AI Security Podcast

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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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S4 · E21
September 15 · 48 min
Black Hat 2026: Why Threat Researchers Are Hoarding Zero-Days
Is the AI "vulnpocalypse" already here? According to Casey Ellis, Founder of Bugcrowd and pioneer of Disclose.io, we aren't quite in an apocalypse yet, we're actually in a "slopdemic." The cost of discovering vulnerabilities has plummeted, flooding bug bounty and SOC triage queues with low-quality, noisy submissions. Because these queues are so overwhelmed, many highly skilled researchers are simply hoarding zero-days because reporting them has become too difficult. In this episode, Ashish sits down with Casey to unpack the major themes and mindset shifts from RSA and Black Hat 2026. Casey breaks down how AI is shrinking the OODA loop for defenders, forcing the industry to adopt a true "assume breach" mentality and reconsider deception technology to frustrate active adversaries. They also explore the risks of non-technical employees "vibe coding" corporate applications and why CISOs must get hands-on with AI tools at home if they want to understand the risks their workforce is taking. Questions asked: (00:00) Introduction to Offensive AI and Black Hat 2026(02:00) Casey Ellis’s Background (Bugcrowd, Disclose.io)(04:00) Major Themes from RSA and Black Hat 2026(09:00) The Impact of Mythos and Daybreak on Security Awareness(12:30) Why Threat Researchers Are Hoarding Zero-Days(14:00) The "Slopdemic" vs. The "Vulnpocalypse"(17:30) Managing Noisy Bug Bounty Queues and Risk Models(20:00) The Futility of Export Controls on Frontier Models(25:00) Point-and-Pwn vs. Building Complex Attack Graphs(29:30) The Defender’s Dilemma and the Shrinking OODA Loop(34:00) Shadow AI and the Risks of Non-Technical "Vibe Coding"(38:30) Why CISOs Need Hands-On Experience with AI Tools(45:30) The Resurgence of Deception Technology Resources spoken about during the episode: Casey's Blog
S4 · E20
September 2 · 37 min
Why Prompt Filters Fail & How to Explain AI Risk to the Board | Cezary Piekarski, Standard Chartered.
Is the cybersecurity industry repeating the same mistakes with prompt injection that it made with buffer overflows decades ago? As attackers iterate through 50 to 60 prompt filter bypasses daily, attempting to artificially separate instruction from data is becoming a futile effort. In this episode, Ashish sits down with Cezary Piekarski, Group CISO of Standard Chartered. Cezary shares his techno-optimist view on how AI will ultimately benefit defenders, while also unpacking the hard realities of securing an enterprise that ingests 50-plus terabytes of observable data every single day. He explains why reactive security operations are dead, why User Behavior Analytics (UBA) often fails at scale due to stochastic human behavior, and why deception technology must be built directly into your ecosystem to actually work. Cezary shares his thoughts on executive communication, detailing a proven three-step framework for explaining complex AI risks to a board of directors without relying on fear-mongering. Finally, we explore why the tension between AI data hunger and user privacy is largely a "fake dilemma" for security teams. Questions asked: (00:00) Introduction: The Futility of Prompt Filters & AI Attack Evolutions(02:30) Cezary Piekarski’s Background and Role at Standard Chartered(03:30) What "Security as a Business Enabler" Actually Means(06:30) The Techno-Optimist View of AI in Cybersecurity(08:50) Why Reactive Security and Manual Triage Are Dead at 50TB/Day(11:30) Doing Deception Technology Right (No More "Surplus Bug" Buying)(15:20) The Flaws of UBA and Behavioral Anomaly Detection(22:30) The Buffer Overflow Analogy: Why Prompt Injection Needs an Architectural Fix(27:30) Under-Discussed Threats: Image-Based Prompt Injection & Data Poisoning(30:00) A 3-Step Masterclass for Explaining AI Risk to the Board(34:30) Why the AI Privacy vs. Security Debate is a "Fake Dilemma" Resources spoken about during the episode: Cyber security and fraud safety | Standard Chartered
S4 · E19
August 27 · 45 min
Why 95% of AI Projects Fail: Model Risk & AI Governance | Sandip Wadje, BNP Paribas
Why do 95% of enterprise AI implementations fail? According to Sandip Wadje, Managing Director at BNP Paribas, many organizations attempt complex reasoning tasks on day one rather than building a mature foundation around data hygiene and simple summarization workflows. In this episode, Ashish sits down with Sandip to explore how global financial institutions navigate Model Risk Management (MRM), GenAI governance, and regulatory expectations across regions like the UK, EU, and US. Sandip breaks down why classical 20-year-old MRM frameworks fall short when applied to non-deterministic black-box LLMs, and why security leaders must focus on output drift and event taxonomies rather than just input prompt filtering. We also examine the concept of the "AI Kitchen" - a cross-functional governance model bringing together IT, CISOs, legal, and Data Protection Officers alongside practical strategies for calculating AI blast radius, cleaning up overprivileged non-human identity (NHI) permissions, and training CSIRT teams for ML SecOps incidents. Questions asked: (00:00) Introduction: AI Risk in Regulated Financial Institutions(01:50) Sandip Wadje’s Background at BNP Paribas(02:50) Classical Model Risk Management (MRM) vs. Generative AI(04:40) Governing the Black Box: Finding the Security Delta(08:00) The CMDB Problem: Building an Accurate AI Use Case Inventory(11:30) Why 95% of AI Projects Fail: Summarize, Write, Reason(15:00) Continuous Evaluation (Evals) and Catching Output Drift(18:50) Event Taxonomy: What Happens When AI Decisions Drift?(25:40) Training CSIRT and SOC Teams for ML SecOps Incidents(30:00) Compensating Controls: Remote Browser Isolation & Prompt Monitoring(34:30) Non-Human Identities (NHI) & Cleaning Birthright Permissions(36:30) Balancing a $1M Savings Against a 4% Revenue Fine(38:30) Open-Weight Models vs. Frontier LLMs in Financial Services(41:00) The "AI Kitchen": Cross-Functional AI Governance(44:30) The #1 Rule for AI Security: Understand Your Data First
S4 · E1
August 20 · 52 min
Why I Dont Trust Your AI Agent | Kane Narraway, Canva
With over 200 AI security vendors in the market, how does an enterprise CISO decide whether to build a custom solution, buy an off-the-shelf product, or just wait out the hype? In this episode of the AI Security Podcast, Ashish and Caleb are joined by Kane Narraway, Head of Enterprise Security at Canva, to debate the realities of AI security in modern enterprises. Kane breaks down why simply sandboxing AI agents doesn't work for workforce productivity, explaining that an overly restrictive sandbox renders an agent useless because it inherently needs access to external files and databases to do its job. We dive deep into the "Confused Deputy" problem, the struggle of granting granular least privilege to AI tools (like letting a bot summarize only Caleb's emails), and whether the old-school concept of network proxies is about to make a massive comeback as the ultimate control layer for AI routing and authorization. Finally, Kane shares why he believes the scariest near-future threat isn't malware, but contractors utilizing "Bring Your Own Agent" (BYOA) in enterprise environments. Questions asked: (00:00) Introduction to AI Agents in the Enterprise(01:50) Kane Narraway’s Background (Digital Forensics, Atlassian, Shopify, Canva)(02:50) The Build vs. Buy Debate in the Era of 200+ AI Security Vendors(09:00) Using Wrappers and Harnesses to Control Vendor APIs (Island Browser Example)(11:00) Why GitOps and PRs are Better for AI Configuration than MCP Deployments(13:00) The "Confused Deputy" Problem: Single-Player vs. Multi-Player AI Bots(16:50) How to Handle Agent Identity: "On Behalf Of" (OBO) vs. SPIFFE / NHI(22:50) Why Sandboxing AI Agents Fails for the General Workforce(28:20) Intent-Based Security and the Lack of Granular Access Controls(29:40) Are Proxies the Next Gen Firewall for AI Agents?(34:00) The Terrifying Future of "Bring Your Own Agent" (BYOA)(38:50) The "Gravel Road" Strategy for Managing Shadow IT and Vibe Coding(42:00) Dealing with Vendors Trying to Exploit Shadow IT Land Grabs(49:30) What Security Leaders are Over-Indexing On (Discovery vs. True Access)(50:40) The "You Laugh, You Lose" Cybersecurity Joke Challenge
S4 · E17
July 23 · 51 min
Baiting the Bot: How to Use Deception to Stop Autonomous AI Agents
When AI agents start swarming your enterprise, they won't care about stealth. They will land a beachhead and instantly spawn 500 agents to crawl, probe, and exfiltrate data at machine speed. Is your detection stack ready? In this episode, Ashish and Caleb sit down with Andy Smith, CEO and co-founder of Tracebit, to completely rethink Deception Technology for the AI era. Forget the heavy, noisy "honeypots" of the 90s. We discuss the modern implementation of deception: lightweight, high-fidelity canary tokens (like fake AWS keys, Chrome cookies, and database tables) that act as guaranteed tripwires the moment an attacker, human or AI, assumes a breach. Andy shares new research on how you can actively weaponize an AI model's own safety guardrails against it. By embedding specific, controversial text strings (like references to biological warfare or sensitive political events) into decoy secrets. Questions asked: (00:00) Introduction to AI Deception(02:30) Andy Smith’s Background and the Founding of Tracebit(03:40) Deception 101: Honeypots vs. Canary Tokens(07:20) The "Assume Breach" Philosophy of Deception(10:00) Why CISOs Default to SIEMs over Quick Deception Wins(13:20) The Psychological Deterrent of Deception on Red Teams(15:10) Setting Up a Database Tripwire (Real-World Example)(17:40) Internal AI Threats: Catching Claude Code in a Production Kubernetes Pod(20:00) Why Deception Fails: The Lack of Strategy and Deployment Complexity(26:30) Using Cloud Serverless (S3/Terraform) to Deploy Deception for Free(28:00) Modern Lateral Movement: Chrome Cookies and Browser History Canaries(41:20) The Future of Attacks: Armies of Fast, Noisy AI Agents(44:50) Weaponizing AI Guardrails to Shut Down Attack Agents(48:20) Where to Start with Your Deception Strategy Today Resources spoken about during the episode: - Tracebit Research - Deception warns your teams at the speed of an AI attacker
S4 · E16
June 26 · 51 min
Why AI Agents Are Forcing a Redesign of Application Security?
When the CEO of Anthropic declares that human coding will disappear within six months, followed quickly by the death of software engineering itself, what does that mean for the future of cybersecurity? In this episode, Ashish and Caleb break down the massive paradigm shift caused by AI coding assistants like Claude Code. Caleb shares his firsthand experience building and deploying software where he has never looked at a single line of the underlying code, arguing that while the need for security will never go away, the humans performing those roles very well might . We explore the illusion of AI prototyping why building a quick AI tool is easy, but maintaining it in production is a nightmare and dive deep into the "Build vs. Buy" debate . Caleb predicts an upcoming "forest fire" that will wipe out bloated security startups, forcing the market to consolidate around vendors with true, defensible moats based on network effects, hardware integration, or complex regulatory expertise Questions asked: (00:00) Introduction(02:50) The Anthropic CEO's Claim: Is Software Engineering Dead? (04:00) Separating Coding from Software Engineering (06:50) Managing Software Without Ever Looking at the Code (08:30) Will AI Eliminate the AppSec Team? (10:30) The Challenge of Legacy Code (COBOL on Mainframes) (15:10) Shifting Focus: From Code Analysis to Agentic Execution (18:00) The Coming "Forest Fire" in the Security Startup Landscape (21:00) The "Build vs. Buy" Illusion: Prototyping vs. Production (36:30) How to Build a Defensible Moat in AI Security (41:00) Why Hardware and Red Tape Are the Ultimate Moats (46:30) The AI Scaffolding Approach for Enterprises (47:50) Automating SIEM Detections Resources spoken about during the episode: World Economic Form - Davos 2026
S4 · E15
June 11 · 42 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
S4 · E14
June 4 · 47 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 Permissions Thank you to Oso for sponsoring this episode of AI Security Podcast.
S4 · E13
May 21 · 1 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?
S4 · E15
May 13 · 1 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)
S4 · E11
April 29 · 47 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
S4 · E9
April 22 · 46 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 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
S4 · E8
April 18 · 1 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 Reality Resources mentioned during the episode: Assessing Claude Mythos Preview’s cybersecurity capabilities Project Glasswing Zero Day Clock
S4 · E7
April 15 · 47 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
S4 · E6
April 2 · 57 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
S4 · E5
March 18 · 50 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 2026 BSidesSF 2026 Glean Zero Day Clock
S4 · E4
March 5 · 59 min
Will Foundation Models Kill Security Startups?
Did Anthropic just kill the AppSec industry? Following the announcement of Claude Code Security, a tool that finds, reasons about, and fixes code vulnerabilities, major security stocks dropped by 8% .In this episode of the AI Security Podcast, Ashish and Caleb break down the reality behind the hype. Caleb explains why using AI for SAST (Static Application Security Testing) is "a no-brainer," noting that many open-source projects and startups have already been doing exactly what Anthropic announced . We discuss why this actually validates the shift toward AI-automated remediation.The conversation goes deeper into the future of the cybersecurity market: Will giant foundation models start acquiring security companies? Will they offer "premium gas" (cheaper tokens) for building on their platforms? And most importantly, what does this mean for AppSec engineers whose jobs involve triaging false positives? Questions asked: (00:00) Introduction: The Claude Code Security Announcement(02:50) What is Claude Code Security? (Finding & Reasoning about VULNs) (03:50) Market Overreaction: Why Security Stocks Dropped 8% (05:10) Why AI-Powered SAST is Not New (OpenAI & Open Source doing it already) (07:20) Will AI Take AppSec Jobs? (Triaging False Positives) (09:00) "Shift Left" on Steroids: Auto-Fixing and PR Submission (11:30) The Threat to Legacy Vendors: Why CrowdStrike's Moat is Safe (14:30) Historical Context: AI is the New Calculator/Typewriter (18:20) The "Gasoline" Theory: Foundation Models as Fuel (21:00) Will Anthropic Acquire Security Startups? (26:30) Anthropic's Go-To-Market Strategy: Building AI SOCs (33:30) Startup Survival: Can Innovation Outpace Big Tech? (41:30) The Future of Threat Intel: Is the Legacy Moat Disappearing? (48:20) Negotiating with Vendors using AI Leverage (53:30) Using Evals for Organizational Anomaly Detection
S4 · E3
February 11 · 47 min
How to Build Your Own AI Chief of Staff with Claude Code
What if you could automate your entire work life with a personal AI Chief of Staff? In this episode, Caleb Sima reveals "Pepper," his custom-built AI agent to Ashish that manages emails, schedules meetings, and even hires other AI experts to solve problems for him . Using Claude Code and a "vibe coding" approach, Caleb built a multi-agent system over a single holiday weekend, without writing a single line of Rust code himself . We discuss how he used this same method to build a black-box testing agent that auto-files bugs on GitHub and even designed the branding for his venture fund, White Rabbit . We explore why "intelligence is becoming a commodity," and how you can survive by becoming an architect of AI agents rather than just a worker Questions asked: (00:00) Introduction(03:20) Meet "Pepper": Caleb's AI Chief of Staff (05:40) How Pepper Dynamically Hires "Expert" Agents (07:30) Pepper Builds its Own Tools (MCP Servers) (11:50) Do You Need to Be a Coder to Do This? (12:50) Using "Claude Superpowers" to Orchestrate Agents (16:50) Automating a Venture Fund: Branding White Rabbit with AI (20:50) Building a "Black Box" Testing Agent in Rust (Without Knowing Rust) (28:50) The Developer Who Went Skiing While AI Did His Job (32:20) The Coming "App Sprawl" Crisis in Enterprise Security (36:00) Security Risks: Managing Shared Memory & Context (41:20) The Future of Work: Is Intelligence Becoming a Commodity? (44:50) Why Plumbers are Safe from AI
S4 · E2
January 28 · 1 hr
AI Security 2026 Predictions: The "Zombie Tool" Crisis & The Rise of AI Platforms
This is a forward-looking episode, as Ashish Rajan and Caleb Sima break down the 8 critical predictions shaping the future of AI security in 2026 We explore the impending "Age of Zombies", a crisis where thousands of unmaintainable, "vibe-coded" internal tools begin to rot as employees churn . We also unpack controversial theory about the "circular economy" of token costs, suggesting that major providers are artificially keeping prices high to avoid a race to the bottom . The conversation dives deep into the shift from individual AI features to centralized AI Platforms , the reality of the Capability Plateau where models are getting "better but not different" , and the hilarious yet concerning story of Anthropic’s Claude not being able to operate a simple office vending machine without resorting to socialism or buying stun guns Questions asked: (00:00) Introduction: 2026 Predictions(02:50) Prediction 1: The Capability Plateau (Why models feel the same) (05:30) Consumer vs. Enterprise: Why OpenAI wins consumer, but Anthropic wins code (09:40) Prediction 2: The "Evil Conspiracy" of High AI Costs (12:50) Prediction 3: The Rise of the Centralized AI Platform Team (15:30) The "Free License" Trap: Microsoft Copilot & Enterprise fatigue (20:40) Prediction 4: Hyperscalers Shift from Features to Platforms (AWS Agents) (23:50) Prediction 5: Agent Hype vs. Reality (Netflix & Instagram examples) (27:00) Real-World Use Case: Auto-Fixing 1,000 Vulnerabilities in 2 Days (31:30) Prediction 6: Vibe Coding is Replacing Security Vendors (34:30) Prediction 7: Prompt Injection is Still the #1 Unsolved Threat (43:50) Prediction 8: The "Confused Deputy" Identity Problem (51:30) The "Zombie Tool" Crisis: Why Vibe Coded Tools will Rot (56:00) The Claude Vending Machine Failure: Why Operations are Harder than Code
S4 · E1
January 23 · 51 min
Why AI Agents Fail in Production: Governance, Trust & The "Undo" Button
Is your organization stuck in "read-only" mode with AI agents? You're not alone. In this episode, Dev Rishi (GM of AI at Rubrik, formerly CEO of Predibase) joins Ashish and Caleb to dissect why enterprise AI adoption is stalling at the experimentation phase and how to safely move to production . Dev reveals the three biggest fears holding IT leaders back: shadow agents, lack of real-time governance, and the inability to "undo" catastrophic mistakes . We dive deep into the concept of "Agent Rewind", a capability to roll back changes made by rogue AI agents, like deleting a production database and why this remediation layer is critical for trust . The conversation also explores the technical architecture needed for safe autonomous agents, including the debate between MCP (Model Context Protocol) and A2A (Agent to Agent) standards . Dev explains why traditional "anomaly detection" fails for AI and proposes a new model of AI-driven policy enforcement using small language models (SLMs) as judges . Questions asked: (00:00) Introduction(02:50) Who is Dev Rishi? From Predibase to Rubrik(04:00) The Shift from Fine-Tuning to Foundation Models (07:20) Enterprise AI Use Cases: Background Checks & Call Centers (11:30) The 4 Phases of AI Adoption: Where are most companies? (13:50) The 3 Biggest Fears of IT Leaders: Shadow Agents, Governance, & Undo (18:20) "Agent Rewind": How to Undo a Rogue Agent's Actions (23:00) Why Agents are Stuck in "Read-Only" Mode (27:40) Why Anomaly Detection Fails for AI Security (30:20) Using AI Judges (SLMs) for Real-Time Policy Enforcement (34:30) LLM Firewalls vs. Bespoke Policy Enforcement (44:00) Identity for Agents: Scoping Permissions & Tools (46:20) MCP vs. A2A: Which Protocol Wins? (48:40) Why A2A is Technically Superior but MCP Might Win

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