Responsible AI. Zero compromise.

Training data encrypted at rest with AES-256 and municipality-controlled key management Differential privacy techniques applied to training data to prevent model memorization Data lineage tracking records provenance of all training data from source to model Feature store access control with column-level security and row-level filtering Anonymization pipelines transform PII before model training — no personal information in model weights Training dataset versioning with immutable audit trail of all modifications

REST and gRPC inference endpoints secured with OAuth 2.0 and JWT token validation Rate limiting per application and department prevents resource exhaustion Adversarial input detection identifies and blocks manipulated inputs designed to exploit models Model output sanitization prevents information leakage from prediction responses Inference logging captures every prediction with tamper-proof audit trail Canary deployment with automated rollback prevents compromised models from serving production traffic

Mandatory pre-deployment bias testing against protected characteristics — no bypass pathway Explainability engine provides SHAP values and LIME explanations for every prediction type Approval workflows enforce human review gates proportional to model risk classification (Type I–IV) Model cards document intended use, training methodology, performance characteristics, and limitations Citizen appeal mechanism enables human review of any AI-assisted decision affecting individuals Quarterly AI transparency reports auto-generated for council and public accountability

GPU/CPU compute clusters isolated in private network segments with no public internet exposure Container security scanning for all model serving images before deployment Kubernetes workload hardening with pod security policies and network policies Secret management for model credentials, API keys, and data source connections via HashiCorp Vault Multi-factor authentication for all administrative access to AI platform management interfaces Security Information and Event Management (SIEM) integration for all AI platform events

ETL pipeline encryption for data flowing between municipal systems and the feature store Data quality gates validate integrity, completeness, and consistency before feature computation Schema enforcement prevents unexpected data modifications from corrupting model inputs Secure CDC (Change Data Capture) channels maintain encrypted, authenticated data synchronization Pipeline observability with anomaly detection flags unusual data patterns before they reach models Feature drift monitoring detects distribution changes that may indicate data supply chain compromise

Role-based access control with principle of least privilege for all platform functions Segregation of duties — model developers cannot approve their own models for production Immutable audit trail records every model registration, deployment, configuration change, and access event Incident response playbooks for AI-specific scenarios: bias detection, model failure, data breach, adversarial attack Disaster recovery with RPO < 1 hour and RTO < 4 hours for all AI platform services Annual AI-specific penetration testing including model inversion, membership inference, and data poisoning attacks