AI Model Registry and Lifecycle Governance

We deploy a secure, centralized registry that automatically tracks every model's origin, training data, versions, and deployment path. This eliminates shadow AI by providing a single source of truth, enabling instant audit trails for compliance with frameworks like NIST AI RMF and ISO/IEC 42001.

We implement automated governance checks within your CI/CD pipeline. Models cannot progress to staging or production without passing security scans, bias audits, and data privacy validations, enforcing your EU AI Act risk categorization automatically.

We integrate fine-grained, role-based access controls (RBAC) for model artifacts, training data, and inference endpoints. All access, modifications, and deployments are immutably logged, providing the granular audit trail required for SOC 2 and internal security reviews.

We instrument deployed models for real-time performance, data drift, and concept drift. The system triggers automated alerts and supports one-click rollback to a previous, stable model version, drastically reducing mean time to recovery (MTTR) for AI incidents.

We empower your ML engineers with a compliant, self-service portal for model registration, experimentation, and promotion. This accelerates development cycles while keeping all activity within the governed framework, directly addressing the root cause of shadow AI.

We deploy a secure, version-controlled registry (MLflow, Kubeflow) that tracks every model's lineage, training data, hyperparameters, and performance metrics. This provides immutable audit trails for compliance with NIST AI RMF and ISO/IEC 42001.

We implement policy-as-code to enforce automated checks for bias, security, and performance before a model can progress from development to staging to production. This prevents unauthorized or non-compliant deployments.

Our registry integrates directly with your AI-SPM platform (Wiz, Laminar) to feed model metadata into centralized risk scoring and compliance dashboards. This unifies visibility across sanctioned and unsanctioned AI, closing the loop with our Shadow AI Detection and Security Posture Management services.

We implement role-based access control (RBAC) with fine-grained permissions for model viewing, promotion, and rollback. All actions are logged to immutable storage, providing the audit trail required for internal audits and regulatory demonstrations.

We instrument deployed models to feed performance, data drift, and concept drift metrics back into the registry. This creates a closed-loop governance system where models can be automatically flagged for retraining or rollback based on objective criteria.