AI Security Podcast

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Oct 2026

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The #1 source for AI Security insights for CISOs and cybersecurity leaders. Hosted by two former CISOs, the AI Security Podcast provides expert, no-fluff discussions on the security of AI systems and the use of AI in Cybersecurity. Whether you're a CISO, security architect, engineer, or cyber leader, you'll find practical strategies, emerging risk analysis, and real-world implementations without the marketing noise. These conversations are helping cybersecurity leaders make informed decisions and lead with confidence in the age of AI.

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October 21, 2026Episode 2558 min

How to Build an AI Security SWAT Team

While attackers using AI can now operate at "machine speed," traditional enterprise security teams are often stuck moving at "meeting speed." To bridge this gap, organizations must fundamentally rethink how they structure their security operations, leverage AI for defensive scale, and eliminate the bureaucratic bottlenecks that delay critical patching and remediation. In this episode of the AI Security Podcast, Ashish and Caleb sit down to analyze the September 2026 Anthropic Threat Report and the underlying causes of recent high-profile AI sandbox escapes. Caleb explains how security leaders can fight AI with AI, sharing how he successfully used autonomous agents to conduct a 98% accurate least-privilege architectural audit across his entire organization. Caleb also speaks about the "barbell hiring" strategy eliminating middle management to build a highly agile, cross-functional Security SWAT team composed entirely of principal-level experts and junior engineers. Questions asked: (00:00) Introduction: The September 2026 Anthropic Threat Report(05:50) The Rapid Evolution of Offensive AI Capabilities(08:00) How AI Agents Connect Vulnerabilities Across Complex Kill Chains(12:00) Unpacking the Irregular Contractor Incident and Sandbox Escapes(14:30) The Legal Accountability Dilemma of Rogue AI Agents(17:30) The Core Security Bottleneck: Machine Speed vs. Meeting Speed(21:50) Using AI to Automate Enterprise Least Privilege Analysis(26:00) Rethinking Human-in-the-Loop for Automated Pull Requests(30:30) Why Legacy Point-in-Time Detection Fails Against Agent Swarms(36:30) Using Frontier Models to Build Zero-Defect Product Security(39:30) The Argument for Government-Backed AI Red Teaming(45:30) The "Negative Zero-Day Clock" and the Future of Exploit Generation(52:50) The Barbell Hiring Strategy: Eliminating Middle Management(55:00) Building a Cross-Functional AI Security SWAT Team Resources spoken about during the episode: Anthropic’s Detecting and countering misuse of AI: September 2026 How a small Israeli startup was linked to rogue AI hacks at OpenAI, Anthropic and Meta. Expanding Daybreak as the Cyber Defense Window Narrows Who’s legally to blame for Anthropic and OpenAI’s autonomous AI hacks? It’s complicated

October 14, 2026Episode 2447 min

How to Secure Egress for AI Workloads

As the speed of automated threats outpaces traditional patching cycles, organizations must prioritize structural containment over purely reactive detection. Cloud network architectures frequently default to permissive egress, meaning workloads can reach the public internet unless explicitly restricted. In this episode, Doug Merritt , CEO of Aviatri x and former CEO of Splunk, details how to build practical containment strategies for modern cloud and AI environments. He outlines how security teams can implement egress controls in monitor mode to validate traffic without risking system downtime. The discussion covers the transition from legacy IP-based firewalls to identity-based policies for ephemeral serverless and Kubernetes workloads. By prioritizing reachability and blast radius as core metrics, organizations can systematically reduce their attack surface and improve resilience. Questions asked: (00:00) Introduction to Containment and Network Security(02:50) Doug Merritt's Background and Journey from Splunk to Aviatrix(04:30) The Evolution from Prevention to Detection and Response(08:30) Why Detection Must Be Paired with Blast Radius Management(11:30) Implementing Cloud Egress Controls and Network Segmentation(15:00) Overcoming the Fear of System Downtime in Network Security(18:30) Applying the OSI Model to Modern Cloud and AI Workloads(23:30) Managing Default-Permissive Architectures in Public Clouds(26:30) Identity-Based Network Policies for Serverless and Kubernetes(31:00) The Reality of the Cloud Shared Responsibility Model(37:30) Using Monitor Mode for Safe Egress Implementation(40:00) Presenting Reachability and Blast Radius as Board-Level Metrics

October 7, 2026Episode 2347 min

How to Build an MCP Server to Inject Security Context into AI Agents

When non-technical employees can spin up fully functioning apps in days and AI agents are running parallel workflows that babysit their own pull requests, the traditional SDLC is dead. In this episode, Ashish sits down with Prahathess Rengasamy , Senior Security Engineer at SpaceX AI , to discuss how the staggering compression of development time requires an entirely new approach to security engineering. Prahathess explains why legacy 10-minute PR wait times are incompatible with multi-agent workflows, and how security teams can build internal Model Context Protocol (MCP) servers to inject compliance and security guardrails directly into an agent's context window. We unpack the differences between Q&A chatbots and autonomous coding agents, the critical need to preserve foundational preventative cloud controls (like AWS SCPs and GCP Org Policies), and why holding an autonomous machine accountable for management decisions is a critical architectural flaw. Finally, Prahathess breaks down the "build vs. buy" debate, revealing how small security teams can leverage AI to rapidly deploy internal platforms and paved roads that previously took months to engineer. Questions asked: (00:00) Introduction: Compressing the Software Development Life Cycle (SDLC)(01:50) Prahathess Rengasamy’s Background (Block, Apple, SpaceX AI)(02:50) How Non-Technical Employees Are Bypassing Traditional CI/CD Pipelines(07:30) Injecting Security Context into Agents Using MCP (Model Context Protocol)(10:30) The Anti-Pattern: Abandoning Preventative Cloud Controls for AI Buzzwords(12:20) Identity and Accountability: Why Machines Shouldn't Make Management Decisions(15:30) Why Legacy Pull Requests Are Too Slow for Multi-Agent Workflows(18:20) Moving Security Left: Giving Agents Feedback During Local Execution(22:30) The "Build vs. Buy" Shift: Security Teams Deploying Their Own AI Paved Roads(25:40) Killing the Excel Sheet: Building AI Bots to Explain 403 Errors to Developers(29:00) How to Evaluate AI Workflows Without a Deep Technical Background(32:00) Securing Complex Agent Infrastructure and Maintaining Egress Controls(36:00) Treating AI Agents Like Microservices to Control Sprawl(38:30) Coding Agents vs. Q&A Chatbots: Defining the Threat Model(42:30) Applying Inward Security Policies to the SOC and Threat Hunting Teams Resources discussed during the interview: fwd:cloudsec talk - Agentic Paved Roads: Shifting Security Left to the Machine That Thinks

October 2, 2026Episode 2255 min

How Visa Secures Trillions Using AI Agents & Open-Source Harnesses

How does a global financial engine moving trillions annually manage cyber risk in the age of AI? In this episode, Ashish sits down with Subra Kumaraswamy , CISO of Visa . Subra discusses Visa's journey as part of Anthropic's Glasswing Project testing the Mythos model, revealing why a majority of Mythos-discovered vulnerabilities were non-exploitable due to robust zero-trust architecture and micro-segmentation. Subra explains why Visa open-sourced VVAH (Visa Vulnerability Agentic Harness) to help security teams scale vulnerability discovery, prioritization, and remediation across any model. He also breaks down how Visa triages 98% of Level 1 SOC incidents with AI agents, why "Mean Time to Adapt" (MTTA) is replacing legacy patch timelines, and how cross-functional AI governance enables innovation while maintaining strict production controls (00:00) Introduction: Securing $18 Trillion in Global Transactions(01:50) Subra Kumaraswamy’s 30-Year Career: Netscape, Sun, and Visa(04:30) Improving Visa’s Cyber Maturity Score from 3.2 to 4.9(07:30) Inside Project Glasswing and Testing Anthropic’s Mythos(09:30) Why Visa Open-Sourced VVAH (Visa Vulnerability Agentic Harness)(13:30) Mythos vs. Zero Trust: Why Only 0.03% of Vulns Were Exploitable(16:30) Defining MTTA: Mean Time to Adapt at Machine Speed(19:00) The Threat of Multi-Service Vulnerability Chaining(24:00) Prioritizing Exploits and Automating PRs with Developer Agents(28:30) Moving to Autonomous Defense in High-Stakes Environments(33:00) AI Governance: Balancing Vibe-Coding with Production Gates(36:00) Collapsing Silos: Unifying Endpoint, Identity, and AppSec Signals(39:00) Building Custom Control Planes with Agent Harnesses(45:30) Triaging 98% of Level 1 Incidents with AI Agents(48:30) Hiring for CQ (Curiosity Quotient) & Developer Mindsets(53:00) The "You Laugh, You Lose" Cybersecurity Joke Challenge Resources spoken about during the episode: VVAH - Visa Vulnerability Agentic Harness

September 15, 2026Episode 2148 min

Black Hat 2026: Why Threat Researchers Are Hoarding Zero-Days

Is the AI "vulnpocalypse" already here? According to Casey Ellis , Founder of Bugcrowd and pioneer of Disclose.io , we aren't quite in an apocalypse yet, we're actually in a "slopdemic." The cost of discovering vulnerabilities has plummeted, flooding bug bounty and SOC triage queues with low-quality, noisy submissions. Because these queues are so overwhelmed, many highly skilled researchers are simply hoarding zero-days because reporting them has become too difficult. In this episode, Ashish sits down with Casey to unpack the major themes and mindset shifts from RSA and Black Hat 2026. Casey breaks down how AI is shrinking the OODA loop for defenders, forcing the industry to adopt a true "assume breach" mentality and reconsider deception technology to frustrate active adversaries. They also explore the risks of non-technical employees "vibe coding" corporate applications and why CISOs must get hands-on with AI tools at home if they want to understand the risks their workforce is taking. Questions asked: (00:00) Introduction to Offensive AI and Black Hat 2026(02:00) Casey Ellis’s Background (Bugcrowd, Disclose.io)(04:00) Major Themes from RSA and Black Hat 2026(09:00) The Impact of Mythos and Daybreak on Security Awareness(12:30) Why Threat Researchers Are Hoarding Zero-Days(14:00) The "Slopdemic" vs. The "Vulnpocalypse"(17:30) Managing Noisy Bug Bounty Queues and Risk Models(20:00) The Futility of Export Controls on Frontier Models(25:00) Point-and-Pwn vs. Building Complex Attack Graphs(29:30) The Defender’s Dilemma and the Shrinking OODA Loop(34:00) Shadow AI and the Risks of Non-Technical "Vibe Coding"(38:30) Why CISOs Need Hands-On Experience with AI Tools(45:30) The Resurgence of Deception Technology Resources spoken about during the episode: Casey's Blog

September 2, 2026Episode 2037 min

Why Prompt Filters Fail & How to Explain AI Risk to the Board | Cezary Piekarski, Standard Chartered.

Is the cybersecurity industry repeating the same mistakes with prompt injection that it made with buffer overflows decades ago? As attackers iterate through 50 to 60 prompt filter bypasses daily, attempting to artificially separate instruction from data is becoming a futile effort. In this episode, Ashish sits down with Cezary Piekarski , Group CISO of Standard Chartered . Cezary shares his techno-optimist view on how AI will ultimately benefit defenders, while also unpacking the hard realities of securing an enterprise that ingests 50-plus terabytes of observable data every single day. He explains why reactive security operations are dead, why User Behavior Analytics (UBA) often fails at scale due to stochastic human behavior, and why deception technology must be built directly into your ecosystem to actually work. Cezary shares his thoughts on executive communication, detailing a proven three-step framework for explaining complex AI risks to a board of directors without relying on fear-mongering. Finally, we explore why the tension between AI data hunger and user privacy is largely a "fake dilemma" for security teams. Questions asked: (00:00) Introduction: The Futility of Prompt Filters & AI Attack Evolutions(02:30) Cezary Piekarski’s Background and Role at Standard Chartered(03:30) What "Security as a Business Enabler" Actually Means(06:30) The Techno-Optimist View of AI in Cybersecurity(08:50) Why Reactive Security and Manual Triage Are Dead at 50TB/Day(11:30) Doing Deception Technology Right (No More "Surplus Bug" Buying)(15:20) The Flaws of UBA and Behavioral Anomaly Detection(22:30) The Buffer Overflow Analogy: Why Prompt Injection Needs an Architectural Fix(27:30) Under-Discussed Threats: Image-Based Prompt Injection & Data Poisoning(30:00) A 3-Step Masterclass for Explaining AI Risk to the Board(34:30) Why the AI Privacy vs. Security Debate is a "Fake Dilemma" Resources spoken about during the episode: Cyber security and fraud safety | Standard Chartered

August 27, 2026Episode 1945 min

Why 95% of AI Projects Fail: Model Risk & AI Governance | Sandip Wadje, BNP Paribas

Why do 95% of enterprise AI implementations fail? According to Sandip Wadje , Managing Director at BNP Paribas, many organizations attempt complex reasoning tasks on day one rather than building a mature foundation around data hygiene and simple summarization workflows. In this episode, Ashish sits down with Sandip to explore how global financial institutions navigate Model Risk Management (MRM), GenAI governance, and regulatory expectations across regions like the UK, EU, and US. Sandip breaks down why classical 20-year-old MRM frameworks fall short when applied to non-deterministic black-box LLMs, and why security leaders must focus on output drift and event taxonomies rather than just input prompt filtering. We also examine the concept of the "AI Kitchen" - a cross-functional governance model bringing together IT, CISOs, legal, and Data Protection Officers alongside practical strategies for calculating AI blast radius, cleaning up overprivileged non-human identity (NHI) permissions, and training CSIRT teams for ML SecOps incidents. Questions asked: (00:00) Introduction: AI Risk in Regulated Financial Institutions(01:50) Sandip Wadje’s Background at BNP Paribas(02:50) Classical Model Risk Management (MRM) vs. Generative AI(04:40) Governing the Black Box: Finding the Security Delta(08:00) The CMDB Problem: Building an Accurate AI Use Case Inventory(11:30) Why 95% of AI Projects Fail: Summarize, Write, Reason(15:00) Continuous Evaluation (Evals) and Catching Output Drift(18:50) Event Taxonomy: What Happens When AI Decisions Drift?(25:40) Training CSIRT and SOC Teams for ML SecOps Incidents(30:00) Compensating Controls: Remote Browser Isolation & Prompt Monitoring(34:30) Non-Human Identities (NHI) & Cleaning Birthright Permissions(36:30) Balancing a $1M Savings Against a 4% Revenue Fine(38:30) Open-Weight Models vs. Frontier LLMs in Financial Services(41:00) The "AI Kitchen": Cross-Functional AI Governance(44:30) The #1 Rule for AI Security: Understand Your Data First

August 20, 2026Episode 152 min

Why I Dont Trust Your AI Agent | Kane Narraway, Canva

With over 200 AI security vendors in the market, how does an enterprise CISO decide whether to build a custom solution, buy an off-the-shelf product, or just wait out the hype? In this episode of the AI Security Podcast, Ashish and Caleb are joined by Kane Narraway , Head of Enterprise Security at Canva, to debate the realities of AI security in modern enterprises. Kane breaks down why simply sandboxing AI agents doesn't work for workforce productivity, explaining that an overly restrictive sandbox renders an agent useless because it inherently needs access to external files and databases to do its job. We dive deep into the "Confused Deputy" problem, the struggle of granting granular least privilege to AI tools (like letting a bot summarize only Caleb's emails), and whether the old-school concept of network proxies is about to make a massive comeback as the ultimate control layer for AI routing and authorization. Finally, Kane shares why he believes the scariest near-future threat isn't malware, but contractors utilizing "Bring Your Own Agent" (BYOA) in enterprise environments. Questions asked: (00:00) Introduction to AI Agents in the Enterprise(01:50) Kane Narraway’s Background (Digital Forensics, Atlassian, Shopify, Canva)(02:50) The Build vs. Buy Debate in the Era of 200+ AI Security Vendors(09:00) Using Wrappers and Harnesses to Control Vendor APIs (Island Browser Example)(11:00) Why GitOps and PRs are Better for AI Configuration than MCP Deployments(13:00) The "Confused Deputy" Problem: Single-Player vs. Multi-Player AI Bots(16:50) How to Handle Agent Identity: "On Behalf Of" (OBO) vs. SPIFFE / NHI(22:50) Why Sandboxing AI Agents Fails for the General Workforce(28:20) Intent-Based Security and the Lack of Granular Access Controls(29:40) Are Proxies the Next Gen Firewall for AI Agents?(34:00) The Terrifying Future of "Bring Your Own Agent" (BYOA)(38:50) The "Gravel Road" Strategy for Managing Shadow IT and Vibe Coding(42:00) Dealing with Vendors Trying to Exploit Shadow IT Land Grabs(49:30) What Security Leaders are Over-Indexing On (Discovery vs. True Access)(50:40) The "You Laugh, You Lose" Cybersecurity Joke Challenge

July 23, 2026Episode 1751 min

Baiting the Bot: How to Use Deception to Stop Autonomous AI Agents

When AI agents start swarming your enterprise, they won't care about stealth. They will land a beachhead and instantly spawn 500 agents to crawl, probe, and exfiltrate data at machine speed. Is your detection stack ready? In this episode, Ashish and Caleb sit down with Andy Smith , CEO and co-founder of Tracebit , to completely rethink Deception Technology for the AI era. Forget the heavy, noisy "honeypots" of the 90s. We discuss the modern implementation of deception: lightweight, high-fidelity canary tokens (like fake AWS keys, Chrome cookies, and database tables) that act as guaranteed tripwires the moment an attacker, human or AI, assumes a breach. Andy shares new research on how you can actively weaponize an AI model's own safety guardrails against it. By embedding specific, controversial text strings (like references to biological warfare or sensitive political events) into decoy secrets. Questions asked: (00:00) Introduction to AI Deception(02:30) Andy Smith’s Background and the Founding of Tracebit(03:40) Deception 101: Honeypots vs. Canary Tokens(07:20) The "Assume Breach" Philosophy of Deception(10:00) Why CISOs Default to SIEMs over Quick Deception Wins(13:20) The Psychological Deterrent of Deception on Red Teams(15:10) Setting Up a Database Tripwire (Real-World Example)(17:40) Internal AI Threats: Catching Claude Code in a Production Kubernetes Pod(20:00) Why Deception Fails: The Lack of Strategy and Deployment Complexity(26:30) Using Cloud Serverless (S3/Terraform) to Deploy Deception for Free(28:00) Modern Lateral Movement: Chrome Cookies and Browser History Canaries(41:20) The Future of Attacks: Armies of Fast, Noisy AI Agents(44:50) Weaponizing AI Guardrails to Shut Down Attack Agents(48:20) Where to Start with Your Deception Strategy Today Resources spoken about during the episode: - Tracebit Research - Deception warns your teams at the speed of an AI attacker

June 26, 2026Episode 1651 min

Why AI Agents Are Forcing a Redesign of Application Security?

When the CEO of Anthropic declares that human coding will disappear within six months, followed quickly by the death of software engineering itself, what does that mean for the future of cybersecurity? In this episode, Ashish and Caleb break down the massive paradigm shift caused by AI coding assistants like Claude Code. Caleb shares his firsthand experience building and deploying software where he has never looked at a single line of the underlying code, arguing that while the need for security will never go away, the humans performing those roles very well might . We explore the illusion of AI prototyping why building a quick AI tool is easy, but maintaining it in production is a nightmare and dive deep into the "Build vs. Buy" debate . Caleb predicts an upcoming "forest fire" that will wipe out bloated security startups, forcing the market to consolidate around vendors with true, defensible moats based on network effects, hardware integration, or complex regulatory expertise Questions asked: (00:00) Introduction(02:50) The Anthropic CEO's Claim: Is Software Engineering Dead? (04:00) Separating Coding from Software Engineering (06:50) Managing Software Without Ever Looking at the Code (08:30) Will AI Eliminate the AppSec Team? (10:30) The Challenge of Legacy Code (COBOL on Mainframes) (15:10) Shifting Focus: From Code Analysis to Agentic Execution (18:00) The Coming "Forest Fire" in the Security Startup Landscape (21:00) The "Build vs. Buy" Illusion: Prototyping vs. Production (36:30) How to Build a Defensible Moat in AI Security (41:00) Why Hardware and Red Tape Are the Ultimate Moats (46:30) The AI Scaffolding Approach for Enterprises (47:50) Automating SIEM Detections Resources spoken about during the episode: World Economic Form - Davos 2026