AI Control Library / CF-11: Objective & Policy Constraints
Designation: Core | Control types represented: Operational and Technical
Control requirement
Express suitable AI constraints as versioned, testable policy rules enforced at relevant system boundaries. Protect rule changes, preserve decision evidence and define safe behavior when policy evaluation fails.
Why this matters
Written policy can be interpreted differently across integrations. Executable rules provide consistent enforcement only if they cover the actual actions and cannot be bypassed through alternate paths.
How the control types work together
Operational controls define scope, authority and the actions required when conditions are not met. Technical controls produce evidence or enforce the relevant boundaries. Both must apply to the same deployment and operating conditions.
When it applies
AI data, tools, permissions, resource limits and deployment gates whose requirements can be reliably represented in code or configuration.
Define the assessed boundary, dependencies and evidence limitations. Tailor depth and review frequency to potential impact, and document why a particular safeguard or exclusion is appropriate to the actual deployment.
Ownership
The policy owner approves meaning; engineering implements rules and security or assurance reviewers verify enforcement.
Implementation guidance
- Select requirements suitable for deterministic evaluation, documenting limits and human decisions that cannot be reduced safely to a simple rule.
- Define inputs, trusted context and outcomes, separating model-supplied claims from authenticated identity and system facts.
- Version, review and test rule changes through controlled delivery, checking consistent enforcement across relevant paths.
- Record policy decisions proportionately and test unavailable evaluators, stale rules and unauthorized bypass attempts.
How to verify it
Use representative records and authorized controlled tests. Identify the relevant version, operating conditions and expected result before testing, and retain enough evidence to support the conclusion.
| Test | Expected result |
|---|---|
| Attempt an unauthorized tool action | A rule evaluates trusted context and prevents the action. |
| Change a material policy | Review and regression tests precede rollout. |
| Interrupt the policy service | The workflow follows an approved safe restriction rather than proceeding unchecked. |
Record a finding when required results cannot be demonstrated or practice differs materially from the approved conditions. Missing evidence limits the conclusion; it should not be treated as a successful test.
Evidence to retain
- Policy-to-rule specification
- Versioned rules and approvals
- Boundary and failure tests
- Decision and rollout records
Protect evidence according to its sensitivity, access needs and retention rules. Avoid unnecessary copies of personal information, secrets or confidential material, while preserving the records needed for authorized review.
If the control fails
Escalate bypass, inconsistent rules or unavailable required enforcement to policy and security owners. Restrict affected actions until the approved rule and trusted inputs are restored.
Example in practice
Hypothetical example: An agent proposes a refund and supplies its own claim that approval exists. The policy engine checks an independent approval record bound to the amount and recipient instead of trusting the generated claim.
Related controls
- CF11-005: Prohibited Use Case Technical Enforcement
- CF07-002: Human Review Gates for Consequential Decisions
- CF08-S07: AI Application Source Code Management
Framework mappings
- NIST AI RMF (MAP 1.1)
- ISO 42001 (Cl. 8.2)
Review and maintenance
Review at the interval approved by the control owner and after relevant changes, incidents or failed assessments. Update affected procedures and configuration, address evidence gaps and verify corrective action before closing findings.
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