AI Control Library / CF-11: Objective & Policy Constraints
Designation: Core | Control types represented: Operational and Technical
Control requirement
Enforce explicit output contracts before AI-generated results are consumed, validating syntax, required fields, types, allowed values and relevant semantic or authorization conditions. Handle invalid or partial results safely.
Why this matters
Valid JSON is not proof that an amount, recipient or action is authorized. Structure validation is necessary for reliable integration but must connect to the downstream meaning and consequences.
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-generated structured records, API arguments, classifications and other outputs consumed by software or controlled workflows.
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 consuming application owner defines contracts with domain and security reviewers; engineering enforces them.
Implementation guidance
- Define versioned schemas and meaning, including required fields, permitted values, ranges and unknown-field treatment.
- Validate independently of the model, using constrained generation where helpful without treating it as complete assurance.
- Check semantic relationships and authorization before consequential use, separating data validation from execution rights.
- Test malformed, partial, extra-field and valid-but-unauthorized cases, controlling retries and downstream side effects.
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 |
|---|---|
| Return structurally invalid output | The consumer rejects or safely handles it. |
| Return valid structure with an unauthorized value | Independent checks prevent the consequence. |
| Retry a failed parse | The workflow does not duplicate an already completed external action. |
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
- Output schema and semantic rules
- Validator and authorization configuration
- Negative and retry tests
- Version and error-handling 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
Block unsafe consumption and notify the application owner. Investigate contract mismatches and reconcile external effects before retry or recovery.
Example in practice
Hypothetical example: A payment object has all required fields, but its recipient is outside the approved list. Semantic and authorization checks deny the action despite successful schema validation.
Related controls
- CF11-003: AI Output Validation & Filtering
- CF12-011: AI Output Handling Security
- CF07-002: Human Review Gates for Consequential Decisions
Framework mappings
- CSA AICM
- NIST AI RMF (MEASURE 2.5)
- OWASP LLM Top 10
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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