AI Security Podcast

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 2026We 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 gunsQuestions 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

It's the season finale of the AI Security Podcast! Ashish Rajan and Caleb Sima look back at their 2025 predictions and reveal that they went 9 for 9. We wrap up the year by dissecting exactly what the industry got right (and wrong) about the trajectory of AI, providing a definitive "state of the union" for AI security.We analyze why SOC Automation became the undisputed king of real-world AI impact in 2025 , while mature AI production systems failed to materialize beyond narrow use cases due to skyrocketing costs and reliability issues . They also review the accuracy of their forecasts on the rise of AI Red Teaming , the continued overhyping of Agentic AI , and why Data Security emerged as a critical winner in a geo-locked world .Looking ahead to 2026, the conversation shifts to bold new predictions: the inevitable bursting of the "AI Bubble" as valuations detach from reality and the rise of self-fine-tuning models . We also explore the controversial idea that the "AI Engineer" is merely a rebrand for data scientists and a lot more…Questions asked:(00:00) Introduction: 2025 Season Wrap Up(02:50) State of AI Utility in late 2025: From coding to daily tasks(09:30) 2025 Report Card: Mature AI Production Systems? (Verdict: Correct)(10:45) The Cost Barrier: Why Production AI is Expensive(13:50) 2025 Report Card: SOC Automation is #1 (Verdict: Correct)(16:00) 2025 Report Card: The Rise of AI Red Teaming (Verdict: Correct)(17:20) 2025 Report Card: AI in the Browser & OS(21:00) Security Reality: Prompt Injection is still the #1 Risk(22:30) 2025 Report Card: Data Security is the Winner(24:45) 2025 Report Card: Geo-locking & Data Sovereignty(28:00) 2026 Outlook: Age Verification & Adult Content Models(33:00) 2025 Report Card: "Agentic AI" is Overhyped (Verdict: Correct)(39:50) 2025 Report Card: CISOs Should NOT Hire "AI Engineers" Yet(44:00) The "AI Engineer" is just a rebranded Data Scientist(46:40) 2026 Prediction: Self-Training & Self-Fine-Tuning Models(47:50) 2026 Prediction: The AI Bubble Will Burst(49:50) Bold Prediction: Will OpenAI Disappear?(01:01:20) Final Thoughts: Looking ahead to Season 4

Should you build your own AI security tools or buy from a vendor? In this episode, Ashish Rajan and Caleb Sima dive deep into the "Build vs. Buy" debate, sparked by Google DeepMind's release of CodeMender, an AI agent that autonomously finds, root-causes, and patches software vulnerabilities .While building an impressive AI prototype is easy, maintaining and scaling it into a production-grade security product is "very, very difficult" and often leads to failure after 18 months of hidden costs and consistency issues . We get into the incentives driving internal "AI sprawl," where security teams build tools just to secure budget and promotions, potentially fueling an AI bubble waiting to pop .We also discuss the "overhyped" state of AI security marketing, why nobody can articulate the specific risks of "agentic AI," and the future where third-party security products use AI to automatically personalize themselves to your environment, eliminating the need for manual tuning .Questions asked:(00:00) Introduction: The "Most Innovative" Episode Ever(01:40) DeepMind's CodeMender: Autonomously Finding & Patching Vulnerabilities(05:00) The "Build vs. Buy" Debate: Can You Just Slap an LLM on It?(06:50) The Prototype Trap: Why Internal AI Tools Fail at Scale(11:15) The "Data Lake" Argument: Can You Replace a SIEM with DIY AI?(14:30) Bank of America vs. Capital One: Are Banks Building AI Products?(18:30) The Failure of Traditional Threat Intel & Building Your Own(23:00) Perverse Incentives: Why Teams Build AI Tools for Promotions & Budget(26:30) The Coming AI Bubble Pop & The Fate of "AI Wrapper" Startups(31:30) AI Sprawl: Repeating the Mistakes of Cloud Adoption(33:15) The Frustration with "Agentic AI" Hype & Buzzwords(38:30) The Future: AI Platforms & Auto-Personalized Security Products(46:20) Secure Coding as a Black Box: The End of DevSecOps?

Anthropic's August 2025 AI Threat Intelligence report is out, and it paints a fascinating picture of how attackers are really using large language models like Claude Code. In this episode, Ashish Rajan and Caleb Sima dive deep into the 10 case studies, revealing a landscape where AI isn't necessarily creating brand new attack vectors, but is dramatically lowering the bar and professionalizing existing ones.The discussion covers shocking examples, from "biohacking" attacks using AI for sophisticated extortion strategies , to North Korean IT workers completely dependent on AI, simulating technical competence to successfully gain and maintain employment at Fortune 500 companies . We also explore how AI enables the rapid development of ransomware-as-a-service and malware with advanced evasion, even by actors lacking deep technical skills .This episode is essential for anyone wanting to understand the practical realities of AI threats today, the gaps in defense, and why the volume might still be low but the potential impact is significant.Questions asked:(00:00) Introduction: Anthropic's AI Threat Report(02:20) Case Study 1: Biohacking & AI-Powered Extortion Strategy(08:15) Case Study 2: North Korean IT Workers Simulating Competence with AI(12:45) The Identity Verification Problem & Potential Solutions(16:20) Case Study 3: AI-Developed Ransomware-as-a-Service (RaaS)(17:35) How AI Lowers the Bar for Malware Creation(20:25) The Gray Area: AI Safety vs. Legitimate Security Research(25:10) Why Defense & Enterprise Adoption of AI Security is Lagging(30:20) Case Studies 4-10 Overview (Fraud, Scams, Malware Distribution, Credential Harvesting)(35:50) Multi-Lingual Attacks: Language No Longer a Barrier(36:45) Case Study: Russian Actor's Rapid Malware Deployment via AI(43:10) Key Takeaways: Early Days, But Professionalizing Existing Threats(45:20) Takeaway 2: The Need for Enterprises to Leverage AI Defensively(50:45) The Gap: Security for AI vs. AI for SecurityResources discussed during the interview:Anthropic - Threat Intelligence Report August 2025

Welcome to the 2025 State of AI Security. This year, the conversation has moved beyond simple prompt injection to a far more complex threat: attacking the entire ecosystem surrounding the LLM. In this deep-dive discussion, offensive security experts Jason Haddix (Arcanum Information Security) and Daniel Miessler (Unsupervised Learning) break down the real-world attack vectors they're seeing in the wild.The conversation explores why prompt injection remains an unsolved problem and how the LLM is now being used as a delivery system to attack internal developers and connected applications. We also tackle the critical challenge of incident response, questioning how you can detect or investigate a malicious prompt when privacy regulations in some regions prevent logging and observability.This episode is a must-listen for anyone looking to understand the true offensive and defensive landscape of AI security, from the DARPA Cyber Challenge to the race for AI to control the browser.Questions asked:(00:00) Introduction(02:22) Who are Jason Haddix & Daniel Miessler?(03:40) The State of AI Security in 2025(06:20) It's All About the "Scaffolding", Not Just the Model(08:30) Why Prompt Injection is a Fundamental, Unsolved Problem(10:45) "Attacking the Ecosystem": Using the LLM as a Delivery System(12:45) The New Enterprise Protocol: Prompts in English(15:10) The Incident Response Dilemma: How Do You Detect Malicious Prompts?(16:50) The Challenge of Logging: When Privacy Laws Block Observability(21:30) Has Data Poisoning Become a Major Threat?(27:20) How Far Can Autonomous AI Go in Hacking Today?(28:30) An Inside Look at the DARPA AI Cyber Challenge (AIxCC)(40:45) Are Attackers Actually Using AI in the Wild?(47:30) The Evolution of the "Script Kitty" in the Age of AI(51:00) Would AGI Solve Security? The Problem of Politics & Context(59:15) Context is King: Why Prompt Engineering is a Critical Skill(01:03:30) What are the Best LLMs for Security & Productivity?(01:05:40) The Next Frontier: Why AI is Racing to Own the Browser(01:20:20) Does Using AI to Write Content Erode Trust?

Is generative AI a security team's greatest new weapon or its biggest new vulnerability? This episode dives headfirst into the debate with two leading experts on opposite sides of the AI dragon. We 1st published this episode on Cloud Security Podcast and because of the feedback we received from those diving into all things AI Security, we wanted to bring it to those who haven't probably had the chance to hear it yet on this podcast. On one side, discover how to leverage and "tame" AI for your defense. Jackie Bow explains how Anthropic uses its own powerful LLM, Claude, to revolutionize threat detection and response. Learn how AI can be used to:Build investigation and triage tools with incredible speed. Break free from the "black box" of traditional security tools, offering more visibility and control. Creatively "hallucinate" within set boundaries to uncover investigative paths a human might miss. Lower the barrier to entry for security professionals, enabling them to build prototypes and tools without deep coding expertise. On the other side, Kane Narraway provides a masterclass in threat modeling the new landscape of AI systems. He argues that while AI introduces new challenges, many are amplifications of existing SaaS risks. This conversation covers the critical aspects of securing AI, including:Why access, integrations, and authorization are the biggest risk factors in enterprise AI. How to approach threat modeling for both in-house and third-party AI tools. The security challenges of emerging standards like MCP (Meta-Controller Protocol) and the importance of securing the data AI tools can access. The critical need for security teams to adopt AI to keep pace with modern engineering departments. Questions asked:(00:00) Intro: Slaying or Training the AI Dragon at BSidesSF?(02:22) Meet Jackie Bow (Anthropic): Training AI for Security Defense(02:51) Meet Kane Narraway (Canva): Securing AI Systems & Facing Risks(03:49) Was Traditional Security Ops "Hot Garbage"? Setting the Scene(05:57) The Real Risks: What AI Brings to Your Organisation(06:53) AI in Action: Leveraging AI for Threat Detection & Response(07:46) AI Hallucinations: Bug, Feature, or Security Blind Spot?(08:55) Threat Modeling AI: The Core Challenges & Learnings(12:26) Getting Started: Practical AI Threat Detection First Steps(16:42) AI & Cloud: Integrating AI into Your Existing Environments(25:21) AI vs. Traditional: Is Threat Modeling Different Now?(28:34) Your First Step: Where to Begin with AI Threat Modeling?(31:59) Fun Questions & Final Thoughts on the Future of AI SecurityResourcesBSidesSF 2025 - AI's Bitter Lesson for SOCs: Let Machines Be MachinesBSidesSF 2025 - One Search To Rule Them All: Threat Modelling AI Search

In this episode, we welcome back Guy Podjarny, founder of Snyk and Tessl, to explore the evolution of AI-assisted coding. We dive deep into the three chapters of AI's impact on software development, from coding assistants to the rise of "vibe coding" and agentic development.Guy explains what "vibe coding" truly is, a term coined by Andrej Karpathy where developers delegate more control to AI, sometimes without even reviewing the code. We discuss how this opens the door for non-coders to create real applications but also introduces significant risks.Caleb, Ashish and Guy discuss:The Three Chapters of AI-Assisted Coding: The journey from simple code completion to full AI agent-driven development.Vibe Coding Explained: What is it, who is using it, and why it's best for "disposable apps" like prototypes or weekend projects.A New Security Threat - Slopsquatting: Discover how LLMs can invent fake library names that attackers can exploit, a risk potentially greater than typosquatting.The Future of Development: Why the focus is shifting from the code itself—which may become disposable—to the importance of detailed requirements and rigorous testing.The Developer as a Manager: How the role of an engineer is evolving into managing AI labor, defining specifications, and overseeing workflowsQuestions asked:(00:00) The Evolution of AI Coding Assistants(05:55) What is Vibe Coding?(08:45) The Dangers & Opportunities of Vibe Coding(11:50) From Vibe Coding to Enterprise-Ready AI Agents(16:25) Security Risk: What is "Slopsquatting"?(22:20) Are Old Security Problems Just Getting Bigger?(25:45) Cloud Sprawl vs. App Sprawl: The New Enterprise Challenge(33:50) The Future: Disposable Code, Permanent Requirements(40:20) Why AI Models Are Getting So Good at Understanding Your Codebase(44:50) The New Role of the AI-Native Developer: Spec & Workflow Manager(46:55) Final Thoughts & Favorite Coding ToolsResources spoken about during the episode:AI Native Dev CommunityTesslCursorBoltBASE44Vercel