AI Security Podcast

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

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

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.

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?

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)

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

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

1. https://blog.box.com/big-cybersecurity-bets-part1
2. https://blog.box.com/big-cybersecurity-bets-part-2
3. https://blog.box.com/big-security-bet-3-ai-redefines-vulnerability-management
4. https://blog.box.com/5-big-cybersecurity-bets-4-scaling-security-architecture-ai-first-world
5. https://blog.box.com/5-big-cybersecurity-bets-continuous-adversarial-validation
   

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

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

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

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

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

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

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

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

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

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 Bits

• Buttercup (Open Source Project)
• DARPA AI Cyber Challenge (AICC)
• Movie: The Princess Bride

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 Security

Resources discussed during the interview:

Anthropic - Threat Intelligence Report August 2025

What if the prompts used in your AI systems were treated as a new class of threat indicator? In this episode, Thomas Roccia, Senior Security Researcher at Microsoft, introduces the concept of the IOPC (Indicator of Prompt Compromise), sharing that "when there is a threat actors using a GenAI model for malicious activities, then the prompt... is considered as an IOPC".

The conversation dives deep into the practical application of AI in threat intelligence. Thomas shares details from his open-source projects, including NOVA, a tool for detecting adversarial prompts, and an AI agent he built to track the complex money laundering scheme from a $1.4 billion crypto hack . We also explore how AI is dramatically lowering the barrier to entry for complex tasks like reverse engineering, turning a once-niche skill into something accessible to a broader range of security professionals .

Questions asked:

(00:00) Introduction(02:20) Who is Thomas Roccia?(03:20) Using AI for Reverse Engineering & Malware Analysis(04:30) Building an AI Agent to Track Crypto Money Laundering(11:30) What is an IOPC (Indicator of Prompt Compromise)?(14:40) MITRE ATLAS: A TTP Framework for LLMs(18:20) NOVA: An Open-Source Tool for Detecting Malicious Prompts(23:15) Using RAG for Threat Intelligence on Data Leaks(31:00) Proximity: A New Scanner for Malicious MCP Servers(34:30) Why Good Ideas are Now More Valuable Than Execution(35:30) Real-World AI Threats: Stolen API Keys & Smart Malware(40:15) The Challenge of Building Reliable Multi-Agent Systems(48:20) How AI is Lowering the Barrier for Reverse Engineering(50:30) "Vibe Investigating": Assisting the SOC with AI(54:15) Caleb's Personal AI Agent for Document Organization

Resources discussed during the call:

NOVA- The Prompt Pattern Matching

DEF CON 33 Talk - Where’s My Crypto, Dude? The Ultimate Guide to Crypto Money Laundering

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 the current AI hype cycle different from the ones that failed before? How do you build a security program for technology that can't give the same answer twice? This episode features a deep-dive conversation with Damian Hasse, CISO of Moveworks and a security veteran from Amazon's Alexa team, VMware, and Microsoft.

Damian provides a practical blueprint for securing both traditional Machine Learning (ML) and modern Generative AI (GenAI). We discuss the common pitfalls of newly formed AI Councils, where members may lack the necessary ML background to make informed decisions. He shares his framework for assessing AI risk by focusing on the specific use case, the data involved, and building a multi-layered defense against threats like prompt injection and data leakage.

This is an essential guide for any security leader or practitioner tasked with navigating the complexities of AI security, from protecting intellectual property in AI-assisted coding to implementing safeguards for enterprise chatbots.

Questions asked:

(00:00) Introduction(02:31) Who is Damian Hasse? CISO at Moveworks(04:00) AI Security: The Difference Between the Pre-GPT and Post-GPT Eras(06:00) The Problem with New AI Councils Lacking ML Expertise(07:50) A History of AI: The Hype Cycles and Winters Since the 1950s(16:20) Is This AI Hype Cycle Different? The Power of Accessibility(20:25) Securing AI-Assisted Coding: IP Risks, Data Leakage, and Poisoned Models(23:30) The Threat of Indirect Prompt Injection in Open Source Packages(26:20) Are You Asking Your AI the Right Questions? The Power of "What Am I Missing?"(40:20) A CISO's Framework for Securing New AI Features(44:30) Building Practical Safeguards for Enterprise Chatbots(47:25) The Biggest Challenge in Real-Time AI Security: Performance(50:00) Why Access Control in AI is a Deterministic Problem

Resources spoken about during the interview

Tracing the thoughts of a large language model

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 Security

Resources

BSidesSF 2025 - AI's Bitter Lesson for SOCs: Let Machines Be Machines
BSidesSF 2025 - One Search To Rule Them All: Threat Modelling AI Search 

For more episodes like this go to www.aisecuritypodcast.com

Questions asked:

(00:00) Why Vibe Coding is a C-Suite Issue

(02:34) The Strategic Advantage of Hands-On AI

(04:20) Your AI Development Toolkit: Where to Start

(12:08 Choosing Your First Project: A Framework for Success

(16:46) The CISO as an AI Engineering Manager: A Step-by-Step Workflow

(31:32) A Surprising Security Finding: AI and Least Privilege

(36:47) Augmenting AI with Agents and Live Data

(38:50) Beyond Code: AI Agents for Business Automation (Zapier, etc.)

(43:30) The "Production Ready" Problem: Who Owns the Code?

(53:25) A CISO's Playbook for Governing AI Development

Resources spoken about during the episode:

AI Native Landscape - Tools

Cline

Roo-Code

Visual Studio Code

Windsurf

Bolt.new

Aider

v0 - Vercel

Lovable

Claude Code

ChatGPT

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 workflows

Questions 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 Tools

Resources spoken about during the episode:

AI Native Dev Community

Tessl

Cursor

Bolt

BASE44

Vercel

In this episode, we're joined by Leonard Tang, Co-founder and CEO of Haize Labs, to explore how AI red teaming is changing.

Leonard discusses the fundamental shifts in red teaming methodologies brought about by AI, common vulnerabilities he's observing in enterprise AI applications, and the emerging risks associated with multimodal AI (like voice and image processing systems). We delve into the intricacies of achieving precise output control for crafting sophisticated AI exploits, the challenges enterprises face in ensuring AI safety and reliability, and practical mitigation strategies they can implement.

Leonard shares his perspective on the future of AI red teaming, including the critical skills cybersecurity professionals will need to develop, the potential for fingerprinting AI models, and the ongoing discussion around protocols like MCP.

Questions asked:

• 00:00 Intro: AI Red Teaming's Evolution

• 01:50 Leonard Tang: Haize Labs & AI Expertise
• 05:06 AI vs. Traditional Red Teaming (Enterprise View)
• 06:18 AI Quality Assurance: The Haize Labs Perspective
• 08:50 AI Red Teaming: Real-World Application Examples
• 10:43 Major AI Risk: Multimodal Vulnerabilities Explained
• 11:50 AI Exploit Example: Voice Injections via Background Noise
• 15:41 AI Vulnerabilities & Early XSS: A Cybersecurity Analogy
• 20:10 Expert AI Hacking: Precisely Controlling AI Output for Exploits
• 21:45 The AI Fingerprinting Challenge: Identifying Chained Models
• 25:48 Fingerprinting LLMs: The Reality & Detection Difficulty
• 29:50 Top Enterprise AI Security Concerns: Reputation & Policy
• 34:08 Enterprise AI: Model Choices (Frontier Labs vs. Open Source)
• 34:55 Future of LLMs: Specialized Models & "Hot Swap" AI
• 37:43 MCP for AI: Enterprise Ready or Still Too Early?
• 44:50 AI Security: Mitigation with Precise Input/Output Classifiers
• 49:50 Future Skills for AI Red Teamers: Discrete Optimization

Resources discussed during the episode:

Baselines for Watermarking Large Language Models

Haize Labs

In this episode, we sit down with Joseph Thacker, a bug bounty hunter and AI security researcher, to uncover the evolving threat landscape of AI-powered applications and agents. Joseph shares battle-tested insights from real-world AI bug bounty programs, breaks down why AI AppSec is different from traditional AppSec, and reveals common vulnerabilities most companies miss, like markdown image exfiltration, XSS from LLM responses, and CSRF in chatbots.

He also discusses the rise of AI-driven pentesting agents ("hack bots"), their current limitations, and how augmented human hackers will likely outperform them, at least for now. If you're wondering whether AI can really secure or attack itself, or how AI is quietly reshaping the bug bounty and AppSec landscape, this episode is a must-listen.

Questions asked:

(00:00) Introduction

(02:14) A bit about Joseph

(03:57) What is AI AppSec?

(05:11) Components of AI AppSec

(08:20) Bug Bounty for AI Systems

(10:48) Common AI security issues

(15:09) How will AI change pentesting?

(20:23) How is the attacker landscape changing?

(22:33) Where would autimation add the most value?

(27:03) Is code being deployed less securely?

(32:56) AI Red Teaming

(39:21) MCP Security

(42:13) Evolution of pentest with AI

Resources shared during the interview:

- How to Hack AI Agents and Applications

- Critical Thinking Bug Bounty Podcast

- The Rise of AI Hackbots

- Shift - Caido Plugin

- Shadow Repeater

- Nuclei

- Haize Labs

- White Circle AI

- Prompt Injection Primer for Engineers

In this episode, hosts Caleb Sima and Ashish Rajan is joined by Adrian Ludwig, CISO at Tools For Humanity (World ID project), former Chief Trust Officer at Atlassian, and ex-Google security lead for Android. Together, they explore:

• Why digital identity is fundamentally broken and needs a major reboot
• The rise of AI-powered identity fraud and how it threatens security
• How World ID is using blockchain and biometrics to verify real humans
• The debate: Should we trust governments, companies, or decentralized systems with our identity?
• The impact of GenAI & deepfakes on authentication and online trust

Questions asked:

(00:00) Introduction

(03:55) Digital Identity in 2025

(14:13) How has AI impacted Identity?

(29:33) Trust and Transparency with AI

(32:18) Authentication and Identity

(49:53) What can people do today?

(52:05) Where can people learn about World Foundation?

(53:49) Adoption of new identity protocols

Resources spoken about during the episode:

Tools for Humanity

World.org

AI is evolving fast, and AI agents are the latest buzzword. But what exactly are they? Are they truly intelligent, or just automation in disguise? In this episode, Caleb Sima and Ashish Rajan spoke to Daniel Miessler—a cybersecurity veteran who is now deep into AI security research.

🎙️ In this episode, we cover:

✅ What AI agents really are (and what they’re NOT)

✅ How AI is shifting from searching to making decisions

✅ The biggest myths and misconceptions about AI automation

✅ Why most companies calling their tools “AI agents” are misleading you

✅ How AI agents will impact cybersecurity, business, and the future of work

✅ The security risks and opportunities no one is talking about

Questions asked:

(00:00) Introduction

(03:50) What are AI Agents?

(06:53) Use case for AI Agents

(14:39) Can AI Agents be used for security today?

(22:06) AI Agent’s impact on Attackers and Defenders in Cybersecurity

(37:05) AI Agents and Non Human Identities

(45:22) The big picture with AI Agents

(48:28) Transparency and Ethics for AI Agents

(58:36) Whats exciting about future of AI Agents?

(01:08:00) Would there still be value for foundational knowledge

AI is revolutionizing many things, but how does it impact detection engineering and SOC teams? In this episode, we sit down withDylan Williams, a cybersecurity practitioner with nearly a decade of experience in blue team operations and detection engineering. We speak about how AI is reshaping threat detection and response, the future role of detection engineers in an AI-driven world, can AI reduce false positives and speed up investigations, the difference between automation vs. agentic AI in security and practical AI tools you can use right now in detection & response

Questions asked:

(00:00) Introduction

(02:01) A bit about Dylan Williams

(04:05) Keeping with up AI advancements

(06:24) Detection with and without AI

(08:11) Would AI reduce the number of false positives?

(10:28) Does AI help identity what is a signal?

(14:18) The maturity of the current detection landscape

(17:01) Agentic AI vs Automation in Detection Engineering

(19:35) How prompt engineering is evolving with newer models?

(25:52) How AI is imapcting Detection Engineering today?

(36:23) LLM Models become the detector

(42:03) What will be the future of detection?

(47:58) What can detection engineers practically do with AI today?

(52:57) Favourite AI Tool and Final thoughts on Detection Engineering

Resources spoken about during the episode:

exa.ai - The search engine for AI

Building effective agents (Athropic’s blog different architecture and design patterns for agents)-https://www.anthropic.com/research/building-effective-agents -

Introducing Ambient Agents (LangChain’s blog on Ambient Agents) -https://blog.langchain.dev/introducing-ambient-agents/ -

Jared Atkinson’s Blog on Capability Abstraction -https://posts.specterops.io/capability-abstraction-fbeaeeb26384

LangGraph Studio -https://studio.langchain.com/

n8n -https://n8n.io/

Flowise -https://flowiseai.com/

CrewAI -https://www.crewai.com/

Welcome to 2025! In this episode our hosts Ashish Rajan and Caleb Sima, tackle the pressing question: What should your AI cybersecurity game plan look like this year?

The rapid evolution of agentic AI—where AI agents can perform tasks autonomously—is set to transform businesses, but it comes with unprecedented security challenges. From the resurgence of Identity and Access Management (IAM) to the urgent need for least privilege strategies, this episode captures actionable insights for CISOs and security leaders.

• What is agentic AI and how it may impact businesses?
• Top 3 priorities for building an effective AI security plan.
• The critical role of IAM and least privilege in managing AI agents.
• Real-world examples of how agentic AI will impact operations and security.
• Practical advice on incident response, monitoring, and preparing for AI-driven challenges.

Questions asked:

(00:00) Introduction

(01:59) The current state of AI in Enterprise

(10:22) Different Levels of Agentic AI

(12:05) CISO AI Cybersecurity Game plan for 2025

(15:57) IAM’s fire comeback

(23:11) Top 3 things for AI Cybersecurity Plan

Host Caleb Sima and Ashish Rajan caught up with experts Daniel Miessler (Unsupervised Learning), Joseph Thacker (Principal AI Engineer, AppOmni) to talk about the true vulnerabilities of AI applications, how prompt injection is evolving, new attack vectors through images, audio, and video and predictions for AI-powered hacking and its implications for enterprise security.

Whether you're a red teamer, a blue teamer, or simply curious about AI's impact on cybersecurity, this episode is packed with expert insights, practical advice, and future forecasts. Don’t miss out on understanding how attackers leverage AI to exploit vulnerabilities—and how defenders can stay ahead.

Questions asked:

(00:00) Introduction

(02:11) A bit about Daniel Miessler

(02:22) A bit about Rez0

(03:02) Intersection of Red Team and AI

(07:06) Is red teaming AI different?

(09:42) Humans or AI: Better at Prompt Injection?

(13:32) What is a security vulnerability for a LLM?

(14:55) Jailbreaking vs Prompt Injecting LLMs

(24:17) Whats new for Red Teaming with AI?

(25:58) Prompt injection in Multimodal Models

(27:50) How Vulnerable are AI Models?

(29:07) Is Prompt Injection the only real threat?

(31:01) Predictions on how prompt injection will be stored or used

(32:45) What’s changed in the Bug Bounty Toolkit?

(35:35) How would internal red teams change?

(36:53) What can enterprises do to protect themselves?

(41:43) Where to start in this space?

(47:53) What are our guests most excited about in AI?

Resources

Daniel's Webpage - Unsupervised Learning

Joseph's Website

In this jam-packed episode, with our panel we explored the current state and future of AI in the cybersecurity landscape. Hosts Caleb Sima and Ashish Rajan were joined by industry leaders Jason Clinton (CISO, Anthropic), Kristy Hornland (Cybersecurity Director, KPMG) and Vijay Bolina (CISO, Google DeepMind) to dive into the critical questions surrounding AI security. We’re at an inflection point where AI isn’t just augmenting cybersecurity—it’s fundamentally changing the game. From large language models to the use of AI in automating code writing and SOC operations, this episode examines the most significant challenges and opportunities in AI-driven cybersecurity. The experts discuss everything from the risks of AI writing insecure code to the future of multimodal models communicating with each other, raising important questions about trust, safety, and risk management. For anyone building a cybersecurity program in 2024 and beyond, you will find this conversation valuable as our panelist offer key insights into setting up resilient AI strategies, managing third-party risks, and navigating the complexities of deploying AI securely. Whether you're looking to stay ahead of AI's integration into everyday enterprise operations or explore advanced models, this episode provides the expert guidance you need

Questions asked:

(00:00) Introduction

(02:28) A bit about Kristy Hornland

(02:50) A bit about Jason Clinton

(03:08) A bit about Vijay Bolina

(04:04) What are frontier/foundational models?

(06:13) Open vs Closed Model

(08:02) Securing Multimodal models and inputs

(12:03) Business use cases for AI use

(13:34) Blindspots with AI Security

(27:19) What is RPA?

(27:47) AI’s talking to other AI’s

(32:31) Third Party Risk with AI

(38:42) Enterprise view of risk with AI

(40:30) CISOs want Visibility of AI Usage

(45:58) Third Party Risk Management for AI

(52:58) Starting point for AI in cybersecurity program

(01:02:00) What the panelists have found amazing about AI

What were the key AI Cybersecurity trends at ⁠BlackHat USA⁠? In this episode of the AI Cybersecurity Podcast, hosts ⁠Ashish Rajan⁠ and ⁠Caleb Sima⁠ dive into the key insights from Black Hat 2024. From the AI Summit to the CISO Summit, they explore the most critical themes shaping the cybersecurity landscape, including deepfakes, AI in cybersecurity tools, and automation. The episode also features discussions on the rising concerns among CISOs regarding AI platforms and what these mean for security leaders.

Questions asked:

(00:00) Introduction

(02:49) Black Hat, DEF CON and RSA Conference

(07:18) Black Hat CISO Summit and CISO Concerns

(11:14) Use Cases for AI in Cybersecurity

(21:16) Are people tired of AI?

(21:40) AI is mostly a side feature

(25:06) LLM Firewalls and Access Management

(28:16) The data security challenge in AI

(29:28) The trend with Deepfakes

(35:28) The trend of pentest automation

(38:48) The role of an AI Security Engineer

In this episode of the AI Cybersecurity Podcast, we dive deep into the latest findings from Google's DeepMind report on the misuse of generative AI. Hosts Ashish and Caleb explore over 200 real-world cases of AI misuse across critical sectors like healthcare, education, and public services. They discuss how AI tools are being used to create deepfakes, fake content, and more, often with minimal technical expertise. They analyze these threats from a CISO's perspective but also include an intriguing comparison between human analysis and AI-generated insights using tools like ChatGPT and Anthropic's Claude. From the rise of AI-powered impersonation to the manipulation of public opinion, this episode uncovers the real dangers posed by generative AI in today’s world.

Questions asked:

(00:00) Introduction

(03:39) Generative Multimodal Artificial Intelligence

(09:16) Introduction to the report

(17:07) Enterprise Compromise of GenAI systems

(20:23) Gen AI Systems Compromise

(27:11) Human vs Machine

Resources spoken about during the episode:

Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data

How much can we really trust AI-generated code more over Human generated Code today? How does AI-Generated code compare to Human generated code in 2024? Caleb and Ashish spoke to Guy Podjarny, Founder and CEO at Tessl about the evolving world of AI generated code, the current state and future trajectory of AI in software development. They discuss the reliability of AI-generated code compared to human-generated code, the potential security risks, and the necessary precautions organizations must take to safeguard their systems.

Guy has also recently launched his own podcast with Simon Maple called The AI Native Dev, which you can check out if you are interested in hearing more about the AI Native development space.

Questions asked:

(00:00) Introduction

(02:36) What is AI Generated Code?

(03:45) Should we trust AI Generated Code?

(14:34) The current usage of AI in Code Generated

(18:27) Securing AI Generated Code

(23:44) Reality of Security AI Generated Code Today

(30:22) The evolution of Security Testing

(37:36) Where to start with AI Security today?

(50:18) Evolution of the broader cybersecurity industry with AI

(54:03) The Positives of AI for Cybersecurity

(01:00:48) The startup Landscape around AI

(01:03:16) The future of AppSec

(01:05:53) The future of security with AI

Which AI Security Framework is right for you? As AI is gaining momentum, we are starting to see quite a few frameworks appearing but the question is, which one should you start with and can AI help you decide! Caleb and Ashish tackle this challenge head-on, comparing three major AI security frameworks: Databricks, NIST, and OWASP Top 10. They break down the key components of each framework, discuss practical implementation strategies, and provide actionable insights for CISOs and security leaders. They may have had some help along the way.

Questions asked:

(00:00) Introduction

(02:54) Databricks AI Security Framework (DASF)

(06: 38) Top 3 things from DASF by Claude 3

(07:32) Top 3 things from DASF by ChatGPT

(08:46) DASF Use Case Scenario

(11:01) Thoughts on DASF

(13:18) OWASP Top 10 for LLM Models

(20:12) Google's Secure AI Framework (SAIF)

(21:31) NIST AI Risk Management Framework

(25:18) Claude 3 summarises NIST RMF for 5 year old

(28:00) ChatGPT compares NIST RMF and NIST CSF

(28:48) How do the frameworks compare?

(36:46) Summary of all the frameworks

Resources from this episode:

Databricks AI Security Framework (DASF)

OWASP Top 10 for LLM

NIST AI Risk Management Framework

Google Secure AI Framework

What is the current state and future potential of AI Security? This special episode was recorded LIVE at BSidesSF (thats why its a little noisy), as we were amongst all the exciting action. Clint Gibler, Caleb Sima and Ashish Rajan sat down to talk about practical uses of AI today, how AI will transform security operations, if AI can be trusted to manage permissions and the importance of understanding AI's limitations and strengths.

Questions asked:

(00:00) Introduction

(02:24) A bit about Clint Gibler

(03:10) What top of mind with AI Security?

(04:13) tldr of Clint’s BSide SF Talk

(08:33) AI Summarisation of Technical Content

(09:47) Clint’s favourite part of the talk - Fuzzing

(15:30) Questions Clint got about his talk

(17:11) Human oversight and AI

(25:04) Perfection getting in the way of good

(30:15) AI on the engineering side

(36:31) Predictions for AI Security

Resources from this coversation:

Caleb's Keynote at BSides SF

Clint's Newsletter

Key AI Security takeaways from RSA Conference 2024, BSides SF 2024 and all the fringe activities that happen in SF during that week. Caleb and Ashish were speakers, panelists, participating in several events during that week and this episode captures all the highlights from all the conversations they had and they trends they saw during what they dubbed the "Cybersecurity Fringe Festival” in SF.

Questions asked:

(00:00) Introduction

(02:53) Caleb’s Keynote at BSides SF

(05:14) Clint Gibler’s Bsides SF Talk

(06:28) What are BSides Conferences?

(13:55) Cybersecurity Fringe Festival

(17:47) RSAC 2024 was busy

(19:05) AI Security at RSAC 2024

(23:03) RSAC Innovation Sandbox

(27:41) CSA AI Summit

(28:43) Interesting AI Talks at RSAC

(30:35) AI conversations at RSAC

(32:32) AI Native Security

(33:02) Data Leakage in AI Security

(30:35) Is AI Security all that different?

(39:26) How to filter vendors selling AI Solutions?

How can AI change a Security Analyst's workflow? Ashish and Caleb caught up with Ely Kahn, VP of Product at SentinelOne, to discuss the revolutionary impact of generative AI on cybersecurity. Ely spoke about the challenges and solutions in integrating AI into cybersecurity operations, highlighting how can simplify complex processes and empowering junior to mid-tier analysts.

Questions asked:

(00:00) Introduction

(03:27) A bit about Ely Kahn

(04:29) Current State of AI in Cybersecurity

(06:45) How AI could impact Cybersecurity User Workflow?

(08:37) What are some of the concerns with such a model?

(14:22) How does it compare to a analyst not using this model?

(21:41) Whats stopping models for going into autopilot?

(30:14) The reasoning for using multiple LLMs

(34:24) ChatGPT vs Anthropic vs Mistral

You can discover more about SentinelOne's Purple AI here!

How is AI transforming traditional approaches to offensive security, pentesting, security posture management, security assessment, and even code security? Caleb and Ashish spoke to Rob Ragan, Principal Technology Strategist at Bishop Fox about how AI is being implemented in the world of offensive security and what the right way is to threat model an LLM.

Questions asked:

(00:00) Introductions

(02:12) A bit about Rob Ragan

(03:33) AI in Security Assessment and Pentesting

(09:15) How is AI impacting pentesting?

(14:50 )Where to start with AI implementation in offensive Security?

(18:19) AI and Static Code Analysis

(21:57) Key components of LLM pentesting

(24:37) Testing whats inside a functional model?

(29:37) Whats the right way to threat model an LLM?

(33:52) Current State of Security Frameworks for LLMs

(43:04) Is AI changing how Red Teamers operate?

(44:46) A bit about Claude 3

(52:23) Where can you connect with Rob

Resources spoken about in this episode:

https://www.pentestmuse.ai/

https://github.com/AbstractEngine/pentest-muse-cli

https://docs.garak.ai/garak/

https://github.com/Azure/PyRIT

https://bishopfox.github.io/llm-testing-findings/

https://www.microsoft.com/en-us/research/project/autogen/

What is the current reality for AI automation in Cybersecurity? Caleb and Ashish spoke to Edward Wu, founder and CEO of Dropzone AI about the current capabilities and limitations of AI technologies, particularly large language models (LLMs), in the cybersecurity domain. From the challenges of achieving true automation to the nuanced process of training AI systems for cyber defense, Edward, Caleb and Ashish shared their insights into the complexities of implementing AI and the importance of precision in AI prompt engineering, the critical role of reference data in AI performance, and how cybersecurity professionals can leverage AI to amplify their defense capabilities without expanding their teams.

Questions asked:

(00:00) Introduction

(05:22) A bit about Edward Wu

(08:31) What is a LLM?

(11:36) Why have we not seen entreprise ready automation in cybersecurity?

(14:37) Distilling the AI noise in the vendor landscape

(18:02) Solving challenges with using AI in enterprise internally

(21:35) How to deal with GenAI Hallucinations?

(27:03) Protecting customer data from a RAG perspective

(29:12) Protecting your own data from being used to train models

(34:47) What skillset is required in team to build own cybersecurity LLMs?

(38:50) Learn how to prompt engineer effectively

There is a complex interplay between innovation and security in the age of GenAI. As the digital landscape evolves at an unprecedented pace, Daniel, Caleb and Ashish share their insights on the challenges and opportunities that come with integrating AI into cybersecurity strategies

Caleb challenges the current trajectory of safety mechanisms in technology and how overregulation may inhibit innovation and the advancement of AI's capabilities. Daniel Miessler, on the other hand, emphasizes the necessity of accepting technological inevitabilities and adapting to live in a world shaped by AI. Together, they explore the potential overreach in AI safety measures and discuss how companies can navigate the fine line between fostering innovation and ensuring security.

Questions asked:

(00:00) Introduction

(03:19) Maintaining Balance of Innovation and Security

(06:21) Uncensored LLM Models

(09:32) Key Considerations for Internal LLM Models

(12:23) Balance between Security and Innovation with GenAI

(16:03) Enterprise risk with GenAI

(25:53) How to address enterprise risk with GenAI?

(28:12) Threat Modelling LLM Models