Requirements & constraints:
- Functional: safe, explainable generative AI across products in EU/US/APAC; data residency & regional legal compliance (GDPR, CCPA, PDPA).
- Non‑functional: low-latency, auditable, scalable, 99.9% availability, provable lineage, RBAC.
- Risk targets: acceptable false-positive/negative safety thresholds, SLA for mitigation.
High-level architecture:
- Governance plane (policy engine, model registry, audit log, risk scoring)
- Control plane (deployment gateway, inference proxy, monitoring, data masking)
- Model lifecycle platform (training, validation, provenance tracker, CI/CD)
- Compliance adapters per region (data residency connectors, DPO workflows, legal templates)
Policy & technical controls:
- Central policy catalog with rules: data use, PII handling, allowed prompts, export controls. Policies expressed in machine-readable format (e.g., OPA/Rego).
- Data controls: enforced schemas, synthetic-data use, differential privacy for analytics, automated PII detection & redaction pipeline.
- Model controls: allowed families, pre-approved checkpoints, watermarking & fingerprinting, prompt & response filtering, rate limits and throttling.
- Runtime controls: inference gateway enforces policy decisions, real-time safety filters, explainability hooks (local SHAP/attention maps + user-facing rationale).
- Monitoring: drift detection, toxicity/ hallucination metrics, throughput anomalies, and automated rollback triggers.
Model lifecycle requirements:
- Model registry with versioning, provenance (data snapshots, hyperparams, compute env), signed artifacts.
- Mandatory pre-deploy: functional tests, adversarial robustness, bias audits (metric baseline), privacy risk assessment, ROI & business risk.
- Post-deploy: continuous evaluation, canary releases, periodic re-certification (quarterly), retraining protocols.
Auditability & evidence:
- Immutable audit logs (WORM + signed entries) capturing: data source IDs, training runs, approval flow, policy decisions, inference traces (hashed), model checksum.
- Automated evidence packages for regulators: sanitized datasets, metrics, decision logs, mitigation history.
Roles & responsibilities:
- Product Manager: scope use-cases, acceptable-risk, product-level mitigation, user communication.
- Legal & Compliance: define regional obligations, approve templates, sign-off on high-risk models, maintain DPA & DPIA.
- Engineering/AI: implement controls, run tests, implement model lifecycle, incident response playbooks.
- Security/Privacy: threat modelling, access controls, key management.
- Ethics/Risk Board: periodic review of high-risk deployments, appeals.
18-month prioritized rollout (milestones):
Months 0–3: foundations
- Assemble governance council; define policies; deploy model registry skeleton; region risk inventory.
Months 4–6: core controls
- Implement inference gateway, audit log, policy engine (OPA), PII redaction pipeline; pilot on one product (non-critical).
Months 7–9: validation & regional adapters
- Integrate regional data residency adapters; run full compliance tests (GDPR/CCPA); automate DPIA templates.
Months 10–12: scale & monitoring
- Organization-wide rollout to 50% products; drift & safety monitoring; watermarking/fingerprinting enabled.
Months 13–15: hardening & automation
- Automated re-certification, rollback automation, SOC integration, incident playbooks tested in tabletop exercises.
Months 16–18: optimization & full compliance
- 100% product adoption, regular audit cadence established, external audit & certification, continuous improvement backlog.
Trade-offs & rationale:
- Centralized policy engine gives consistency; regional adapters keep legal flexibility.
- Canary & canary-by-traffic balance rapid innovation and safety.
- Investment in provenance and auditability reduces regulatory risk and speeds approvals.
Key KPIs:
- % of models with full provenance, mean-time-to-mitigation, drift detection lead time, number of compliance incidents, time-to-certify.
This framework lets engineering move fast while providing legal/compliance with the evidence and controls required for multi‑region generative AI deployment.
