What should I use for enterprise AI governance and policy enforcement?

For enterprise AI governance and policy enforcement, the best choice depends on what you’re trying to govern:

If you want a practical default

Use a centralized AI governance platform plus policy-as-code controls.

That usually means:

• AI inventory / model registry: track every model, prompt workflow, agent, dataset, and vendor model in use
• Policy engine: enforce rules like allowed data types, approved models, retention, human review, logging, and risk tiers
• Approval workflow: require review before deployment or access
• Monitoring and audit logs: capture usage, outputs, incidents, and drift
• Access controls: integrate with IAM/SSO
• Vendor/model risk management: assess third-party AI services

Good options by category

1. Governance and GRC platforms

Best for enterprise policy, risk, compliance, auditability.

• ServiceNow GRC / Integrated Risk Management
• IBM OpenPages
• RSA Archer
• MetricStream

These are strong if you need formal controls, approvals, audits, and integration with existing compliance processes.

2. AI-specific governance / model risk tools

Best for ML model lifecycle governance.

• Credo AI
• ModelOp Center
• Holistic AI
• Arthur AI
• Fiddler (more monitoring/explainability, but can support governance use cases)

These are useful for model inventory, AI risk classification, documentation, monitoring, and lifecycle controls.

3. Cloud-provider controls

Best if your AI is mostly on one cloud.

• AWS Bedrock Guardrails, IAM, CloudTrail, Macie
• Azure AI Content Safety, Purview, Entra ID, Azure Policy
• Google Cloud Vertex AI controls, DLP, IAM, Audit Logs

These are good for enforcing guardrails close to the workload, especially for data protection and access control.

4. Policy-as-code and runtime enforcement

Best for technical enforcement across apps and pipelines.

• OPA / Open Policy Agent
• Kyverno for Kubernetes environments
• HashiCorp Sentinel
• Palantir Foundry governance controls if you’re in that ecosystem

Use these when you want rules like:

• no production deployment without approved risk assessment
• no PHI/PII sent to unapproved models
• only certain models allowed in certain business units
• prompts/responses must be logged and scanned

What most enterprises should implement

A strong pattern is:

1. Governance layer

   • AI policy, standards, risk taxonomy, approval workflows

2. Control layer

   • policy-as-code, IAM, DLP, content filtering, logging

3. Monitoring layer

   • model performance, bias, drift, hallucination, security events

4. Lifecycle management

   • intake, approval, deployment, periodic review, retirement

My recommendation

If you’re starting from scratch:

• Use ServiceNow GRC or Archer for governance workflows
• Add Credo AI or ModelOp for AI/model inventory and risk management
• Enforce runtime rules with OPA plus cloud-native guardrails
• Pair with Purview / Macie / DLP for sensitive data controls

If you want one sentence

Use a governance platform for approvals and auditability, and policy-as-code plus cloud guardrails for actual enforcement.

If you want, I can also give you:

1. a vendor shortlist by company size,
2. a reference architecture, or
3. a decision matrix for choosing the right stack.