There's No D3FEND for AI (So He Built One) feat. Edward Lee

About this episode

Quick count: MITRE ATLAS, OWASP's Top 10s, NIST AML, Cisco's framework, MAESTRO, Databricks' AI security framework..Every org says "go do AI security," startups are popping out to sel lit to you…points you at a pile of frameworks that all describe the same handful of problems in slightly different words, and leaves you to cross-reference them yourself at 11pm.

Edward Lee joins Dispatch to talk about AIDEFEND: the open-source knowledge base he built to be the thing MITRE ATT&CK has and AI security doesn't: a D3FEND. One place to search across AIDEFEND and ATLAS techniques, MAESTRO threats, every OWASP Top 10, Cisco, NIST AML,  type in a keyword and see how it maps everywhere at once, instead of holding nine tabs open and doing the translation in your head.

In this episode we get into:

• "Detect" isn't one thing — it's 16+ techniques, and only three signals actually matter: prompt-level telemetry, tool call graphs, and retrieval provenance
• Why the retrieval layer is ground zero — if you can't say what got pulled and from where, you're not reconstructing that incident, you're guessing
• Rogue agents behave less like malware and more like an insider threat — legit permissions, illegitimate intent
• "Goal drift": when a tool call sequence takes a left turn that only makes sense if something got injected midstream
• The case for giving agents their own identities instead of borrowed ones — and why token usage tells you almost nothing useful
• Where to start logging with zero visibility today: proxy-level tool calls and API calls, before you even look at content

Follow Edward's work:

• AIDEFEND — aidefend.net
• AIDEFEND on GitHub — aidefend.dev

Detection Dispatch (Alex's Version) is an independent detection engineering & threat hunting podcast. Rebuilt. Community-first. Featuring a lineup of the real and active projects pushing the limits of detection engineering, threat hunting, and everything in between.