AI Security Podcast

TechRiot.io

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.

  1. HACE 6 DÍAS

    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

    1 h

  2. 11 FEB

    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

    47 min

  3. 28 ENE

    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

    1 h y 1 min

  4. 23 ENE

    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

    51 min

  5. 19/12/2025

    AI Security 2025 Wrap: 9 Predictions Hit & The AI Bubble Burst of 2026

    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

    1 h y 3 min

  6. 10/12/2025

    AI Paywall for Browsers & The End of the Open Web?

    Cloudflare announced this year that AI bots must pay to crawl content. In this episode, Ashish Rajan and Caleb Sima dive deep into what this means for the future of the "open web" and why search engines as we know them might be dying . We explore Cloudflare's new model where websites can whitelist AI crawlers in exchange for payment, effectively putting a price tag on the world's information . Caleb spoke about the potential security implications, predicting a shift towards a web that requires strict identity and authentication for both humans and AI agents . The conversation also covers Cloudflare's new open-source browser, Ladybird, positioning itself as a competitor to the dominant Chromium engine . Is this the beginning of Web 3.0 where "information becomes currency"? Tune in to understand the massive shifts coming to browser security, AI agent identity, and the economics of the internet . Questions asked: (00:00) Introduction(01:55) Cloudflare's Announcement: Blocking AI Bots Unless They Pay (03:50) Why Search Engines Are Dying & The "Oracle" of AI (05:40) How the Payment Model Works: Bidding for Content Access (09:30) Will This Adoption Come from Enterprise or Bloggers?(11:45) Security Implications: The Web Requires Identity & Auth (13:50) Phase 2: Cloudflare's New Browser "Ladybird" vs. Chromium (19:00) Moving from B2B to Consumer: Paying Per Article via Browser (21:50) Managing AI Agent Identity: Who is Buying This Dinner? (23:20) Why Did We Switch to Chrome? (Performance vs. Memory) (27:00) Jony Ive & Sam Altman's AI Device: The Future Interface? (30:20) Google's Response: New Tools like "Opal" to Compete with n8n (33:15) The Controversy: Is This the End of the Free Open Web? (36:20) The New Economics of the Internet: Information as Currency Resources discussed during the interview: Cloudflare Just Changed How AI Crawlers Scrape the Internet-at-Large; Permission-Based Approach Makes Way for A New Business Model

    39 min

  7. 03/12/2025

    Build vs. Buy in AI Security: Why Internal Prototypes Fail & The Future of CodeMender

    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?

    51 min

  8. 06/11/2025

    Inside the 29.5 Million DARPA AI Cyber Challenge: How Autonomous Agents Find & Patch Vulns

    What does it take to build a fully autonomous AI system that can find, verify, and patch vulnerabilities in open-source software? Michael Brown, Principal Security Engineer at Trail of Bits, joins us to go behind the scenes of the 3-year DARPA AI Cyber Challenge (AICC), where his team's agent, "Buttercup," won second place. Michael, a self-proclaimed "AI skeptic," shares his surprise at how capable LLMs were at generating high-quality patches . However, he also shared the most critical lesson from the competition: "AI was actually the commodity" The real differentiator wasn't the AI model itself, but the "best of both worlds" approach, robust engineering, intelligent scaffolding, and using "AI where it's useful and conventional stuff where it's useful" . This is a great listen for any engineering or security team building AI solutions. We cover the multi-agent architecture of Buttercup, the real-world costs and the open-source future of this technology . Questions asked: (00:00) Introduction: The DARPA AI Hacking Challenge(03:00) Who is Michael Brown? (Trail of Bits AI/ML Research)(04:00) What is the DARPA AI Cyber Challenge (AICC)?(04:45) Why did the AICC take 3 years to run?(07:00) The AICC Finals: Trail of Bits takes 2nd place(07:45) The AICC Goal: Autonomously find AND patch open source(10:45) Competition Rules: No "virtual patching"(11:40) AICC Scoring: Finding vs. Patching(14:00) The competition was fully autonomous(14:40) The 3-month sprint to build Buttercup v1(15:45) The origin of the name "Buttercup" (The Princess Bride)(17:40) The original (and scrapped) concept for Buttercup(20:15) The critical difference: Finding vs. Verifying a vulnerability(26:30) LLMs were allowed, but were they the key?(28:10) Choosing LLMs: Using OpenAI for patching, Anthropic for fuzzing(30:30) What was the biggest surprise? (An AI skeptic is blown away)(32:45) Why the latest models weren't always better(35:30) The #1 lesson: The importance of high-quality engineering(39:10) Scaffolding vs. AI: What really won the competition?(40:30) Key Insight: AI was the commodity, engineering was the differentiator(41:40) The "Best of Both Worlds" approach (AI + conventional tools)(43:20) Pro Tip: Don't ask AI to "boil the ocean"(45:00) Buttercup's multi-agent architecture (Engineer, Security, QA)(47:30) Can you use Buttercup for your enterprise? (The $100k+ cost)(48:50) Buttercup is open source and runs on a laptop(51:30) The future of Buttercup: Connecting to OSS-Fuzz(52:45) How Buttercup compares to commercial tools (RunSybil, XBOW)(53:50) How the 1st place team (Team Atlanta) won(56:20) Where to find Michael Brown & Buttercup Resources discussed during the interview: Trail of BitsButtercup (Open Source Project)DARPA AI Cyber Challenge (AICC)Movie: The Princess Bride

    58 min
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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.

Información

  • Creador
    TechRiot.io
  • Años de actividad
    2023 - 2026
  • Episodios
    45
  • Clasificación
    Apto
  • Copyright
    © TechRiot.io
  • Mostrar sitio web
    AI Security Podcast

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