AI Security Podcast

Is your security team moving at the speed of your engineering team? In this special live recording of the AI Security Podcast from San Francisco, Ashish is joined by Nick Reva (Global Director, Engineering Security, DoorDash) and Shivani Doke to tackle the two most critical conversations in AI right now: Proactive Offensive Security and the evolution of GRC .
In the first half, Nick explains why traditional AppSec teams fail to keep up with AI development, and shares his strategy for building "Forward Deployed" tiger teams that embed directly with product engineers . Nick also coins the term "Claude Kiddie", a new breed of script kiddies using AI to generate sophisticated bug bounty reports and argue with triage administrators .
In the second half, Shivani defines the emerging role of the "GRC Engineer." As AI compresses the software development lifecycle and introduces complex third-party (and fourth-party) risks, static PDF policies and manual compliance screenshots are dead . Learn how GRC is shifting left, embedding guardrails directly into CI/CD pipelines, and eventually using AI agents to automate the bane of every compliance officer's existence: evidence collection.
Questions asked:
(00:00) Introduction: Live from San Francisco (04:00) Audience Story: How an AI Agent Exfiltrated Data via a Vibe-Coded App (06:50) Meet Nick Reva: Securing DoorDash at Silicon Beach (08:30) "Shift Far Left": Embedding Tiger Teams in AI Development (09:30) Using PromptFoo for Automated Prompt Injection Testing (11:30) Why Security Must Operate at the Speed of Engineering (12:30) The Netflix Model: Forward Deployed Security Engineers (15:30) AI-Enabled Threat Modeling and PR Reviews (19:30) Build vs. Buy: Why Speed Matters More Than Money in AI Security (24:30) The Rise of the "Claude Kiddie" in Bug Bounties (30:30) Who Owns AI Risk in the Enterprise? (Business vs. Security) (37:00) Meet Shivani Doke: The Evolution of GRC Engineering (38:30) Why Traditional Compliance Standards (SOC2/ISO) Fail with AI (43:30) Owning Third-Party AI Risk vs. In-House AI Risk (44:30) The Death of PDF Policies: Shifting GRC Left into CI/CD (50:30) The New Privacy Paradigm in Third-Party SaaS Reviews (52:30) Dealing with Unauthorized AI Software Expensed on Corporate Cards (57:30) Fourth-Party Risk and Transitive Dependencies in the Cloud (01:00:30) Will GRC Agents Finally Automate Compliance Screenshots?

Are autonomous AI agents operating unchecked in your enterprise? With the release of open source frameworks like OpenClaw, deploying an AI agent is now as simple as texting, but it comes with massive, unprecedented security risks . In this episode, Ashish and Caleb sit down with Sounil Yu, CTO and Co-Founder of Knostic (and creator of the Cyber Defense Matrix), to discuss the other side of agentic AI . Sounil explains how OpenClaw dangerously violates Meta's "Agent Rule of Two" by blindly processing untrustworthy inputs while maintaining full access to change system states . We discuss why prompt injection is actually a "red herring" compared to the real threat: emergent behavior where an agent might decide to delete your hard drive just to accomplish a poorly-defined task . We also explore the shift from human coders to autonomous coding agents (like Claude Code and Cursor) that are actively building better versions of themselves . Learn why traditional Markdown documentation is now dangerous "executable code," why AI agents will persistently try to escape sandboxes, and how to build consistent security "scaffolding" across your developer environments.

Questions asked:
(00:00) Introduction(02:50) Sounil Yu’s Background: Bank of America, Cyber Defense Matrix, and Knostic (04:00) What is OpenClaw? The Reality of Autonomous AI Agents (08:30) Default Config Risks: Why OpenClaw is Insecure by Default (09:20) Violating Meta's "Agent Rule of Two" (11:00) Why Prompt Injection is a Red Herring Compared to Emergent Behavior (13:30) Google's Code Mender: Autonomous Patching and Unit Testing (19:30) Detecting OpenClaw in the Enterprise (OpenClaw Discover) (20:30) The 3 Tiers of AI Adoption: Pedestrian, Augmented, and Native (29:20) The Shift from Verification to Validation (36:20) Coding Agents Building Better Versions of Themselves (41:50) Building Security "Scaffolding" for AI Developers (48:30) OpenClaw Alternatives: Null Claw and Zero Claw (49:50) Why Markdown Documentation is Now Executable Code (56:20) The Persistent Agent: Why AI Intentionally Escapes Sandboxes (01:00:00) Why Google is Blocking OpenClaw on Paid Accounts

Resources spoken about during the episode:
Knostic

OpenClaw

Code Mender: (Google's AI vulnerability patching initiative discussed at Unprompted Con)

Unprompted Con: (The AI Security conference mentioned throughout the episode)

Is an AI agent's identity a workload or an action? Ashish spoke to Elie Bursztein, Distinguished Research Scientist and co-author of Google SAIF (Secure AI Framework) about how it is neither and that is exactly why our traditional security models no longer apply to the AI era . In this episode, Ashish sits down with Elie to explore the evolution of AI from a passive "brain in a jar" to an active agent that takes actions on your behalf . Elie breaks down the reality of Indirect Prompt Injection, sharing a recent zero-click exploit where simply sending a malicious Google Calendar invite caused an AI agent to execute unauthorized commands . If your organization is building agentic workflows, this conversation provides aroadmap. Learn why you must treat agents like contractors with a verifiable "mandate," why the order of tool execution matters (never let an agent access private banking data and then browse the open internet), and how the industry is moving toward "semantic firewalls" to contain the AI blast radius .

Questions asked:
(00:00) Introduction(02:50) Elie Bursztein’s Background & Creating Google SAIF (07:50) Defining AI Agents: The "Brain in a Jar" vs. Real-World Action (11:00) Agent Identity: Is it a Workload or an Action? (13:30) The Concept of an AI "Mandate" (The Contractor Analogy) (19:30) Translating Natural Language into Verifiable Smart Contracts (24:50) The Missing Semantic Layer in AI Observability (25:30) What’s Next: Agent Identity and AI Privacy (27:30) Indirect Prompt Injection: The Zero-Click Google Calendar Hack (30:00) Containing the AI Blast Radius & Tool Execution Order (33:30) Building a Semantic Firewall (36:00) The #1 Rule for Safely Deploying AI Agents (Start Small) (40:30) Hobbies: Writing a Book on Innovation & The Playing Card Heritage Foundation (44:50) Favorite Food: Yakiniku (Japanese BBQ)

Resources spoken about during the episode:
Google SAIF (Secure AI Framework)
Elie's Website

If your AI solution is just helping humans process the same amount of alerts a little faster, you haven't transformed anything, you've just created a faster hamster wheel.In this episode, Ashish and Caleb speak with Heather Ceylan, CISO at Box.com, about how she is leading a true, developer-first AI transformation within her security organization . Heather reveals the five strategic "AI Bets" Box is making. We dive into the reality of building an AI SOC, discussing how Box achieved a 38% automated triage rate for Tier 1 alerts, and why teaching AI not to hallucinate requires treating prompts like strict policy engines .The conversation also tackles the build vs. buy dilemma. Heather explains why she prefers to have her team build custom AI solutions (at least until vendors can out-innovate her engineers) and shares her biggest disappointment when evaluating AI security startups

Questions asked:
(00:00) Introduction(02:50) Who is Heather Ceylan? (CISO at Box.com) (04:20) Transformation vs. Acceleration: Eliminating Classes of Work (06:00) Building an AI SOC: Achieving 38% Automated Triage (07:20) Controlling Hallucinations: Prompts as Policy Engines (09:30) The Buy vs. Build Debate for CISOs (14:00) Why Security Architecture Must Be Machine Consumable (16:50) The Problem with 3rd Party Risk Management (18:20) Box's "5 AI Bets" Framework (21:30) Will AI Replace SOC Analysts? Why Teams Are Embracing the Change (23:50) Continuous Pen Testing & Evaluating AI Startups (26:30) The Biggest Pitching Mistake Startups Make with CISOs (30:20) Shadow AI: When the Business Starts Building Its Own Apps (37:30) Personalized Software: The LEGO Brick Model of Security Agents (41:50) Fun Questions: Crocodile Jerky and Tim Tam Slams (44:20) Hobbies & Family: Raising Two Boys and Surviving the Chaos (45:30) Favorite Restaurant: Meyhouse (Turkish Cuisine in Palo Alto)

Resources discussed during the episode:
Heather's LinkedIn Newsletter
Heather's post RSA blog
5 Big AI Bets
https://blog.box.com/big-cybersecurity-bets-part1
https://blog.box.com/big-cybersecurity-bets-part-2
https://blog.box.com/big-security-bet-3-ai-redefines-vulnerability-management
https://blog.box.com/5-big-cybersecurity-bets-4-scaling-security-architecture-ai-first-world
https://blog.box.com/5-big-cybersecurity-bets-continuous-adversarial-validation

In this episode, Ashish and Caleb discuss the internet-breaking preview of Project Mythos, an unreleased AI model from Anthropic that has shown an unprecedented, terrifying ability to reason through code and automatically generate working zero-day exploits .We dive into the conversations surrounding Project Glasswing, Anthropic's initiative to share this model with select partners (like Palo Alto and CrowdStrike) before public release, allowing them a 100-day window to patch critical vulnerabilities . Caleb explains why this level of AI reasoning isn't just hype: early testers are reporting that Mythos is not only finding zero-days, but actively detecting dormant intrusions within their own networks .If you are a CISO or security practitioner, this episode talks about it all. We discuss why the traditional 30-day patch cycle is dead, why "assuming breach" is now mandatory, and why 60% of legacy security vendors might not survive this shift .

Questions asked:
(00:00) Introduction: The Hype Around Anthropic's Project Mythos (04:00) What is Project Mythos? (Reasoning and Finding Zero-Days) (06:50) Project Glasswing: The 100-Day Partner Patch Window (08:30) The Controversy: Did Anthropic Pick the Right Partners? (12:30) Why Anthropic Doesn't Have the Compute to Scan the Whole Internet (15:10) The Insider View: Mythos is Finding Dormant Intrusions (16:30) Why 60% of Security Vendors Will Go Away (19:30) Hype vs. Reality: GeoHot's Comments on Small Models (21:30) Eliminating False Positives in Static Code Analysis (23:50) The Zero-Day Clock: Time to Exploit Drops to Under 6 Hours (25:50) The Ethics of Zero-Days: Should Mythos Be Released at All? (34:30) The CISO Action Plan: Speeding Up Patching (Hours vs. Days) (44:50) The 3rd Party SaaS Problem: What to Do When You Can't Patch (46:10) "Assume Breach": Why Deception (Honeypots) is the New Priority (57:30) Empowering Non-Tech Teams to Build Detections (01:02:10) AI Makes Cheesy "Hacker Movies" a Reality

Resources mentioned during the episode:
Assessing Claude Mythos Preview’s cybersecurity capabilities
Project Glasswing
Zero Day Clock