Closed-Loop ML
Continuous Defense Hardening
Adversarial false negatives from attack simulations feed the retraining pipeline to harden decision boundaries.
Closed-Loop Feedback Architecture
From threat detection failure to promoted model weights
Current Model
v1.0 Baseline
Attack Simulation
Red Team generates adversarial scenarios
Blue Team Detection
Calculates risk probabilities & boundaries
False Negative Discovery
Missed attacks intercepted as hard examples
Hard Example Mining
Saved to SQLite feedback repository
Adversarial Retraining
Fits new Random Forest on augmented data
Promoted Hardened Model
v2.0 Active