Doc-foundry – AI writes Security detection rules, then proves they work
Adversarial multi-agent detection engineering: Blue authors Sigma rules, Red attacks them, a deterministic referee proves coverage, and a stakeholder review chain ships the rule with its review tra...
An AI system creates security detection rules and proves their effectiveness through a structured process.
The AI writes Sigma rules and provides proof of their effectiveness.
A deterministic referee evaluates the rules for accuracy and evasion resistance.
The system includes specialized agents for threat analysis and validation.
Summarised automatically by AI from the original article by Hacker News. AI can make mistakes, so check the original for details.
The thing nobody else produces is #3. A model can write you a Sigma rule; it cannot hand you a proof that the rule works, an adversary that provably evades the weak version, and an honest record of how the rule was hardened until it held.
A bounded, fixed adversary. Red commits to two real, documented attacks in round one and re-throws those same two against the hardened rule each round — the same "built once, can't move its goalposts" discipline as the fixed test corpus. real-coverage therefore means something concrete and honest: the first-version rule fires on every attack in the corpus, stays silent on benign look-alikes, and caught both of Red's real attacks. It is a validated baseline, not a promise of coverage against every future evasion — that is the detection team's job to extend.
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