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

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 [https://www.linkedin.com/in/ashishrajan/]and Caleb [https://www.linkedin.com/in/calebsima/] sit down with Andy Smith [https://www.linkedin.com/in/andy-m-smith/], CEO and co-founder of Tracebit [https://tracebit.com/], 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 [https://agentic.tracebit.com/]

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 [https://www.linkedin.com/in/ashishrajan/]and Caleb [https://www.linkedin.com/in/calebsima/] 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 [https://www.youtube.com/watch?v=02YLwsCKUww]

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 [https://www.linkedin.com/in/ashishrajan/]and Caleb [https://www.linkedin.com/in/calebsima/] sit down with Joe Diamond [https://www.linkedin.com/in/josephmdiamond/], CEO, Axonius [https://www.axonius.com/] 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

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 [https://www.linkedin.com/in/ashishrajan/] and Caleb [https://www.linkedin.com/in/calebsima/] sit down with Graham Neray [https://www.linkedin.com/in/grahamneray/], CEO of Oso, [https://www.osohq.com/] 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.

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 [https://www.linkedin.com/in/nickreva/](Global Director, Engineering Security, DoorDash) and Shivani Doke [https://www.linkedin.com/in/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?