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

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

by Ashish Rajan and Caleb Sima

Applied Sciences & Engineering Computing & Cybersecurity UComputing and Information TechnologyURComputer security
🎙 Ashish Rajan and Caleb Sima 👥 20K 📅 December 3, 2025 ⏱ 50 min 👁 6K 📄 expert opinion 🧭 2026-08-16
Available in: English (current) Français

Keywords

AI securitybuild vs buyCodeMenderinternal prototypesAI bubble

Summary

In this episode of the AI Security Podcast, hosts Ashish Rajan and Caleb Sima discuss the build vs. buy dilemma in AI security, sparked by Google DeepMind’s CodeMender, an AI agent that autonomously finds, root-causes, and patches software vulnerabilities. They argue that while building AI prototypes is easy, scaling and maintaining them into production-grade products is extremely difficult, often leading to failure after 18 months of hidden costs and consistency issues. They explore the incentives driving internal ‘AI sprawl,’ where security teams build tools to secure budget and promotions, potentially fueling an AI bubble. The conversation also covers the overhyped state of AI security marketing, the lack of clarity on agentic AI risks, and the future where third-party security products use AI to auto-personalize to environments. They compare this to the cloud adoption cycle, predicting a similar pattern of initial DIY attempts followed by a return to vendors. The hosts share insights from their experience, including a portfolio company that had to build its own threat intel due to poor vendor quality. They conclude that while AI will become integral to security, building internal AI products is often not the right choice for most organizations.

195 words

Critical Evaluation

Value of the Information & Strength of the Argument

The value of the information lies in the practical insights from two experienced security professionals, particularly the discussion on the prototype trap and the perverse incentives driving AI adoption. The argumentation is coherent and grounded in real-world examples, such as the threat intel failure and the cloud analogy. However, the arguments are largely anecdotal and lack empirical data or formal research, which limits their generalizability.

Scientific Rigor, Source Quality, Title Accuracy

The scientific rigor is moderate; the hosts rely on personal experience and industry observations rather than citing specific studies or data. The sources mentioned are limited to the podcast’s own website and newsletter, with no external references to CodeMender or other relevant research. The title accurately reflects the content, focusing on the build vs. buy debate and the specific case of CodeMender. No comments were provided for analysis.

148 words

Title / Content Match

The title accurately reflects the core debate on build vs. buy in AI security, with specific reference to CodeMender and internal prototype failures.

Quality & Reliability

6/10

The discussion is based on practical experience and industry observations, but lacks formal citations or empirical data. The hosts provide reasoned arguments but rely on anecdotal evidence.

Chapters

Cited Sources

Concurring Sources

Contribution & Novelties

The episode provides a nuanced perspective on the build vs. buy debate in AI security, highlighting the often-overlooked challenges of scaling AI prototypes and the perverse incentives that drive internal AI projects. It offers a realistic view of the current state of AI security tools and predicts a future where AI platforms auto-personalize to environments.

Pour aller plus loin :

  • Model Context Protocol (MCP) — A protocol mentioned in the episode for connecting AI agents to tools, relevant to the build vs. buy discussion.
  • Google DeepMind’s CodeMender — The AI agent discussed, providing context on its capabilities.
  • AI Bubble Concerns — A concept discussed in the episode, with background on the potential overvaluation of AI startups.

116 words

Radar Profile

The radar profile shows moderate scores across all dimensions, indicating a balanced but not highly technical or rigorously sourced discussion. The strengths lie in the quantity of information and practical insights, while the weaknesses are in technical depth and formal reliability.

Reliability 6/10