AI Security Podcast

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Jun 2026

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The #1 source for AI Security insights for CISOs and cybersecurity leaders. Hosted by two former CISOs, the AI Security Podcast provides expert, no-fluff discussions on the security of AI systems and the use of AI in Cybersecurity. Whether you're a CISO, security architect, engineer, or cyber leader, you'll find practical strategies, emerging risk analysis, and real-world implementations without the marketing noise. These conversations are helping cybersecurity leaders make informed decisions and lead with confidence in the age of AI.

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June 11, 2026Episode 1542 min

Why Asset Intelligence is Replacing the CMDB & Static Dashboards

Why do CISOs still struggle with asset intelligence in 2026? Despite decades of security tooling, most organizations still have a massive 40% "dark matter" blind spot in their environment and the explosion of ephemeral AI agents is only making it worse.In this episode, Ashish and Caleb sit down with Joe Diamond, CEO, Axonius to discuss the evolution of the asset space. We explore why traditional CMDBs (which track business processes and IT hardware) fall short for cyber asset attack surface management (CAASM), and why the industry is shifting from static asset inventory to dynamic asset intelligence. Joe spoke about how AI agents whether they run for five minutes or five months must be treated as a distinct asset class, complete with their own access logs and token utilization tracking.The conversation also goes into the future of enterprise software interfaces. Joe predicts that within three to five years, the traditional dashboard UI will completely disappear, replaced entirely by natural language prompts and AI-driven BI. Finally, we tackle the "Build vs. Buy" dilemma: if AI can integrate tools in five minutes, why do we still need vendors?Questions asked:(00:00) Introduction(01:50) Joe Diamond's Background and Journey into Cybersecurity(02:50) Why Asset Management is Still an Unsolved Problem(04:00) The 40% "Dark Matter" Blind Spot in Enterprise Environments(05:30) How Do We Actually Define an Asset?(08:30) CMDB vs. Asset Intelligence: Understanding the Delta(12:30) Defining AI Models and AI Agents as an Asset Class(15:30) Do Ephemeral AI Agents Need to be Tracked?(18:30) The "Time Machine" Feature: Tracking Asset Configuration Drift(20:30) Use Case: Remediating the CrowdStrike Outage Using Asset Intelligence(23:30) Why You Need Asset Intelligence if You Already Have CSPM/CNAPP(31:30) The End of the UI: Why Dashboards Will Be Replaced by AI Prompts(36:30) A Simple 3-Question Framework for AI Asset Management(38:30) Build vs. Buy: Why AI Cannot Operate and Maintain Software

June 4, 2026Episode 1447 min

The AI AuthZ Problem: Why Human Least Privilege Fails for Autonomous Agents

Why are security leaders terrified of connecting AI agents to production data? Because unlike humans, AI agents don't apply judgment, and they operate at machine speed, meaning they can relentlessly hunt down production credentials and do catastrophic damage before a human analyst even blinks.In this episode, Ashish and Caleb sit down with Graham Neray, CEO of Oso, to tackle the massive, unsolved problem of AuthZ (Authorization) for autonomous AI. We explore why the industry's reliance on static, over-permissioned human identities is a recipe for disaster when applied to tools like Claude Code and Notion Agents. Graham explains the dangerous pitfalls of allowing agents to adopt the permissions of their human operators (privilege escalation), versus the complexity of assigning agents their own unique service accounts.The conversation dives deep into the fragmented agent security market. Should you deploy a browser extension, an endpoint sensor, or an edge proxy?. Learn why blocking destructive actions is a flawed approach (because agents need to destroy things to work), and why the future of AI AuthZ requires dynamic, data-level policies and continuous "human in the loop" validation.Questions asked:(00:00) Introduction(02:50) Graham Neray’s Background and the Mission of Oso(04:20) Why No One is Actually Building Their Own Agents(05:50) The Core Anxiety: Connecting AI to Production Data(07:20) Why Humans Have Judgment and Agents Don't(11:00) The Unsolved Crisis of Human Least Privilege(16:50) Agent Identities: Adopting User Permissions vs. Unique Service Accounts(18:20) Case Study: Privilege Escalation in Agent Alpha Testing(20:00) Background Agents and Unique Identities (Notion, Cursor, Perplexity)(22:30) Why You Need a Governance Plane Outside the AI Product(25:50) The False Promise of Blanket "No Destructive Actions" Policies(33:30) How to Deploy Agent Security: Browsers, Endpoints, and Proxies(38:30) Why No One Actually Uses the "Block" Feature in Security(41:50) The Context Problem: When is an RM-RF Command Good vs. Bad?(43:30) The Future of AuthZ: Resource and Data-Level Agent PermissionsThank you to Oso for sponsoring this episode of AI Security Podcast.

May 21, 2026Episode 131 hr 3 min

Securing AI at the Speed of Engineering | DoorDash | Forward Deployed Security | GRC Engineering

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?

May 13, 2026Episode 151 hr 10 min

Verification vs. Validation: How Autonomous AI is Changing Cybersecurity

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)

April 29, 2026Episode 1147 min

The Zero-Click AI Hack: How to Contain the Blast Radius of Autonomous Agents

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

April 22, 2026Episode 946 min

Buy vs. Build AI Security: Why [Box.com](http://Box.com) CISO is Creating their Own Agentic SOC

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 startupsQuestions 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 NewsletterHeather's post RSA blog5 Big AI Betshttps://blog.box.com/big-cybersecurity-bets-part1https://blog.box.com/big-cybersecurity-bets-part-2https://blog.box.com/big-security-bet-3-ai-redefines-vulnerability-managementhttps://blog.box.com/5-big-cybersecurity-bets-4-scaling-security-architecture-ai-first-worldhttps://blog.box.com/5-big-cybersecurity-bets-continuous-adversarial-validation

April 18, 2026Episode 81 hr 3 min

Anthropic's Project Mythos: Why the "Zero-Day Machine" is Terrifying the Security Industry

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 RealityResources mentioned during the episode:Assessing Claude Mythos Preview’s cybersecurity capabilitiesProject GlasswingZero Day Clock

April 15, 2026Episode 747 min

Are AI Security Startups Faking It? How to Separate Signal from Noise

With over 70 startups claiming to have built the perfect "AI SOC Analyst" or "AI Threat Hunter," how do you separate the real products from the vaporware? Recorded live at Decibel RSAC Founder Festival, Ashish and Caleb hosted a heated panel with Edward Wu (Founder & CEO, Dropzone AI) and Lou Manousos (Co-Founder & CEO, Ent AI). The group debates the controversial claim that AI can provide 100% threat prevention and exposes the dirty secret of the industry: Many AI startups are "cheating" by hiding human analysts behind their software.If you were a CISO or security practitioner navigating the vendor floor at RSA, this episode provides a BS-detector framework. Learn why an AI wrapper around Claude Code isn't enough, why "consistency" is the ultimate test for AI agents, and how to verify if a startup actually has real-world, paying enterprise deployments (and not just friendly design partners) .Questions asked:(00:00) Introduction: Live with Decibel(01:30) Meet the Panel: Edward Wu (Dropzone) & Lou Manousos (Ent) (03:40) The Great Debate: Has the Industry Given Up on Prevention? (05:50) What Has AI Actually Solved? (Repetitive Work vs. Context) (09:00) How to Spot BS on the RSA Show Floor (11:30) Defining an AI Agent: Chatbots vs. Threat Hunters (13:40) The Claude Code Problem: Is Your Product Just a Wrapper? (16:50) The 80% Accuracy Trap & Why Consistency is Key (21:30) Proving ROI: Evaluating AI Agents Like Human Employees (24:50) The Dirty Secret: Humans Hiding Behind AI Startups (26:30) Spotting Fake Customer Logos (28:30) Audience Q&A: Scaling the SOC vs. Replacing Humans (36:10) Forward Deployed Engineering & Personalized Software (40:30) Reimagining Security Architecture from the Inside Out (43:30) How Ent Detects Remote Workers Outsourcing Their Jobs (45:30) Final Thoughts: Asking Vendors for Real Proof Points

April 2, 2026Episode 657 min

How Lovable Manages 100+ Daily Changes, Vibe Coding & Shadow AI

What does it actually look like to run security inside one of Europe's fastest-growing AI companies? In this episode, recorded live at the Munich Cybersecurity Conference (MCSC), Ashish Rajan sat down with Igor Andriushchenko Head of Security at Lovable, the AI-native platform that lets anyone build and ship full applications without writing a line of code.Igor joined Lovable as employee #40. Six months later, the team had grown to 150+. Developers were running multi-agent workflows overnight, PMs were pushing pull requests, and the volume of code changes was hitting numbers that challenged every traditional security process they had. This is the security story nobody talks about in AI-native scale-ups and Igor lived it.In this episode, they cover: why your CI/CD pipeline is being load-tested to destruction by AI-generated churn · how to use PAM (Privileged Access Management) as a practical guardrail for AI agents that can't escalate to production secrets · why the allow-list vs deny-list logic is reversed for AI agents compared to traditional security · the overlooked SCA supply chain risk when AI recommends unmaintained or hallucinated packages · why old SAST tools are failing and what the new generation of agentic code scanners does differently · how to identify and manage advanced, intermediate, and basic AI users in your org without killing their productivity · and the practical "crawl, walk, run" approach to building internal AI security tooling that actually sticks.Igor also shares how Lovable's security team built an incident response AI skill, uses reachability analysis agents to triage SCA findings for enterprise customers, and why the real investment isn't in the AI model, it's in the skills ecosystem and data connections underneath.Questions asked:(00:00) Introduction: Securing the AI Workforce(03:50) Who is Igor Andriushchenko? (Head of Security, Lovable) (06:10) The Churn of Change: Why AI Will Break Your CI/CD (10:40) The FOMO Problem: Don't Force AI Adoption (11:50) The "Air Pocket" Strategy for Safe AI Experimentation (14:00) The Context Paradox: More Access = Dumber AI (17:40) Managing Agent Sprawl and "Advanced" Users (19:40) Why You Must Treat AI Agents Like Human Developers (PAM Controls) (22:30) The Need for AI Telemetry & Visibility (27:50) Blurring Roles: When PMs Become Developers (31:30) Why You Must Use "Deny Lists" Instead of "Allow Lists" for AI (34:30) AI SAST vs. Traditional SAST: Finding Business Logic Flaws (39:40) Supply Chain Risks: When AI Recommends Dead Libraries (45:40) Building Custom AI Skills for Incident Response (52:50) Fun Questions: Battlefield, Team Culture, and Comfort Food

March 18, 2026Episode 550 min

Questions Every CISO Must Ask AI Security Vendors

RSA Conference 2026 is here and the AI agent hype machine is louder than ever. In this episode, Ashish and Caleb cut through the noise and arm CISOs, practitioners, and security teams with a clear-eyed view of what's actually happening in AI security this year.From the vendor floor at RSAC to the future of internal security automation, Caleb and Ashish speak about why 70% of "AI agent security" vendors can't even define what an agent is, why security team consolidation around 2–3 major platforms (plus internal AI capability) may be the most underrated CISO strategy of 2026, and why the window from vulnerability disclosure to live exploitation has collapsed from months to under two days.They also explore the emerging idea of a centralised AI automation function inside security teams and why the future of security isn't buying more point solutions, it's building internal AI capability on top of a standardised vendor stack.Questions asked:(00:00) Introduction: Preparing for RSAC 2026(03:50) The Year of the "AI Agent" Marketing Hype (06:50) The Secret to AI Context: Enterprise Search (Glean) (09:50) Why Your SOC Needs a Centralized AI Platform Team (13:30) The #1 Question to Ask Vendors at RSAC: API Access (16:50) The Myth of MCP (Model Context Protocol) as the Gold Standard (20:50) Why RSAC is Too Noisy: Vibe Coding & 1,000 New Startups (22:30) Is Capital Raised the Only Signal of Trust? (24:50) Prediction: CISOs Will Fire 500 Vendors and Consolidate (30:50) The Build vs. Buy Debate for AI Security Features (35:50) Surviving RSAC: Sorting Signal from Noise (38:50) The Problem with "End-to-End" AI Agent Claims (41:50) Are AI-Driven Attacks Real? (44:50) The Zero-Day Clock: From 5 Months to 2 Days (48:50) RSAC Events: Live Recordings and CISO Panels Resources spoken about during the episode:RSAC 2026BSidesSF 2026Glean Zero Day Clock