Why this matters
As an MLOps Engineer, you will be asked to prove who trained, approved, deployed, and used a model; to reconstruct decisions; and to show that sensitive data was handled properly. Audit logs and governance give you the evidence. They reduce risk, speed up incident response, and make compliance reviews predictable instead of stressful.
- Investigate a model incident by tying alerts to the exact code commit, data version, and person who approved a release.
- Answer a regulator or internal audit request about why a prediction was made on a specific day.
- Detect unauthorized access to a feature store or model registry.
- Demonstrate that inference logs exclude raw PII yet retain enough context to audit fairness and performance.
Concept explained simply
Think of your ML platform as a black box with a flight recorder. The flight recorder (audit logs) automatically writes down who did what, when, where, and why. Governance is the rulebook that says what must be recorded, who can access it, how long to keep it, and how to prove it wasn’t tampered with.
Mental model
- Lifecycle lanes: Data, Training, Model Registry, Deployment, Inference, Monitoring.
- Event types: Access, Change, Approval, Execution, Decision, Alert.
- Standard fields per event: who, what, when, where, why, correlation_id, version_digests, result, sensitivity.
- Controls: retention policy, access reviews, tamper-resistance (append-only), incident playbooks.
What to log across the ML lifecycle
1) Data
- Dataset/feature version identifiers and hashes
- Read/write events: actor, purpose, location
- Sensitivity classification and masking status
2) Training
- Job ID, code commit, container/image digest
- Hyperparameters and config file hash
- Input dataset versions; output model artifact hash
- Who triggered; approval reference; start/end timestamps
3) Model registry
- Register/promote/deprecate/roll back events
- Signer/approver identity and rationale
- Model card version and change notes
4) Deployment
- Environment, model version, infra template hash
- Deployer identity, canary/blue-green details
- Rollback triggers and timestamps
5) Inference
- Request correlation_id, model version
- Feature vector fingerprint (not raw PII)
- Decision, confidence/thresholds, explanation summary
- Consumer system identity and purpose
6) Monitoring
- Alerts: drift, performance, fairness thresholds
- Responder actions: suppress/ack/mitigate
- Evidence links: dashboards/snapshots IDs
Tip: Minimal standard fields to include everywhere
- who: user/service principal
- what: event type and resource
- when: ISO timestamp with timezone
- where: environment/cluster/region
- why: ticket/approval/change request
- correlation_id: to tie related events
- version_digests: hashes for code/data/model
- result: success/failure with error
- sensitivity: data classification
Worked examples
Example 1 — Reproducible training job
Scenario: A quarterly retrain produced worse AUC. You need to prove inputs and reproduce.
- Training event logs: job_id, who, code_commit, container_digest, params_hash, dataset_version, model_artifact_hash, metrics, approval_id.
- Outcome: You correlate to a feature definition edit absent from approval; rollback approved.
Example 2 — Auditable credit decision
Scenario: A customer disputes a loan denial.
- Inference logs: correlation_id, model_version, feature_fingerprint, decision=deny, threshold, top_contributors=[feature names only], consumer_system, request_purpose.
- Outcome: You show decision lineage without exposing raw PII; policy upheld.
Example 3 — Incident triage after drift alert
Scenario: Drift alert fired after a hotfix deploy.
- Deployment logs: model v1.7, canary 10%, deployer, change_request.
- Monitoring logs: drift_score spike, alert_id linked to same correlation_id.
- Outcome: Immediate rollback using deployment event history; postmortem links all evidence.
Governance essentials
- Policies: define what must be logged, where stored, and retention (e.g., training logs 3 years; inference decision logs 1–3 years depending on product risk).
- Access control: least privilege; separate duties (developers vs approvers vs auditors).
- Tamper resistance: append-only storage, write-once retention, immutable buckets or signed logs.
- Data minimization: avoid storing raw PII; use tokens or fingerprints.
- Time sync: NTP or equivalent; reject logs with skew beyond tolerance.
- Reviews: quarterly access reviews and sample audits.
Self-check: Is your log store audit-ready?
- Can you prove a log wasn’t altered? (signatures/versioning)
- Can you answer who approved the last promotion to production?
- Can you trace a decision to code/data/model versions within minutes?
Implementation blueprint
Step 1 — Define event schema
Create a JSON schema covering standard fields; extend per lifecycle lane.
Step 2 — Instrument producers
Training pipelines, registry actions, deployment tool, inference services emit structured logs.
Step 3 — Centralize and secure
Send to a central log store/SIEM with role-based access, encryption, and retention policies.
Step 4 — Correlate
Use correlation_id across events; enrich with model and dataset hashes.
Step 5 — Validate
Automated checks: required fields present; timestamp sanity; schema validation in CI/CD.
Step 6 — Prove immutability
Enable append-only or signed logs; document verification steps in a runbook.
Exercises
Do these in a text editor or notebook. You can check solutions below.
Exercise 1 — Design an audit log schema
Task: Draft a minimal JSON structure for two event types: training_run and inference_request. Include who, what, when, where, why, correlation_id, version_digests, and event-specific fields. Produce one example event for each type.
Exercise 2 — Draft governance rules
Task: Write concise policies for retention, access, approvals, tamper resistance, and data minimization for ML logs. Include responsible roles and a review cadence.
Checklist before you move on
- Your schema has standard fields and event-specific fields.
- Inference logs avoid raw PII but keep enough context to audit decisions.
- You defined retention by event risk level.
- There is a clear approval trail for model promotions.
- Log store is append-only or signed, and you know how to verify.
Common mistakes and how to self-check
- Missing correlation_id: You cannot tie training to deployment and inference. Fix: generate one per release and propagate.
- Logging PII at inference: Risky and usually unnecessary. Fix: store tokens/fingerprints and redact sensitive fields by default.
- No schema validation: Logs vary by team and break queries. Fix: enforce JSON schema at ingestion.
- Over-retention without controls: Expensive and risky. Fix: risk-based retention with automatic purge after approval.
- Undocumented approvals: Hard to prove compliance. Fix: write approvals into the registry and log them with signer identity.
Who this is for
- MLOps Engineers implementing reliable, compliant ML platforms
- Data/ML Engineers integrating pipelines with governance
- Security/Compliance colleagues partnering with ML teams
Prerequisites
- Basic understanding of ML lifecycle (data, training, deployment, inference)
- Familiarity with structured logs (e.g., JSON) and environment variables/secrets
- Knowledge of your organization’s data classification policy
Learning path
- First: Audit Logs and Governance (this lesson)
- Next: Access controls, secrets management, and environment isolation
- Then: Monitoring, drift detection, alerting, and incident response
- Finally: Documentation and internal audit playbooks
Practical projects
- Project 1: Implement a training pipeline that emits a complete lineage event and stores a signed log file. Validate the signature in CI.
- Project 2: Add inference logging to a REST model service with redaction rules and correlation IDs. Demonstrate a full decision trace.
- Project 3: Create an audit readiness runbook that reconstructs a deployment timeline from logs in under 15 minutes.
Mini challenge
You discover a spike in model denials and a support complaint. Using only your defined schema and correlation strategy, outline the 5 log queries you would run to determine if the cause was a data drift, a configuration change, or a code regression.
Quick Test
Take the quick test below. Available to everyone; only logged-in users get saved progress.
Tip: If you miss a question, revisit the exercises and the checklist above.
Good luck!