AI Governance and Model Risk Platform (Multi-Cloud)

Overview

An AI governance platform gives an organization a single, authoritative view of every AI and ML system it runs, the risks each one carries, and the controls that keep them compliant. As regulations like the EU AI Act and frameworks like NIST AI RMF take hold, enterprises must prove that models are inventoried, reviewed, and monitored. This platform is deliberately multi-cloud because models live across AWS, Azure, GCP, and on-prem, and governance must span all of them.

Components

• Model registry: a central inventory of every model, its owner, purpose, risk tier, data sources, and lifecycle stage.
• Policy engine: evaluates each model and use case against organizational and regulatory policies, blocking or flagging non-compliant deployments.
• Workflow engine: drives risk assessments, approvals, and periodic reviews with assigned reviewers and deadlines.
• Identity provider: ties every action to a person and role for accountability.
• PostgreSQL + object storage: store governance metadata, evidence, model cards, and evaluation artifacts.
• SIEM: aggregates audit logs from all clouds for tamper-evident records.

Data Flow

When a team registers a model, the platform classifies its risk tier from its purpose and data. The policy engine checks required controls (bias testing, documentation, human oversight) and routes the model through the workflow engine for assessment and approval. Connectors pull deployment and monitoring signals from each cloud's ML platform so the inventory stays accurate. All decisions, evidence, and changes flow to the SIEM as an immutable audit trail used for internal and regulatory reporting.

Scaling and Resilience

The platform itself is modest in compute but must be highly available because it gates deployments; run it active across regions with a replicated database. Cloud connectors poll or subscribe to events from each provider and degrade gracefully if one cloud is unreachable, queuing updates for later reconciliation. Keep the audit store append-only and replicated. Version policies so a model can be evaluated against the rules in force when it was approved.

Security

Governance data is sensitive and high-value: enforce least privilege, strong authentication, and separation of duties so no one approves their own model. Encrypt everything and keep audit logs tamper-evident. Restrict who can change policies, and log policy changes themselves. Because the platform reaches into every cloud, scope its credentials tightly and rotate them. Protect model cards and evaluation evidence that may reveal sensitive system details.

Trade-offs and Alternatives

A central governance platform creates consistency and a single audit trail, but it can become a bottleneck if reviews are heavy; tier the process so low-risk models move fast and only high-risk ones get deep scrutiny. Building in-house gives control and multi-cloud reach; commercial GRC and model-governance tools speed adoption but may not cover every cloud equally. The hardest part is cultural, not technical: governance only works if teams register models, so make registration the easy default path to deployment.