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

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?

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