Artificial Intelligence1 min read

Doc-foundry – AI writes Security detection rules, then proves they work

By · Published by Everything Blog

In short

The platform ingests data such as CVEs, CISA KEV entries, and ATT&CK techniques to output validated baseline Sigma rules backed by detailed proof bundles. Evaluations rely on a deterministic engine to referee adversarial red-team mutations against benign telemetry look-alikes. In addition to an iterative Blue-versus-Red testing loop, users can deploy an alternate review chain that incorporates SOC lead feedback to effectively balance evasion risk against alert volume.

Key points

  • Detection verdicts and coverage ratings are decided by a deterministic engine rather than…: Detection verdicts and coverage ratings are decided by a deterministic engine rather than relying solely on subjective model outputs.
  • Organizations can select between an adversarial proof loop and a structured review chain…: Organizations can select between an adversarial proof loop and a structured review chain to tailor rule generation to their operational needs.
  • The software functions via a Python command-line interface or local web interface, suppor…: The software functions via a Python command-line interface or local web interface, supporting both live executions and offline canned fixtures.

Sources checked by our analysis pipeline: 1

Turn a threat (a CVE, a CISA KEV entry, an incident writeup, an ATT&CK technique) into a validated first-version detection — a proven starting point a detection team hardens further, not a claim of finished, forever-bulletproof coverage — plus three artifacts that ship together:

- a Sigma detection rule that has survived a Blue-vs-Red adversarial loop,

- a threat report with the round-by-round convergence history, and

- a proof bundle — a positive corpus the rule fires on, a benign baseline it

stays silent on, an adversarial mutation set, and a computed coverage label

(

real-coverage

/brittle

/broken

) with FP/FN numbers and a reproducibility hash.

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…

Artificial Intelligence