Topics:

• Can you describe the key components of an AI software supply chain, and how do they compare to those in a traditional software supply chain? 
• I hope folks listening have heard past episodes where we talked about poisoning training data. What are the other interesting and unexpected security challenges and threats associated with the AI software supply chain? 
• We like to say that history might not repeat itself but it does rhyme – what are the rhyming patterns in security practices people need to be aware of when it comes to securing their AI supply chains?
• We’ve talked a lot about technology and process–what are the organizational pitfalls to avoid when developing AI software? What organizational "smells" are associated with irresponsible AI development? 
• We are all hearing about agentic security – so can we just ask the AI to secure itself? 
• Top 3 things to do to secure AI software supply chain for a typical org? 

 Resources:

• Video
• “Securing AI Supply Chain: Like Software, Only Not” blog (and paper)
• “Securing the AI software supply chain” webcast
• EP210 Cloud Security Surprises: Real Stories, Real Lessons, Real "Oh No!" Moments
• Protect AI issue database
• “Staying on top of AI Developments” 
• “Office of the CISO 2024 Year in Review: AI Trust and Security”
• “Your Roadmap to Secure AI: A Recap” (2024)
• "RSA 2025: AI’s Promise vs. Security’s Past — A Reality Check" (references our "data as code" presentation)