Guest:

• Craig H. Rowland, Founder and CEO, Sandfly Security

Topics:

• When it comes to Linux environments – spanning on-prem, cloud, and even–gasp–hybrid setups – where are you seeing the most significant blind spots for security teams today?
• There's sometimes a perception that Linux is inherently more secure or less of a malware target than Windows. Could you break down some of the fundamental differences in how malware behaves on Linux versus Windows, and why that matters for defenders in the cloud?
• 'Living off the Land' isn't a new concept, but on Linux, it feels like attackers have a particularly rich set of native tools at their disposal. What are some of the more subtly abused but legitimate Linux utilities you're seeing weaponized in cloud attacks, and how does that complicate detection?
• When you weigh agent-based versus agentless monitoring in cloud and containerized Linux environments, what are the operational trade-offs and outcome trade-offs security teams really need to consider?
• SSH keys are the de facto keys to the kingdom in many Linux environments. Beyond just 'use strong passphrases,' what are the critical, often overlooked, risks associated with SSH key management, credential theft, and subsequent lateral movement that you see plaguing organizations, especially at scale in the cloud?
• What are the biggest operational hurdles teams face when trying to conduct incident response effectively and rapidly across such a distributed Linux environment, and what's key to overcoming them?

Resources:

• EP194 Deep Dive into ADR - Application Detection and Response
• EP228 SIEM in 2025: Still Hard? Reimagining Detection at Cloud Scale and with More Pipelines

Guest:

• Dominik Swierad, Senior PM D&R AI and Sec-Gemini

Topics:

• When introducing AI agents to security teams at Google, what was your initial strategy to build trust and overcome the natural skepticism? Can you walk us through the very first conversations and the key concerns that were raised?
• With a vast array of applications, how did you identify and prioritize the initial use cases for AI agents within Google's enterprise security?
• What specific criteria made a use case a good candidate for early evaluation? Were there any surprising 'no-go' areas you discovered?"
• Beyond simple efficiency gains, what were the key metrics and qualitative feedback mechanisms you used to evaluate the success of the initial AI agent deployments?
• What were the most significant hurdles you faced in transitioning from successful pilots to broader adoption of AI agents?
• How do you manage the inherent risks of autonomous agents, such as potential for errors or adversarial manipulation, within a live and critical environment like Google's?
• How has the introduction of AI agents changed the day-to-day responsibilities and skill requirements for Google's security engineers?
• From your unique vantage point of deploying defensive AI agents, what are your biggest concerns about how threat actors will inevitably leverage similar technologies?

Resources:

• EP235 The Autonomous Frontier: Governing AI Agents from Code to Courtroom
• EP236 Accelerated SIEM Journey: A SOC Leader's Playbook for Modernization and AI
• EP224 Protecting the Learning Machines: From AI Agents to Provenance in MLSecOps
• EP227 AI-Native MDR: Betting on the Future of Security Operations?
• EP75 How We Scale Detection and Response at Google: Automation, Metrics, Toil

Guest:

• Kim Albarella, Global Head of Security, TikTok

Questions:

• Security is part of your DNA. In your day to day at TikTok, what are some tips you’d share with users about staying safe online?
• Many regulations were written with older technologies in mind. How do you bridge the gap between these legacy requirements and the realities of a modern, microservices-based tech stack like TikTok's, ensuring both compliance and agility?
• You have a background in compliance and risk management. How do you approach demonstrating the effectiveness of security controls, not just their existence, especially given the rapid pace of change in both technology and regulations?
• TikTok operates on a global scale, facing a complex web of varying regulations and user expectations. How do you balance the need for localized compliance with the desire for a consistent global security posture? How do you avoid creating a fragmented and overly complex system, and what role does automation play in this balancing act?
• What strategies and metrics do you use to ensure auditability and provide confidence to stakeholders?
• We understand you've used TikTok videos for security training. Can you elaborate on how you've fostered a strong security culture internally, especially in such a dynamic environment?
• What is in your TikTok feed?

Resources:

• Kim on TikTok @securishe and TikTopTips
• EP214 Reconciling the Impossible: Engineering Cloud Systems for Diverging Regulations
• EP161 Cloud Compliance: A Lawyer - Turned Technologist! - Perspective on Navigating the Cloud
• EP14 Making Compliance Cloud-native

Guest:

• Manija Poulatova, Director of Security Engineering and Operations at Lloyd's Banking Group

Topics:

• SIEM migration is hard, and it can take ages. Yours was - given the scale and the industry - on a relatively short side of 9 months. What’s been your experience so far with that and what could have gone faster?
• Anton might be a “reformed” analyst but I can’t resist asking a three legged stool question: of the people/process/technology aspects, which are the hardest for this transformation? What helped the most in solving your big challenges?
• Was there a process that people wanted to keep but it needed to go for the new tool?
• One thing we talked about was the plan to adopt composite alerting techniques and what we’ve been calling the “funnel model” for detection in Google SecOps. Could you share what that means and how your team is adopting?
• There are a lot of moving parts in a D&R journey from a process and tooling perspective, how did you structure your plan and why?
• It wouldn’t be our show in 2025 if I didn’t ask at least one AI question! What lessons do you have for other security leaders preparing their teams for the AI in SOC transition?

Resources:

• EP234 The SIEM Paradox: Logs, Lies, and Failing to Detect
• EP197 SIEM (Decoupled or Not), and Security Data Lakes: A Google SecOps Perspective
• EP231 Beyond the Buzzword: Practical Detection as Code in the Enterprise
• EP184 One Week SIEM Migration: Fact or Fiction?
• EP125 Will SIEM Ever Die: SIEM Lessons from the Past for the Future
• EP223 AI Addressable, Not AI Solvable: Reflections from RSA 2025
• “Maverick” — Scorched Earth SIEM Migration FTW! blog
• “Hack the box” site

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