Govern AI systems
with AI-native oversight.

Continuously monitors AI/ML model outputs for disparate impact across protected attributes (race, gender, age, disability). Applies four-fifths rule and statistical significance testing, generates bias reports, and triggers model retraining when thresholds are breached.

Classifies all organizational AI systems by risk level (Unacceptable, High, Limited, Minimal) per EU AI Act framework. Maintains AI system inventory, assesses risk per use case, and generates conformity assessment documentation for high-risk systems.

Monitors compliance across the full AI lifecycle. Tracks conformity assessments, CE marking requirements, post-market monitoring obligations, serious incident reporting, and transparency requirements for each AI system under the EU AI Act.

Generates human-readable explanations for AI model decisions. Produces feature importance analysis, counterfactual explanations, and decision audit trails. Validates that adverse action notices include AI-specific reasoning per ECOA/FCRA requirements.

Continuously tracks AI model accuracy, precision, recall, and latency in production. Detects data drift and concept drift using PSI and KL divergence statistical tests, and triggers alerts when performance degrades below configured thresholds.

Detects and responds to AI system failures, harmful outputs, and adversarial attacks. Triggers model rollback for safety-critical issues, generates AI incident reports, coordinates with InfoSec, and manages regulatory notification for serious AI incidents.

Screens new AI use cases against ethical guidelines and organizational AI principles. Evaluates potential harms, assesses proportionality, checks for dual-use concerns, and generates ethics review recommendations for the AI Ethics Committee.

Manages synthetic data generation for AI training — ensuring no re-identification risk, proper consent chain from source data, statistical fidelity validation, and compliance with privacy regulations for all training data handling.