Decision Provenance

The unbroken chain tracing input data from its origin through every transformation, join, and feature engineering step. Data Lineage Graphs capture source systems, timestamps, and transformation logic.

• Tracks raw data ingestion from operational databases or streams
• Records all ETL/ELT transformations with deterministic hashes
• Links to upstream Data Quality Posture metrics at ingestion time
• Enables point-in-time reconstruction of the exact feature vector

A composite cryptographic identifier that uniquely binds a specific prediction to its originating context. The fingerprint combines the model version hash, input snapshot hash, and configuration parameters (temperature, top-k, system prompt).

• Uses SHA-256 or stronger hashing over canonical JSON serialization
• Includes the Model Card version active at inference time
• Enables Deterministic Replay for regulatory investigations
• Serves as the primary key linking predictions to their audit trail

A recorded event capturing every instance where a human operator intervenes to modify or reverse an automated decision. This creates a critical accountability boundary between algorithmic output and final business action.

• Logs operator identity, timestamp, and authorization context
• Records the original model output and the overridden value
• Triggers Chain of Custody updates for downstream consumers
• Supports Right to Explanation requirements under GDPR Article 22

The security property ensuring that no entity can plausibly deny the authenticity or origin of a logged decision. Achieved through Secure Timestamping (RFC 3161) and digital signatures bound to hardware-backed keys.

• Each decision record receives a trusted timestamp from a Timestamp Authority
• Merkle Tree Hashing enables efficient verification of log consistency
• Secure Enclave Logging protects records from host OS tampering
• Supports legal admissibility under eIDAS and similar frameworks

Regulatory and organizational rules expressed as machine-readable code that automatically validates every decision point before execution. Non-compliant decisions are blocked and logged as violations.

• Encodes constraints like Purpose Limitation and data minimization
• Evaluates rules synchronously within the decision path
• Logs policy evaluation results alongside the decision record
• Enables Continuous Compliance Monitoring across model versions

The practice of attaching interpretability outputs directly to the decision record. SHAP Value Logging captures feature attribution at inference time, while Hallucination Flagging marks outputs below confidence thresholds.

• Stores SHAP or LIME explanations as structured JSON in the audit record
• Includes counterfactual explanations for adverse decisions
• Binds Hallucination Flagging scores to specific output spans
• Automates Right to Explanation API responses for data subjects