Should you build your own AI security tools or buy from a vendor? In this episode, Ashish Rajan and Caleb Sima dive deep into the "Build vs. Buy" debate, sparked by Google DeepMind's release of CodeMender, an AI agent that autonomously finds, root-causes, and patches software vulnerabilities .

While building an impressive AI prototype is easy, maintaining and scaling it into a production-grade security product is "very, very difficult" and often leads to failure after 18 months of hidden costs and consistency issues . We get into the incentives driving internal "AI sprawl," where security teams build tools just to secure budget and promotions, potentially fueling an AI bubble waiting to pop .

We also discuss the "overhyped" state of AI security marketing, why nobody can articulate the specific risks of "agentic AI," and the future where third-party security products use AI to automatically personalize themselves to your environment, eliminating the need for manual tuning .

Questions asked:

(00:00) Introduction: The "Most Innovative" Episode Ever(01:40) DeepMind's CodeMender: Autonomously Finding & Patching Vulnerabilities(05:00) The "Build vs. Buy" Debate: Can You Just Slap an LLM on It?(06:50) The Prototype Trap: Why Internal AI Tools Fail at Scale(11:15) The "Data Lake" Argument: Can You Replace a SIEM with DIY AI?(14:30) Bank of America vs. Capital One: Are Banks Building AI Products?(18:30) The Failure of Traditional Threat Intel & Building Your Own(23:00) Perverse Incentives: Why Teams Build AI Tools for Promotions & Budget(26:30) The Coming AI Bubble Pop & The Fate of "AI Wrapper" Startups(31:30) AI Sprawl: Repeating the Mistakes of Cloud Adoption(33:15) The Frustration with "Agentic AI" Hype & Buzzwords(38:30) The Future: AI Platforms & Auto-Personalized Security Products(46:20) Secure Coding as a Black Box: The End of DevSecOps?

Build vs. Buy in AI Security: Why Internal Prototypes Fail & The Future of CodeMender

AI Security Podcast