As AI systems become more integrated into enterprise operations, understanding how to test their security effectively is paramount.

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

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

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

Questions asked:

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

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

Resources discussed during the episode:

Baselines for Watermarking Large Language Models

Haize Labs

Caleb and Ashish cut through the Agentic AI hype, expose real MCP (Multi-Cloud Platform) risks, and discuss the future of AI in cybersecurity. If you're trying to understand what really happened at RSA and what it means for the industry, you would want to hear this.

In this episode, Caleb Sima and Ashish Rajan dissect the biggest themes from RSA, including:

• Agentic AI Unpacked: What is Agentic AI really, beyond the marketing buzz?
• MCP & A2A Deployment Dangers: MCPs are exploding, but how do you deploy them safely across an enterprise without slowing down business?
• AI & Identity/Access Management: The complexities AI introduces to identity, authenticity, and authorization.
• RSA Innovation Sandbox Insights
• Getting Noticed at RSA: What marketing strategies actually work to capture attention from CISOs and executives at a massive conference like RSA?
• The Current State of AI Security Knowledge

Questions asked:

(00:00) Introduction

(02:44) RSA's Big Theme: The Rise of Agentic AI

(09:07) Defining Agentic AI: Beyond Basic Automation

(12:56) AI Agents vs. API Calls: Clarifying the Confusion

(17:54) AI Terms Explained: Inference vs. User Inference

(21:18) MCP Deployment Dangers: Identifying Real Enterprise Risks

(25:59) Managing MCP Risk: Practical Steps for CISOs

(29:13) MCP Architecture: Understanding Server vs. Client Risks

(32:18) AI's Impact on Browser Security: The New OS?

(36:03) AI & Access Management: The Identity & Authorization Challenge

(47:48) RSA Innovation Sandbox 2025: Top Startups & Winner Insights

(51:40) Marketing That Cuts Through: How to REALLY Get Noticed at RSA

Dive deep into the world of AI agent communication with this episode. Join hosts Caleb Sima and Ashish Rajan as they break down the crucial protocols enabling AI agents to interact and perform tasks: Model Context Protocol (MCP) and Agent-to-Agent (A2A).

Discover what MCP and A2A are, why they're essential for unlocking AI's potential beyond simple chatbots, and how they allow AI to gain "hands and feet" to interact with systems like your desktop, browsers, or enterprise tools like Jira. The hosts explore practical use cases, the underlying technical architecture involving clients and servers, and the significant security implications, including remote execution risks, authentication challenges, and the need for robust authorization and privilege management.

The discussion also covers Google's entry with the A2A protocol, comparing and contrasting it with Anthropic's MCP, and debating whether they are complementary or competing standards. Learn about the potential "AI-ification" of services, the likely emergence of MCP firewalls, and predictions for the future of AI interaction, such as AI DNS.

If you're working with AI, managing cybersecurity in the age of AI, or simply curious about how AI agents communicate and the associated security considerations, this episode provides critical insights and context.

Questions asked:

(00:00) Introduction: AI Agents & Communication Protocols

(02:06) What is MCP (Model Context Protocol)? Defining AI Agent Communication

(05:54) MCP & Agentic Workflows: Enabling AI Actions & Use Cases

(09:14) Why MCP Matters: Use Cases & The Need for AI Integration

(14:27) MCP Security Risks: Remote Execution, Authentication & Vulnerabilities

(19:01) Google's A2A vs Anthropic's MCP: Protocol Comparison & Debate

(31:37) Future-Proofing Security: MCP & A2A Impact on Security Roadmaps

(38:00) - MCP vs A2A: Predicting the Dominant AI Protocol

(44:36) - The Future of AI Communication: MCP Firewalls, AI DNS & Beyond

(47:45) - Real-World MCP/A2A: Adoption Hurdles & Practical Examples

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

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

Questions asked:

(00:00) Introduction

(02:14) A bit about Joseph

(03:57) What is AI AppSec?

(05:11) Components of AI AppSec

(08:20) Bug Bounty for AI Systems

(10:48) Common AI security issues

(15:09) How will AI change pentesting?

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

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

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

(32:56) AI Red Teaming

(39:21) MCP Security

(42:13) Evolution of pentest with AI

Resources shared during the interview:

- How to Hack AI Agents and Applications

- Critical Thinking Bug Bounty Podcast

- The Rise of AI Hackbots

- Shift - Caido Plugin

- Shadow Repeater

- Nuclei

- Haize Labs

- White Circle AI

- Prompt Injection Primer for Engineers

Can you prove you’re actually human? In a world of AI deepfakes, synthetic identities, and evolving cybersecurity threats, digital identity is more critical than ever.

With AI-generated voices, fake videos, and evolving fraud tactics, the way we authenticate ourselves online is rapidly changing. So, what’s the future of digital identity? And how can you protect yourself in this new era?

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

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

Questions asked:

(00:00) Introduction

(03:55) Digital Identity in 2025

(14:13) How has AI impacted Identity?

(29:33) Trust and Transparency with AI

(32:18) Authentication and Identity

(49:53) What can people do today?

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

(53:49) Adoption of new identity protocols

Resources spoken about during the episode:

Tools for Humanity

World.org

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

🎙️ In this episode, we cover:

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

✅ How AI is shifting from searching to making decisions

✅ The biggest myths and misconceptions about AI automation

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

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

✅ The security risks and opportunities no one is talking about

Questions asked:

(00:00) Introduction

(03:50) What are AI Agents?

(06:53) Use case for AI Agents

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

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

(37:05) AI Agents and Non Human Identities

(45:22) The big picture with AI Agents

(48:28) Transparency and Ethics for AI Agents

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

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

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

Questions asked:

(00:00) Introduction

(02:01) A bit about Dylan Williams

(04:05) Keeping with up AI advancements

(06:24) Detection with and without AI

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

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

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

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

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

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

(36:23) LLM Models become the detector

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

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

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

Resources spoken about during the episode:

exa.ai - The search engine for AI

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

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

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

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

n8n -https://n8n.io/

Flowise -https://flowiseai.com/

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

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

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

Questions asked:

(00:00) Introduction

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

(10:22) Different Levels of Agentic AI

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

(15:57) IAM’s fire comeback

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

In this episode, to kick of 2025, we dive deep into AI and cybersecurity predictions for 2025 exploring the opportunities, challenges, and trends shaping the future of the industry.

Our hosts, Ashish Rajan and Caleb Sima sat down to discuss the evolution of SOC automation and its real-world impact on cybersecurity, the practical use cases for AI-enhanced security tools in organizations, why data security might be the real winner in 2025, the potential of agentic AI and its role in transforming security operations and predictions for AI-powered startups and their production-ready innovations in 2025.

Questions asked:

(00:00) Introduction

(06:32) Current AI Innovation in Cybersecurity

(21:57) AI Security Predictions for 2025

(25:02) Data Security and AI in 2025

(30:56) The rise of Agentic AI

(35:40) Planning for AI Skills in the team

(42:53) What to ditch from 2024?

(48:00) AI Making Security Predictions for 2025

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

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

Questions asked:

(00:00) Introduction

(02:11) A bit about Daniel Miessler

(02:22) A bit about Rez0

(03:02) Intersection of Red Team and AI

(07:06) Is red teaming AI different?

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

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

(14:55) Jailbreaking vs Prompt Injecting LLMs

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

(25:58) Prompt injection in Multimodal Models

(27:50) How Vulnerable are AI Models?

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

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

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

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

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

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

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

Resources

Daniel's Webpage - Unsupervised Learning

Joseph's Website