Every reviewer correction on the platform — anonymized, aggregated, public. This is the proof that closed-loop learning is real, not a slide. Nobody else in CMMC publishes this.
How we measure: Every batch ingested under a model_version is replayed through _shared/gap-diff.ts against the stored human_output in v_athena_learning_dataset. Acceptance = reviewer kept the AI output unchanged. Δ score = average token-level distance from human truth (lower is better).
MFA enforced for remote access only
MFA enforced for all privileged accounts, including local console
Mapped to network monitoring controls (incorrect)
Mapped to external system use — vendor MSAs are the right evidence
FIPS-validated cryptography assumed from product name
Requires explicit CMVP certificate # — pulled from vendor PDF
IRP narrative flagged as "weak language"
Pattern matched to NIST 800-61 template — accepted as strong
Baseline config applied only to servers
Baseline must include endpoints + cloud workloads
Single SIEM screenshot accepted as monitoring evidence
Requires alert-rule export + 30-day retention proof
This page is generated from anonymized rollups of gap_review_workflow_metrics and v_athena_learning_dataset. No customer names, no document contents, no identifying metadata ever appears here. The point is to show that Athena learns — and to let you compare us against any other CMMC tool that claims AI. None of them publish anything like this.
Start an assessment today and your corrections shape the model your competitors run on tomorrow.