AI Security Podcast

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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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30 episodes
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S4 · E25
Thursday · 46 min
How to Evaluate AI Security Vendors and Manages MCP Risks
As organizations rush to adopt AI security tools, many discover that while these agents work well within their own silos (EDR, CSPM, SCA), they fail to communicate across boundaries, leaving security teams blind to the true blast radius of an incident. Additionally, the widespread use of the Model Context Protocol (MCP) by both engineers and non-technical staff is creating massive credential sprawl, often leaving unencrypted, over-privileged credentials sitting in plain text config files. In this episode of the AI Security Podcast, Ashish speaks with Mohit Bansal, Senior Manager of Security Engineering at Webflow, about separating the signal from the noise when evaluating the explosive AI security vendor market. Mohit explains his framework for investing in structurally permanent solutions (like SCA) while waiting for the dust to settle on temporarily hard problems (like Agentic SOCs). The conversation dives deep into the realities of securing MCP calls, the need for open-source tools to redact secrets from agent memory, and why autonomous security still requires expert human knowledge to set blast radius boundaries. Questions asked: (00:00) Introduction to AI Vendor Evaluation and MCP Risks(02:50) How AI is Accelerating the Implementation Speed for Developers(05:30) Why Security Vendors Struggle to Identify the True Blast Radius(08:30) The Consolidation of the AI Security Vendor Market(12:30) Framework for Evaluating AI Vendors: Permanent vs. Temporary Problems(16:30) Managing Security Operations entirely through the Terminal via MCP(20:30) Why AI Will Not Eliminate the Need for Security Maintenance(23:30) Introducing "Context Cop": Webflow's Open Source Secret Redactor(27:30) How to Develop AI Autonomy Based on Engineering Seniority(32:30) The Danger of Blindly Trusting Non-Deterministic AI Output(35:30) Incident Response for AI: Visibility, Containment, and Tabletop Exercises(38:30) The Hidden Dangers of Plain Text Credentials in MCP Config Files(42:30) Advice for Security Teams with Limited Access to Frontier Models
S4 · E26
October 28 · 46 min
How to Harden AI Agents and Pave the Path for Vibe Coders
When traditionally non-technical employees ( "vibe coders") use LLMs to write and deploy code, the potential blast radius for misconfigurations, Internet exposed apps and incidents exponentially increases. Vibe coder direct agents operating probabilistically. Agents are overzealous and want to be helpful and will break security rules to get it done. Security teams cannot rely solely on paper policies and must start enforcing hardened, paved paths. In this episode of the AI Security Podcast, Ashish sits down with Nick Reva, Global Director, Engineering Security at DoorDash, to discuss how his team proactively secures AI agents and developer workflows. Nick breaks down the three primary AI archetypes within an organization- the AI champions, the professional software engineers, and vibe coders and outlines the specific guardrails needed for each. From pushing secure configuration files to local IDEs, hardening Claude.MD files to building safety belts that prevent agents from pushing code to non-enterprise remotes, Nick provides actionable strategies for securing the software development lifecycle at scale. Questions asked: (00:00) Introduction to Securing Vibe Coders and AI Agents(02:30) Nick Reva’s Background (SpaceX, Snapchat, DoorDash)(04:00) The Shift from Deterministic to Probabilistic Security Controls(05:30) The Dangers of Non-Technical Employees "Vibe Coding"(08:30) Paving the Path for Professional Software Engineers Using AI(11:30) Using Decorator Functions for Agent Authorization(15:30) Shifting Left: AI-Assisted Threat Modeling on Design Documents(18:30) Trusting AI: Using Ground Truth Data to Train PR Review Agents(21:30) Hardening Deployment Platforms (Vercel, Lovable) for Vibe Coders(25:00) The Importance of Claiming Enterprise Domains and Enforcing SSO(27:30) Developing "Safety Belts" to Prevent Code Pushes to Public Repos(31:30) AI for Security: Automating Incident Response Workflows(33:30) The Critical Role of Network Egress Containment in AI Incidents(39:30) Three Quick Wins for Implementing Security for AI(43:30) The "You Laugh, You Lose" Cybersecurity Joke Challenge Resources spoken about during the episode: DoorDash Engineering Blog
S4 · E25
October 21 · 58 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
S4 · E24
October 14 · 47 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 Aviatrix 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
S4 · E23
October 7 · 47 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
S4 · E22
October 2 · 55 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
S4 · E21
September 15 · 48 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
S4 · E20
September 2 · 37 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
S4 · E19
August 27 · 45 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
S4 · E1
August 20 · 52 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
S4 · E17
July 23 · 51 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
S4 · E16
June 26 · 51 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
S4 · E15
June 11 · 42 min
Why Asset Intelligence is Replacing the CMDB & Static Dashboards
Why do CISOs still struggle with asset intelligence in 2026? Despite decades of security tooling, most organizations still have a massive 40% "dark matter" blind spot in their environment and the explosion of ephemeral AI agents is only making it worse. In this episode, Ashish 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
S4 · E14
June 4 · 47 min
The AI AuthZ Problem: Why Human Least Privilege Fails for Autonomous Agents
Why are security leaders terrified of connecting AI agents to production data? Because unlike humans, AI agents don't apply judgment, and they operate at machine speed, meaning they can relentlessly hunt down production credentials and do catastrophic damage before a human analyst even blinks. In this episode, Ashish 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.
S4 · E13
May 21 · 1 hr 3 min
Securing AI at the Speed of Engineering | DoorDash | Forward Deployed Security | GRC Engineering
Is your security team moving at the speed of your engineering team? In this special live recording of the AI Security Podcast from San Francisco, Ashish is joined by Nick Reva (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?
S4 · E15
May 13 · 1 hr 10 min
Verification vs. Validation: How Autonomous AI is Changing Cybersecurity
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)
S4 · E11
April 29 · 47 min
The Zero-Click AI Hack: How to Contain the Blast Radius of Autonomous Agents
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
S4 · E9
April 22 · 46 min
Buy vs. Build AI Security: Why [Box.com](http://Box.com) CISO is Creating their Own Agentic SOC
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 https://blog.box.com/big-cybersecurity-bets-part1 https://blog.box.com/big-cybersecurity-bets-part-2 https://blog.box.com/big-security-bet-3-ai-redefines-vulnerability-management https://blog.box.com/5-big-cybersecurity-bets-4-scaling-security-architecture-ai-first-world https://blog.box.com/5-big-cybersecurity-bets-continuous-adversarial-validation
S4 · E8
April 18 · 1 hr 3 min
Anthropic's Project Mythos: Why the "Zero-Day Machine" is Terrifying the Security Industry
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
S4 · E7
April 15 · 47 min
Are AI Security Startups Faking It? How to Separate Signal from Noise
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

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