Cloud Security Podcast by Google

Overview

Episodes

Details

Cloud Security Podcast by Google focuses on security in the cloud, delivering security from the cloud, and all things at the intersection of security and cloud. Of course, we will also cover what we are doing in Google Cloud to help keep our users' data safe and workloads secure. We're going to do our best to avoid security theater, and cut to the heart of real security questions and issues. Expect us to question threat models and ask if something is done for the data subject's benefit or just for organizational benefit. We hope you'll join us if you're interested in where technology overlaps with process and bumps up against organizational design. We're hoping to attract listeners who are happy to hear conventional wisdom questioned, and who are curious about what lessons we can and can't keep as the world moves from on-premises computing to cloud computing.

Recent Episodes

DEC 8, 2025

EP255 Separating Hype from Hazard: The Truth About Autonomous AI Hacking

Guest:

Heather Adkins, VP of Security Engineering, Google

Topic:

The term "AI Hacking Singularity" sounds like pure sci-fi, yet you and some other very credible folks are using it to describe an imminent threat. How much of this is hyperbole to shock the complacent, and how much is based on actual, observed capabilities today?
Can autonomous AI agents really achieve that "exploit - at - machine - velocity" without human intervention for the zero-day discovery phase?
On the other hand, why may it actually not happen?
When we talk about autonomous AI attack platforms, are we talking about highly resourced nation-states and top-tier criminal groups, or will this capability truly be accessible to the average threat actor within the next 6-12 months? What's the "Metasploit" equivalent for AI-powered exploitation that will be ubiquitous?
Can you paint a realistic picture of the worst-case scenario that autonomous AI hacking enables? Is it a complete breakdown of patch cycles, a global infrastructure collapse, or something worse?
If attackers are operating at "machine speed," the human defender is fundamentally outmatched. Is there a genuine "AI-to-AI" counter-tactic that doesn't just devolve into an infinite arms race? Or can we counter without AI at all?
Given that AI can expedite vulnerability discovery, how does this amplified threat vector impact the software supply chain? If a dependency is compromised within minutes of a new vulnerability being created, does this force the industry to completely abandon the open-source model, or does it demand a radical, real-time security scanning and patching system that only a handful of tech giants can afford?
Are current proposed regulations, like those focusing on model safety or disclosure, even targeting the right problem?
If the real danger is the combinatorial speed of autonomous attack agents, what simple, impactful policy change should world governments prioritize right now?

Resources:

29 MIN

DEC 1, 2025

EP254 Escaping 1990s Vulnerability Management: From Unauthenticated Scans to AI-Driven Mitigation

Guest:

Caleb Hoch, Consulting Manager on Security Transformation Team, Mandiant, Google Cloud

Topics:

How has vulnerability management (VM) evolved beyond basic scanning and reporting, and what are the biggest gaps between modern practices and what organizations are actually doing?
Why are so many organizations stuck with 1990s VM practices?
Why mitigation planning is still hard for so many?
Why do many organizations, including large ones, still rely on unauthenticated scans despite the known importance of authenticated scanning for accurate results?
What constitutes a "gold standard" vulnerability prioritization process in 2025 that moves beyond CVSS scores to incorporate threat intelligence, asset criticality, and other contextual factors?
What are the primary human and organizational challenges in vulnerability management, and how can issues like unclear governance, lack of accountability, and fear of system crashes be overcome?
How is AI impacting vulnerability management, and does the shift to cloud environments fundamentally change VM practices?

Resources:

31 MIN

NOV 24, 2025

EP253 The Craft of Cloud Bug Hunting: Writing Winning Reports and Secrets from a VRP Champion

Guests:

Sivanesh Ashok, bug bounty hunter
Sreeram KL, bug bounty hunter

Topics:

We hear from the Cloud VRP team that you write excellent bugbounty reports - is there any advice you'd give to other researchers when they write reports?
You are one of Cloud VRP's top researchers and won the MVH (most valuable hacker) award at their event in June - what do you think makes you so successful at finding issues?
What is a Bugswat?
What do you find most enjoyable and least enjoyable about the VRP?
What is the single best piece of advice you'd give an aspiring cloud bug hunter today?

Resources:

28 MIN

NOV 17, 2025

EP252 The Agentic SOC Reality: Governing AI Agents, Data Fidelity, and Measuring Success

Guests:

Alexander Pabst, Deputy Group CISO, Allianz
Lars Koenig, Global Head of D&R, Allianz

Topics:

Moving from traditional SIEM to an agentic SOC model, especially in a heavily regulated insurer, is a massive undertaking. What did the collaboration model with your vendor look like?
Agentic AI introduces a new layer of risk - that of unconstrained or unintended autonomous action. In the context of Allianz, how did you establish the governance framework for the SOC alert triage agents?
Where did you draw the line between fully automated action and the mandatory "human-in-the-loop" for investigation or response?
Agentic triage is only as good as the data it analyzes. From your perspective, what were the biggest challenges - and wins - in ensuring the data fidelity, freshness, and completeness in your SIEM to fuel reliable agent decisions?
We've been talking about SOC automation for years, but this agentic wave feels different. As a deputy CISO, what was your primary, non-negotiable goal for the agent? Was it purely Mean Time to Respond (MTTR) reduction, or was the bigger strategic prize to fundamentally re-skill and uplevel your Tier 2/3 analysts by removing the low-value alert noise?
As you built this out, were there any surprises along the way that left you shaking your head or laughing at the unexpected AI behaviors?
We felt a major lack of proof - Anton kept asking for pudding - that any of the agentic SOC vendors we saw at RSA had actually achieved anything beyond hype! When it comes to your org, how are you measuring agent success? What are the key metrics you are using right now?

Resources:

35 MIN

NOV 10, 2025

EP251 Beyond Fancy Scripts: Can AI Red Teaming Find Truly Novel Attacks?

Guest:

Ari Herbert-Voss, CEO at RunSybil

Topics:

The market already has Breach and Attack Simulation (BAS), for testing known TTPs. You're calling this 'AI-powered' red teaming. Is this just a fancy LLM stringing together known attacks, or is there a genuine agent here that can discover a truly novel attack path that a human hasn't scripted for it?
Let's talk about the 'so what?' problem. Pentest reports are famous for becoming shelf-ware. How do you turn a complex AI finding into an actionable ticket for a developer, and more importantly, how do you help a CISO decide which of the thousand 'criticals' to actually fix first?
You're asking customers to unleash a 'hacker AI' in their production environment. That's terrifying. What are the 'do no harm' guardrails? How do you guarantee your AI won't accidentally rm -rf a critical server or cause a denial of service while it's 'exploring'?
You mentioned the AI is particularly good at finding authentication bugs. Why that specific category? What's the secret sauce there, and what's the reaction from customers when you show them those types of flaws?
Is this AI meant to replace a human red teamer, or make them better? Does it automate the boring stuff so experts can focus on creative business logic attacks, or is the ultimate goal to automate the entire red team function away?
So, is this just about finding holes, or are you closing the loop for the blue team? Can the attack paths your AI finds be automatically translated into high-fidelity detection rules? Is the end goal a continuous purple team engine that's constantly training our defenses?
Also, what about fixing? What makes your findings more fixable?
What will happen to red team testing in 2-3 years if this technology gets better?

Resource:

25 MIN