Overview
AI Governance Framework wraps the technical controls of Identity for AI into a coherent set of policies, processes, and legal standards. It addresses the "Soft IAM" issues: accountability, ethics, and lifecycle management. It defines the rules of the road for how AI agents are created, approved, monitored, and decommissioned.
Without governance, technical controls are ad-hoc and reactive. Governance provides the strategic alignment for AI adoption.
Architecture
A lifecycle governance model for AI Identities.
Diagram
Key Decisions
- The "Human in the Loop" Policy: Defining which categories of decisions must rely on human approval (e.g., financial transactions > $1000, medical advice).
- Agent Registry: Maintaining a central inventory of all active AI agents, their owners, their purposes, and their identity IDs.
- Kill Switches: Mandating a technical capability to universally suspend an agent's identity instantly across the organization.
Implementation
The Agent Registry
A database or CMDB entry for every AI workload.
- Owner: Product Manager / Lead Dev.
- Purpose: "Customer Support Tier 1".
- Permissions: Read-only on Knowledge Base, Read/Write on Ticket System.
- Risk Level: Medium.
- Expiry: 1 year (requires recertification).
Policy-as-Code (OPA)
Codify governance rules into executable policy.
rego
package ai.governance
deny[msg] {
input.agent.type == "autonomous"
input.action == "delete_database"
not input.human_approval_token
msg = "Autonomous agents cannot delete databases without human approval."
}Risks
- Shadow AI: Departments spinning up AI agents with personal credit cards and API keys, bypassing governance entirely.
- Policy Drift: Governance documents that sit in a PDF and are not enforced technically (Policy-as-Code).
- Accountability Gaps: When an agent causes damage, and it's unclear whether the developer, the prompter, or the model provider is liable. Governance establishes this chain of accountability.