AI Output Validation
Added May 2026
Check AI outputs against quality, safety, and format criteria before users or other systems receive them.
Objective
Catch unacceptable AI outputs before they cause harm by applying automated validation rules at the point where outputs leave the AI system.
Maturity Levels
Initial
No output validation exists; all model outputs are passed directly to users or other systems.
Developing
Basic format validation is applied but safety and quality checks are absent.
Defined
Output validation rules covering format, safety, quality, and policy compliance are applied before outputs are used.
Managed
Validation rule coverage is tracked; validation failures are logged and analyzed.
Optimizing
Validation rules are continuously expanded based on the kinds of failures seen in live use; false positive rates (how often valid outputs are wrongly rejected) are measured.
Evidence Requirements
What an auditor or assessor would expect to see for this control.
- —Output validation rule documentation listing checks, rejection criteria, and handling for each output type
- —Test results for the validation process confirming rules correctly identify invalid outputs across a representative test set
- —Validation failure logs from live use showing rejection frequency and categories over a defined period
- —Sample of rejected outputs with reason codes, confirming rules are triggering appropriately
- —Review records for any exceptions to validation rules, with risk acceptance sign-off
Implementation Notes
Key steps
- Define validation criteria per use case: format requirements, prohibited content types, minimum confidence levels (how sure the model is of its answer), and policy compliance checks.
- Apply validation in the software around the model, not in the prompt (the instructions given to the model). Controls inside the model can be bypassed; separate code that checks outputs afterward cannot.
- Build a validation failure queue: outputs that fail validation should be sampled and reviewed to improve rules and identify model weaknesses.
- For structured outputs (JSON data, code, tables), check them against a schema (the expected layout); broken structure often gives early warning that the model is getting worse.
Example Implementation
AI coding assistant generating SQL queries from natural language for a business intelligence platform
Output Validation Rules: SQL Generation Assistant
Validation checks applied before query is executed or shown to user:
| Check | Method | Action on Fail |
|---|---|---|
| Valid SQL syntax | sqlparse AST parse | Block; return "Could not generate a valid query, please rephrase" |
| Schema conformance | Validate table/column names against allowed schema list | Block; return helpful error |
| Prohibited operations | Regex: no DROP, TRUNCATE, DELETE, ALTER, UPDATE | Block; log as policy violation |
| Row limit present | Assert LIMIT clause present | Auto-insert LIMIT 10000; warn user |
| Sensitive table access | Table allowlist per user role | Block; notify user of access restriction |
| Output row count (post-execution) | Assert result < 100,000 rows | Block execution; prompt user to add filters |
Validation failure log: All blocked outputs logged with: user_id, input query, failure reason, timestamp, reviewed weekly for patterns
User experience: Validation failures return a specific, actionable error message rather than a generic "error occurred"