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What happens when a protocol goes from idea to everywhere before security can catch up? Vandana Verma Sehgal, leader of the OWASP MCP Top 10, returns to explain why most organizations cannot say how many MCP servers are running on their developers' machines. She walks Chris and Robert through how MCP turns a chatbot into a new execution layer, how four lines of malicious code in a tiny server can slip past everyone, and why shadow MCP servers are shadow IT with direct access to action. The three debate whether we are missing the window to build AI security in from the start, why consent fatigue is undermining human in the loop, and where command execution really happens. Plus: why a secure MCP server should be boring, and why no MCP server deserves trust on day one. This episode is sponsored by Security Compass. Make modern software development secure, consistent, and provable. About Security Compass AI writes code faster than anyone reviews the design. Threats do not wait for an annual assessment. Security Compass models threats continuously and turns them into requirements developers act on, not a report read after ship. → Learn more about securing the AI-DLC with Security Compass This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Vandana Verma Sehgal: → Vandana Verma Sehgal on LinkedIn Mentioned in this episode: → OWASP MCP Top 10 → OWASP MCP Top 10 on GitHub → OWASP Top 10 for Large Language Model Applications → OWASP Top 10 for Agentic Applications for 2026 → OWASP Agentic Skills Top 10 → Model Context Protocol → EU AI Act (Regulation (EU) 2024/1689) → Singapore IMDA: Model AI Governance Framework for Agentic AI Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00:00 - Cold open: with great power comes great responsibility 00:00:20 - Meet Vandana Verma Sehgal 00:00:34 - Welcome and what gets Vandana away from screens 00:03:05 - What is MCP, and why should AppSec teams care now? 00:06:56 - MCP moved faster than any protocol before it 00:10:30 - Are we missing the window to secure AI? 00:12:31 - Harnesses, an AI security manifesto, and the EU AI Act 00:17:50 - Why the world needed an OWASP MCP Top 10 00:21:02 - Where command execution really happens 00:23:57 - How the list was built: community feedback and CVE data 00:26:46 - MCP09: shadow MCP servers 00:29:39 - If you could fix only one: scope creep 00:30:48 - Authentication, authorization, and delegated authority 00:35:24 - Consent fatigue and the human in the loop 00:37:04 - What a secure MCP server looks like: boring by design 00:41:27 - How AppSec teams should engage developers building MCP 00:44:46 - How to contribute to the OWASP MCP Top 10 00:45:41 - Key takeaway: never trust an MCP server on day one

What happens when a tsunami of AI-written code meets a security industry that has spent decades chasing vulnerabilities? Brook S.E. Schoenfield, CTO of Rezliant and author of Securing Systems, returns to warn about "the illusion of omniscient perfection," the cheerful confidence that makes AI output feel finished, and the "learned fragility" that sets in when nobody understands the code anymore. He, Chris, and Robert debate whether an AI threat model that gets 90% of the way there is a win, how Brook now uses AI for reachability analysis, and why prompt injection traces back to a 1975 design principle we ignored. They ask what architecture even means in the age of AI, where the next generation of architects will come from if nobody writes code, and why the collapsing cost of fixes means it's time to stop chasing vulnerabilities. This episode is sponsored by Security Compass. Make modern software development secure, consistent, and provable. About Security Compass AI writes code faster than anyone reviews the design. Threats do not wait for an annual assessment. Security Compass models threats continuously and turns them into requirements developers act on, not a report read after ship. → Learn more about securing the AI-DLC with Security Compass This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Brook S.E. Schoenfield: → Brook S.E. Schoenfield on LinkedIn → brookschoenfield.com Mentioned in this episode: → Rezliant → Securing Systems: Applied Security Architecture and Threat Models by Brook S.E. Schoenfield → Saltzer and Schroeder: "The Protection of Information in Computer Systems" (1975) → Izar Tarandach on LinkedIn → Dwarkesh Patel: "The Rise and Fall of Agent Civilizations" (essay on the OpenAI–Hugging Face incident) → Kadrey v. Meta (2025 fair use ruling on AI training) Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00:00 - Cold open — The illusion of omniscient perfection 00:00:48 - Welcome back, Brook S.E. Schoenfield 00:01:51 - AI is writing code and filling the security skills gap 00:06:18 - Can AI threat model as well as the experts? 00:08:46 - Is 90% of the way there good enough? 00:13:47 - Closing one weakness beats chasing perfection 00:16:33 - When AI threat modeling is enough, and when it isn't 00:17:54 - Learned fragility 00:19:26 - Embrace securing AI or be left in the dust 00:23:41 - Against set and forget: agent drift and AI ethics 00:29:30 - What does architecture mean in the age of AI? 00:30:29 - Mixing control and data planes: the root of prompt injection 00:32:49 - Reachability: how Brook threat models with AI today 00:35:08 - Diagram drift and inclusive threat modeling 00:38:46 - Will developers disappear and architects emerge? 00:41:00 - The cost of code has collapsed 00:44:33 - Where will the next architects come from? 00:49:15 - Final thoughts: stop chasing vulnerabilities

Jim Manico thinks the era of human code review is ending, and that clinging to it will hurt your company. The founder of Manicode Security returns to explain why AI didn't kill AppSec education but supercharged it, why vague prompting on frontier models turns companies into "token furnaces," and why prompt injection is the one genuinely new vulnerability class of the AI era. He walks Chris and Robert through his full AI coding workflow, from reverse engineering an architecture file to security rules and planning-mode build plans that make AI output deterministic. The three debate whether AI will finally eliminate SQL injection, whether AppSec vendors can survive without integrating cyber models, and when humans still need to step in: the moment an agent tries something its policy doesn't allow. Plus: the one habit every security leader should teach developers now. This episode is sponsored by Security Compass. Make modern software development secure, consistent, and provable. About Security Compass AI writes code faster than anyone reviews the design. Threats do not wait for an annual assessment. Security Compass models threats continuously and turns them into requirements developers act on, not a report read after ship. → Learn more about securing the AI-DLC with Security Compass This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Jim Manico: → Jim Manico on LinkedIn Mentioned in this episode: → Manicode Security → Manicode Forge → OWASP Artificial Intelligence Security Verification Standard (AISVS) → OWASP AISVS on GitHub → Claude Code → Ollama Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00:00 - Cold open: the era of code review is ending 00:00:47 - Meet Jim Manico 00:01:03 - Welcome and what gets Jim away from screens 00:05:29 - How AI changed developer security education 00:07:03 - Teaching developers to use AI well 00:09:26 - Where AI and AppSec stand: the token furnace 00:12:08 - Separating signal from hype 00:14:24 - Integrate with cyber models or die: the future of AppSec tools 00:18:34 - Are AI coding assistants creating new vulnerabilities? 00:20:02 - Prompt injection: the one truly new class 00:21:15 - Will AI eliminate the OWASP Top 10? 00:24:18 - Local models and Apple's hardware edge 00:28:32 - Jim's AI coding workflow, step by step 00:33:57 - The end of human code review 00:38:31 - Can we trust AI code review, and when do humans step in? 00:44:56 - Where companies really are with AI 00:47:02 - One change for security leaders: planning mode 00:48:19 - Wrap up

Three years ago, Jeevan Singh mapped out what an application security engineer needed to know. AI has rewritten the job since. Jeevan, Director of Security Engineering at Rippling, returns to unpack how his team polices thousands of engineers shipping 10x more code: a "vulnerability jail" that locks non-compliant teams out of the main branch, AI-reviewed extension requests, and homegrown agents that hunt for entire classes of vulnerabilities instead of one bug at a time. He and Chris debate whether AI has killed classic SAST and DAST, whether code review still needs a human in the loop, and whether bug bounty programs still make sense when the researchers on both sides are running the same models. Plus: one concrete move every AppSec leader can make this quarter. This episode is sponsored by Security Compass. Make modern software development secure, consistent, and provable. About Security Compass AI writes code faster than anyone reviews the design. Threats do not wait for an annual assessment. Security Compass models threats continuously and turns them into requirements developers act on, not a report read after ship. → Learn more about securing the AI-DLC with Security Compass This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Jeevan Singh: → Jeevan Singh on LinkedIn Mentioned in this episode: → Rippling → Dwarkesh Patel: "The Rise and Fall of Agent Civilizations" (essay on the OpenAI–Hugging Face incident) Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00:00 - Cold open: vulnerability jail 00:00:55 - Meet Jeevan Singh 00:01:11 - Welcome and Robert's AI lab 00:02:37 - What gets Jeevan away from the machines 00:04:51 - Hardware projects with his son 00:06:20 - What's changed for the AppSec engineer since AI 00:08:17 - Shipping production code and fixing whole vulnerability classes 00:09:13 - Why AppSec has to become less collaborative 00:10:02 - From democratized vuln management to vulnerability jail 00:11:50 - How engineering reacted and the feature flag jail precedent 00:14:05 - From manual jail to automated checks 00:15:30 - An AI bot that reviews SLA extensions 00:16:06 - Can AI wipe out an entire vulnerability class? 00:17:36 - Building an anti-SSRF library and rolling it out 00:19:40 - Which AppSec skills matter now 00:22:28 - Validating all that AI-generated code 00:22:57 - Agents that hunt for vulnerability classes 00:24:38 - Is this the death of classic AppSec tools? 00:26:51 - Code review and humans in, on, and out of the loop 00:28:00 - Objective-based agents that find RCEs 00:28:59 - Build vs. buy for AppSec teams 00:31:29 - Advice for teams of one to five AppSec engineers 00:32:58 - Cutting SLAs to 3, 5, 7, and 10 days 00:34:31 - Parachuted in as the only AppSec engineer 00:37:59 - One investment to make this quarter 00:39:09 - The Hugging Face and OpenAI agent incident 00:40:13 - Is bug bounty dead? 00:42:19 - Key takeaways: be an engineer, run toward AI 00:43:53 - Wrap-up

Most fault trees get built on gut feeling. Petra Vukmirovic did something rarer: she borrowed the actual math from aviation and nuclear-plant safety engineering and pointed it at AI agents. Petra traded emergency medicine for application security and now heads information security at Numan — and she joins Chris Romeo and Robert Hurlbut to make the case for fault tree analysis (FTA), the deductive method that picks up exactly where threat modeling stops. Petra walks through a "wrong customer refund" AI agent scenario step by step, showing how AND/OR gates and minimal cut sets turn vague worry into ranked, data backed probabilities. They dig into where AI helps build a tree, and where garbage in, garbage out still applies, why "comprehensive test coverage" is a myth, and how attaching real dollar figures to failure paths makes it easier to sell security controls to leadership. This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Petra Vukmirovic: → Petra Vukmirovic on LinkedIn → OWASP Threat Model Library Mentioned in this episode: → Adam Shostack: "Stop Trying to 'Manage Risk'" (keynote) → OWASP Global AppSec USA 2026 (San Francisco, Nov 5–6) Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00 Cold open — the math behind where to put your controls 01:09 Meet Petra Vukmirovic 01:28 Petra's origin story: from ER doctor to AppSec 02:50 Career path: engineer to Head of InfoSec at Numan 04:18 What is fault tree analysis, and where threat modeling ends 06:22 Can AI actually do fault tree analysis? 08:12 Walking the "wrong customer refund" agent example 12:33 Storing your trees: JSON vs. Markdown 16:08 Why conjunctive failures trip up narrow thinking 17:27 Top 3 failure modes when agents touch downstream systems 19:29 Real story: an agent pushed code to main without approval 21:27 Testing: why "comprehensive coverage" is a myth 23:54 How rough is rough? Assigning probabilities 28:08 Getting started without a six week science project 31:35 Using FTA to sell controls and build credibility 33:43 The epiphany: FTA is about controls, not faults 34:48 The one thing every agentic team should add today 35:48 Closing thoughts and OWASP Global AppSec USA preview

Most security champions programs don't fail on tooling — they fail on people. Lisi Hocke spent three years as a champion before moving fully into product security, which means she has argued both sides of this from inside the trenches. Drawing on the talk she and Mireia Cano gave at OWASP Global AppSec EU 2026, Lisi walks us through the four things that actually make these programs work: psychological safety first, then cognitive load, then influence when you hold no formal authority, then a champions community so the whole thing doesn't stall the week security goes on vacation. We also get into cutting security wait times, winning organizational support, what AI does and doesn't change here, and why she will tell you never to record the champions meeting. This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Lisi Hocke: → Lisi Hocke on LinkedIn → A Tester's Journey — Lisi's blog Mentioned in this episode: → Slides: Security Champions — Lessons from Opposite Trenches (with Mireia Cano) → OWASP Juice Shop Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00 Cold open — what psychological safety actually means 00:56 Meet Lisi Hocke 02:25 Lisi's security origin story 05:42 "That place was taken" — becoming a champion anyway 07:39 Moving into a full-time product security role 08:39 Meeting Björn Kimminich, the Juice Shop project lead 09:23 Why role play instead of a normal conference talk 12:27 Security and development, disconnected 14:19 The first full-time security role 15:14 Making people wait is the real damage 17:10 Cutting the backlog and the turnaround time 19:31 What Lisi got dead wrong 20:08 What testing and quality work taught her 21:31 The four things that make champions programs work 22:07 One: fostering psychological safety 24:50 Champions without their manager's blessing 28:45 Two: managing cognitive load 29:46 Three kinds of load, and which one to cut 31:21 Three: power sources when you have no formal authority 33:03 Four: build a champions community 34:38 Keeping security people from burning out 36:37 How AI changes who you recruit and what you need 39:32 Should AI change champions programs at all? 40:33 Psychological safety when a bot joins the meeting 42:25 Don't record the champions meetings 43:26 Programs that outlive the person who started them 45:59 Key takeaway and homework 47:21 Closing thoughts

Is traditional DAST finally dead? James Berthoty came back to settle the argument that his last episode started. James is the founder and analyst behind Latio, and he argues that AI pentesting is a genuinely different animal — payloads generated with context about your actual application, agents that chase findings the way a human tester would, and results a scanner was never going to produce. We get into what it costs once tokens enter the picture, who pays for them, whether a pentest on every pull request is realistic, and what stops an autonomous tester from going further than it should. Then we look further out: the future of bug bounties, what happens when cloud and model providers absorb today's security tooling, and who is accountable when an agent deletes your production database. This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with James Berthoty: → James Berthoty on LinkedIn → Latio → Latio Pulse Mentioned in this episode: → Latio's free reports → James on the podcast the first time: Is DAST Dead? And the future of API security Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00 Cold open — the results speak for themselves 01:01 Meet James Berthoty and the "Is DAST dead?" fallout 01:31 Chickens, eggs, and getting away from screens 03:46 Why we're revisiting the DAST question 04:14 A working definition of AI pentesting 05:47 Contextual payloads and application awareness 06:47 Determinism, repeatability, and what buyers actually want 07:46 Can you run an AI pentest on every code change? 09:43 What it really costs 10:40 Incumbents vs. AI-native vendors 13:27 Who pays for the tokens? 14:29 Bundling, platforms, and competitive pressure 16:25 AI across the whole development workflow 18:22 Agents that run all the way to deployment 19:21 A pentest on every pull request 21:52 What stops a pentest from going too far? 23:10 Permission scoping and guardrails 26:07 Where the findings actually land 28:02 The future of bug bounties 30:50 Why pentests command more budget than DAST 31:45 Could the cloud providers absorb security tooling? 34:38 What model providers could build instead 36:36 The same story on the code scanning side 39:24 Accountability when the tool misses something 40:22 Shared responsibility when an agent deletes production 41:23 The verdict on DAST 42:19 Where to find Latio's free reports 43:15 Closing thoughts

AI security has no shortage of standards — the problem is turning them into something a team can actually use. Rob van der Veer has spent 34 years in AI and security, founded the OWASP AI Exchange, and created MOSAIC, the agreement that brought eight standards bodies together with SANS to stop the fragmentation. Rob explains what responsible AI really means, what the EU AI Act actually asks of you, and why most AppSec teams are still missing the point on AI-generated code. We also get into agentic red teaming, what happens when agents quietly exceed their scope, and whether AI finally levels the playing field between attackers and defenders. If you build software with AI in it — or with AI — this one is worth your time. This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Rob van der Veer: → Rob van der Veer on LinkedIn → OWASP AI Exchange → MOSAIC Mentioned in this episode: → OpenCRE → Luna and the Magic AI Paintbrush Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00 Cold open — don't be surprised when the AI breaks out of the cage 01:15 Meet Rob van der Veer: music, cycling, and the Hoodoo 500 05:24 Defining responsible AI 07:41 Fairness, protected attributes, and transparency 09:34 The EU AI Act and what regulation actually asks of you 11:27 How AI changes every part of software development 13:23 Where responsibility lands 15:20 You're not defending your own data center 17:19 What traditional AppSec teams consistently miss about AI 18:18 Finding vulnerabilities in AI-generated code 19:19 Too many standards — and using AI to write them 20:51 MOSAIC: eight standards bodies, one agreement 22:09 One machine-readable taxonomy with OpenCRE 23:06 Can AI level the field between attackers and defenders? 25:59 When AI security becomes security theater 26:56 What agentic red teaming actually looks like 29:44 When agents exceed their scope 32:43 Luna and the Magic AI Paintbrush 33:40 Do we sandbox the agents? 34:40 Guardrails without killing creativity 36:39 Skill atrophy when AI is your only way forward 40:04 "How do we hit this quarter?" and the pressure to ship 43:16 Everyone is selling agentic security 45:07 Key takeaways and where to start with the AI Exchange 47:12 Closing thoughts

You don't have to let AI do the thinking for you. In this episode, Vikram Narayan shares why the smartest teams use AI as an accelerant — not a replacement — and why human judgment still matters most in threat modeling. Vikram created Precogly, an open-source threat modeling platform now running as an OWASP project, and he walks us through what it took to build a free tool on par with commercial vendors. We dig into the tension among speed, compliance, and real risk; whether the Threat Modeling Manifesto needs amending for AI; and what it means to "fight the AI" so critical thinking stays sharp. If you care about AppSec, AI, and the future of threat modeling, this conversation will give you a lot to think about. This episode is sponsored by Corgea. Design it. Build it. Ship it. Corgea secures it. About Corgea Corgea is an AI-native application security platform that secures software from design to production. It brings together security design reviews, AI SAST, dependency and IaC scanning, code quality checks, and autonomous pentesting—helping security and engineering teams find risk earlier, fix what matters, and ship securely. → Learn more about Corgea Connect with Vikram Narayan: → Vikram Narayan on LinkedIn → Precogly — open-source threat modeling (OWASP project) Mentioned in this episode: → Threat Modeling Manifesto → ThreatModCon Follow the Application Security Podcast: ➜ Home: appsecpodcast.com ➜ X: @AppSecPodcast ➜ LinkedIn: The Application Security Podcast ➜ YouTube: @ApplicationSecurityPodcast ➜ Instagram: @appsecpodcast ➜ Facebook: Application Security Podcast Chapters: 00:00 Cold open — the threat model that "feels wrong" 01:22 Welcome and introductions 02:17 Vikram's security origin story 05:43 From machine learning research into LLMs 06:42 Hospital chatbots, hallucination, and knowing when to escalate 07:51 ThreatModCon and the case for an open-source threat modeling tool 09:35 IoT, emergence, and the traffic-light problem 11:17 The OWASP Vienna talk and the Threat Modeling Manifesto 12:26 Why "AI, just do the threat model" falls apart 15:14 What AI is actually good at in threat modeling 18:07 Human discomfort vs. the machine's confident answer 20:29 Inside Precogly: accelerant, not replacement 20:58 Library packs and the skills layer 24:50 Where AI kicks in — and where it shouldn't 27:48 Should the Threat Modeling Manifesto be amended for AI? 30:28 Where Chris and Robert land 31:36 Wi-Fi sensing, privacy, and modeling what you can't see 33:15 If you can't explain it, can you trust it? 35:11 Beyond checklists — design-level questions 35:59 Fight the AI — Vikram's key takeaway 39:10 Closing thoughts

AI is moving AppSec's control point out of CI and directly into the coding agent—but what happens when the model writing the code is also expected to secure it? Semgrep co-founder and CEO Isaac Evans explains why deep background analysis and real-time agent plugins may replace universal rule sets with organization-specific security controls. He and Chris explore how security engineering roles will change, why independent verification still matters, and where business-logic flaws may become the next major battleground. The conversation also covers vibe coding at enterprise scale, the limits of reasoning about model behavior, open source in an agent-built world, and why Isaac sees more opportunity than threat even as AI creates a fresh wave of vulnerabilities and cleanup work. Connect with Isaac Evans: → Isaac Evans on LinkedIn → Semgrep Mentioned in this episode: → Semgrep → DeepSeek → Cursor → OpenAI Codex → Claude Code → Boston Dynamics → DARPA Robotics Challenge → Reflections on Trusting Trust → uutils/coreutils → Rust Follow the Application Security Podcast: ➜ Home ➜ X ➜ LinkedIn ➜ YouTube ➜ Instagram ➜ Facebook Chapters: 00:00 Meet Isaac Evans 01:11 From cryptography to the DARPA Robotics Challenge 03:43 Founding Semgrep 04:05 How AI is reshaping AppSec 06:15 Attackers, defenders, and model choice 08:02 Regenerating code until it clears the security bar 10:30 Organization-specific rules beat universal rules 14:18 The changing role of the security engineer 17:53 Career advice for security practitioners 19:47 Will foundation models absorb security vendors? 24:18 Getting secure changes across an enterprise 26:40 Trusting Trust becomes the easy problem 27:41 How much should we trust agent-generated code? 29:38 Independent verification and competing models 34:50 Business logic flaws after SQL injection 36:56 Protecting the new wave of citizen developers 39:40 Vibe coding and disposable software 42:05 Open source in an agent-built world 44:10 Can the exponential pace continue? 44:41 Key takeaways and calls to action

Why do broken access control and injection still dominate the OWASP Top 10 despite years of mature tooling? Okta's José Carlos Chávez joins Chris to explain what changed in the 2025 list—and what stubbornly did not. Drawing on his path from software engineering and observability into security, José examines why ownership and root causes matter more than another scanner. They explore the rise of supply-chain and software-integrity failures, the fragile security model around downloaded AI skills and agent permissions, and the continuing need for immutable, trustworthy logging. Along the way, José uses museum heists to make the Top 10 memorable and shows how its categories connect. The result is a practical look at where AppSec teams should focus when familiar vulnerabilities persist and autonomous tools gain more access. Connect with José Carlos Chávez: → José Carlos Chávez on LinkedIn → OWASP Coraza Mentioned in this episode: → OWASP Top 10:2025 → OWASP Coraza → Traceable → tj-actions/changed-files advisory (CVE-2025-30066) → Apache Kafka → Istio → Falco → OpenTelemetry → lodash → The left-pad incident Follow the Application Security Podcast: ➜ Home ➜ X ➜ LinkedIn ➜ YouTube ➜ Instagram ➜ Facebook Chapters: 00:00 Meet José Carlos Chávez 01:19 From software engineering to application security 04:54 Observability as a security foundation 10:32 Museums, heists, and teaching the OWASP Top 10 13:50 What changed in the OWASP Top 10 for 2025 17:31 Why broken access control is still number one 24:59 Why injection refuses to disappear 30:05 Supply chain risk vs. software integrity failures 34:11 Can you trust downloaded AI skills? 35:13 When an agent quietly controls your computer 38:29 Immutable logging and incident evidence 43:46 Root causes across the Top 10 49:08 Ownership is the key takeaway 52:07 Closing thoughts

When every employee can generate working software, who owns the risk? Michael Burch, VP of AI Enablement and Acceleration at Security Journey, explains how AI is turning nondevelopers into citizen developers faster than enterprises can build guardrails around them. He and the hosts examine rollouts that hand GitHub and Claude Code to hundreds of employees, the danger of measuring adoption instead of business value, and why prompt libraries alone do not meet people where they work. Michael argues for sandboxed workflows, automated security controls, clear limits, and AI champion programs that quietly carry security practices into every team. The discussion closes on evaluating generated code, token-cost incentives, and the need to define a measurable outcome before buying licenses or opening production access. Connect with Michael Burch: → Michael Burch on LinkedIn → Security Journey Mentioned in this episode: → SecureMyVibe → Manicode Security → The Security Champions Podcast → OWASP Low-Code/No-Code Top 10 → Claude Code Follow the Application Security Podcast: ➜ Home ➜ X ➜ LinkedIn ➜ YouTube ➜ Instagram ➜ Facebook Chapters: 00:00 Meet Michael Burch 02:12 From Army Ranger to AppSec education 05:40 What is an AI-era citizen developer? 06:52 When low-code guardrails disappear 08:49 Giving GitHub to 200 nondevelopers 12:16 The rollout is already underway 13:23 What happens outside the pipeline 17:20 Training gaps and adoption without outcomes 20:03 Measuring ROI instead of usage 22:28 Why prompt libraries are not enough 25:28 Sandboxing citizen developers 28:36 Are organizations waiting for a breach? 29:56 Turn security champions into AI champions 32:00 How AppSec teams need to change 34:58 Guardrails, expectations, and saying no 38:41 Can developers evaluate generated code? 42:40 Token pricing and platform lock-in 44:36 When token usage becomes the wrong incentive 46:17 A practical plan for citizen development 48:28 Closing thoughts

Traditional SAST is deterministic but shallow; AI can reason about context but may answer differently every time. Can the two approaches make each other better? Bounce Security CTO Josh Grossman explains why he built AGHAST, an open-source framework that combines static discovery with LLM analysis to investigate authorization, business-logic, and organization-specific risks. He walks through reducing false positives, importing SARIF, controlling token costs, and deciding where AI-assisted checks belong in developer workflows and CI. Josh also shares how he used Claude Code to build most of the project while retaining the architecture, product judgment, and code-review responsibility himself. The episode closes with AGHAST's roadmap, supported languages, practical adoption advice, and a guided demonstration of the tool. Connect with Josh Grossman: → Josh Grossman on LinkedIn → OWASP AGHAST Mentioned in this episode: → OWASP AGHAST → Semgrep → Cursor → Claude Code → SARIF → NDC Security → Black Hat → DEF CON → ISACA → Manicode Security Follow the Application Security Podcast: ➜ Home ➜ X ➜ LinkedIn ➜ YouTube ➜ Instagram ➜ Facebook Chapters: 00:00 Meet Josh Grossman 01:11 Why Josh built AGHAST 04:22 Will AI disrupt AppSec tooling? 06:09 How AGHAST combines static analysis and AI 08:48 Deterministic rules and pure AI checks 10:23 Reducing SAST false positives 11:49 Using SARIF from existing scanners 12:46 Building AGHAST with Claude Code 14:14 The product specification and human judgment 17:48 How much code did the AI write? 19:05 The architect and product-manager mindset 21:08 Token economics becomes its own industry 22:54 Authorization and business-logic checks 25:55 Context makes custom rules valuable 28:15 Where AGHAST belongs in the workflow 30:11 Languages, frameworks, and COBOL 32:10 The AGHAST roadmap 34:20 Key takeaway and call to action 36:56 Training and conference appearances 37:29 AGHAST demonstration

GitGuardian found 29 million hard-coded secrets in public GitHub commits in one year—a 34% increase and its largest jump yet. Why is a supposedly simple problem getting worse? Principal Developer Advocate Dwayne McDaniel explains what the 2026 State of Secrets Sprawl report reveals about public and private repositories, AI coding tools, MCP server templates, and developer-targeted supply-chain attacks. He and Chris unpack why standing credentials persist, how private repositories create false confidence, and why frontier models may improve without solving the organizational problem. The conversation moves from detection to governance: short-lived identity, ownership, feedback loops, and the political will to remove embedded keys. Dwayne's core challenge is blunt—organizations already have better authentication patterns, so what will make them finally use them? Connect with Dwayne McDaniel: → Dwayne McDaniel on LinkedIn → State of Secrets Sprawl 2026 Mentioned in this episode: → GitGuardian State of Secrets Sprawl Report 2026 → LangChain → OpenRouter → DeepSeek → Mistral AI → Perplexity → Ox Security → SPIFFE → CNCF → AWS STS → OpenID Connect → GitHub Octoverse → Claude Code Follow the Application Security Podcast: ➜ Home ➜ X ➜ LinkedIn ➜ YouTube ➜ Instagram ➜ Facebook Chapters: 00:00 Meet Dwayne McDaniel 00:39 Dwayne's path into secrets security 02:23 How GitGuardian builds the report 05:10 Where the private-repository data comes from 06:20 Twenty-nine million leaked secrets 09:15 Why the problem persists 12:37 Secrets, identity, and standing privilege 15:21 Three ways AI makes leakage worse 16:39 Explosive growth in AI-service credentials 17:57 MCP templates teach insecure authentication 20:08 Is Claude Code getting safer? 22:55 Hope for frontier models 24:36 What will the OWASP Top 10 become? 27:27 AI-assisted attacks target developers 30:29 Old supply-chain attacks at machine speed 33:06 What are organizations protecting now? 35:39 Private repositories are six times riskier 38:48 Moving from the problem to solutions 39:21 Does the organization have the will to fix it? 40:53 Governance and short-lived credentials 44:51 Closing thoughts

If AI writes all the code and the developer barely reads it, where does AppSec fit? Tanya Janca returns to define vibe coding and explain why models trained on insecure public code do not understand secure design by default. She and the hosts build a practical secure-vibe-coding framework: explicit requirements, human-led threat modeling, reusable security prompts, iterative review, SAST, and independent testing. Tanya shares hard-earned examples of Claude removing error handling, models confidently reviewing their own insecure output, and developers accepting enormous finding backlogs for code they did not enjoy writing. The discussion also examines whether security tooling will consolidate into AI platforms, how AppSec must be reimagined, and why continuous, embedded guidance matters more than occasional training. Tanya closes by introducing her DevSecStation podcast. Connect with Tanya Janca: → Tanya Janca on LinkedIn → SecureMyVibe → DevSecStation Mentioned in this episode: → SecureMyVibe → OWASP Top 10 → Burp Suite → OWASP ZAP → DevSecStation → Tanya Janca (SheHacksPurple) → ChatGPT → Stack Overflow → The Security Table podcast Follow the Application Security Podcast: ➜ Home ➜ X ➜ LinkedIn ➜ YouTube ➜ Instagram ➜ Facebook Chapters: 00:00 Tanya Janca returns 02:10 What vibe coding actually means 04:03 AI adoption and how developers prompt 05:57 Treating AI like an intern 07:28 Do AI systems need parental controls? 09:34 Security prompts for everyday development 11:47 Models, memory, and protecting sensitive data 14:11 What happens when Claude goes away? 16:02 The most dangerous vibe-coding misconception 18:06 Threat modeling before AI writes the code 22:48 Using AI throughout the development lifecycle 25:32 A reusable secure-prompt framework 29:09 Expose security assumptions and challenge flattery 32:30 Reviewing an AI-generated codebase 36:09 Will AI platforms absorb security tooling? 37:39 Five million findings for code nobody wrote 42:19 The remaining pieces of secure vibe coding 43:52 Reimagining AppSec 45:25 Continuous guidance beats occasional training 46:05 Introducing DevSecStation 47:12 Closing thoughts