Inference Logging

Records the exact feature vector and preprocessing transformations applied to raw data before it enters the model. This includes:

• Numerical scaling and normalization parameters
• Tokenization maps for text inputs
• Image resizing and channel normalization values
• Missing value imputation logic

Capturing the raw input alongside the transformed features ensures that a prediction can be perfectly reproduced during an audit, eliminating ambiguity about data preparation steps.

Logs the complete probability distribution across all classes, not just the winning prediction. For regression tasks, this includes the raw continuous value and any prediction intervals. Key metadata:

• Softmax scores for classification
• Calibrated confidence estimates
• Anomaly scores from out-of-distribution detectors
• Alternative top-k predictions

Storing the full distribution allows auditors to identify borderline decisions where the model was uncertain, a critical signal for high-stakes applications.

Immutable metadata linking each inference to the exact model artifact, training dataset, and hyperparameters used. This creates a provenance chain that includes:

• Git commit hash of the inference code
• Model registry ID and semantic version
• Training data snapshot identifier
• Feature store timestamp for online features

Without this lineage, a logged prediction is meaningless—you cannot determine if a decision was made by a deprecated, biased, or compromised model version.

Environmental and request-level metadata that frames the operational circumstances of each prediction:

• Timestamp with millisecond precision and timezone
• Request ID for distributed tracing correlation
• Client identifier or session token
• Hardware accelerator type (GPU model, TPU version)
• Latency breakdown by pipeline stage

This context enables SRE teams to correlate model behavior with infrastructure events and trace a single user journey across microservices.

Optional but increasingly mandated: storing the model explainability output generated at inference time. This includes:

• SHAP or LIME feature attribution vectors
• Integrated gradients for neural networks
• Attention weight visualizations for transformers
• Counterfactual explanations for recourse

Embedding explanations directly in the inference log creates a self-contained audit record that demonstrates why a decision was reached, satisfying regulatory requirements like the EU AI Act's transparency mandates.

Inference logs must be written to immutable, append-only storage with cryptographic integrity guarantees. Implementation patterns include:

• Write-Once-Read-Many (WORM) compliant storage volumes
• Merkle tree chaining of sequential log entries
• Periodic blockchain anchoring of root hashes
• Digital signatures using hardware security modules (HSMs)

This transforms the log from a simple record into a non-repudiable forensic artifact that can withstand legal scrutiny and prove no post-hoc tampering occurred.

A specialized audit record capturing every interaction with a machine learning model during the prediction phase. Unlike standard application logs, these records contain:

• Input features and pre-processed tensors
• Raw prompt text and tokenized sequences
• Output predictions with confidence scores
• Token usage and compute duration

These logs are the primary data source for inference logging pipelines, enabling post-hoc auditing of model decisions without altering training data.

A chronological record of inference events that cannot be altered or deleted after creation. This is achieved through:

• Cryptographic hash chaining linking each log entry to its predecessor
• Write-Once-Read-Many (WORM) storage enforcement
• Trusted timestamping from a certified time authority

For inference logging, immutability ensures that model predictions presented during a compliance audit or legal discovery process have not been tampered with post-generation.

A method of tracking a single inference request as it propagates through multiple services using a unique trace ID. In a RAG architecture, a single user query might traverse:

• An embedding generation service
• A vector database retrieval call
• The LLM inference engine
• A post-processing guardrail model

Distributed tracing correlates these disparate logs, enabling latency analysis and root cause identification when model outputs deviate from expected behavior.

The practice of writing inference log entries in a consistent, machine-parseable format like JSON rather than unstructured text. A structured inference event might include:

jsonCopy
{
  "trace_id": "a1b2c3",
  "model_version": "llama-3-70b-v2",
  "input_tokens": 1450,
  "output_tokens": 320,
  "latency_ms": 850,
  "stop_reason": "length"
}

This enables efficient querying, aggregation, and automated analysis across millions of predictions.

The documented history of the origin, custody, and transformations of data used during inference. For a RAG system, provenance tracking answers:

• Which enterprise documents were retrieved for this prediction?
• When were those documents last updated?
• What chunking and embedding strategies were applied?

Combined with inference logging, provenance provides a complete lineage graph showing exactly how a model arrived at a specific output, critical for algorithmic explainability audits.

A methodology using automated systems to perform control assessments on inference logs in near real-time, shifting from periodic sampling to comprehensive verification. Key capabilities include:

• Automated drift detection comparing current prediction distributions to baselines
• Policy violation alerts when outputs contain prohibited content patterns
• Anomaly scoring on input feature vectors to detect adversarial prompts

This transforms inference logging from a passive archival function into an active governance control.