Define tiers by potential business impact, user harm, and regulatory exposure:
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Low risk: internal analytics, non-decisioning models with no PII exposure. SLA: best-effort (48–72h incident response). Controls: unit tests, train/validation split, basic fairness checks, model card. Enforcement: pre-deploy CI checks (unit tests, data-schema validation, performance thresholds). Runtime: simple health checks, request/response logging. Monitoring: basic telemetry (latency, error rate).
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Medium risk: customer-facing recommendations or soft-decision models (personalization) with limited PII. SLA: 24–48h incident response, rollback within 4–8 hours. Controls: holdout testing, drift detectors, automated bias metrics, versioned model registry, access control. Enforcement: pre-deploy gating in CI/CD (autotest suite: performance, fairness, explainability summary, adversarial tests). Runtime guards: rate limiting, input validators, feature-range checks, canary deploys. Monitoring: automated alerting on metric degradation, data drift, and unexpected feature distribution shifts; periodic audits.
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High risk: regulatory/financial/health decisions or models that can cause safety/harm. SLA: immediate (4–8 hours) response, automatic rollback within minutes. Controls: strict governance, line-by-line reproducibility, formal validation, thresholded approvals, human-in-the-loop, detailed model card, encrypted PII handling, explainability and counterfactual analysis. Enforcement: pre-deploy: mandatory approval workflow, formal validation suite (statistical tests, stress tests, compliance checks), signed provenance in model registry. Runtime guards: kill-switch, prediction confidence thresholds forcing human review, sandboxing, strict RBAC, input provenance checks. Monitoring: continuous model performance, fairness, concept-drift detectors, replayable audit logs, automated compliance reports and SLA-driven alert escalation (pager).
Programmatic enforcement examples:
- Pre-deploy CI pipeline (YAML) that runs: data-schema validator -> unit/integration tests -> performance & fairness tests -> artifact signing; fail pipeline on any violation.
- Runtime middleware that validates inputs, checks feature ranges, enforces confidence thresholds, logs requests to a streaming store (Kafka) for real-time drift detection.
- Monitoring stack: Prometheus/Grafana for latency/errors, ML-specific monitors (WhyLabs, Evidently) for data/model drift, and an alerting runbook that triggers automated rollback via Kubernetes job or feature-flag disabling when critical SLA breaches occur.
This tiered approach balances speed and control: higher tiers require stronger automated gates, faster SLAs, and more invasive runtime protections.
