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GOVERN-1.7 / MANAGE-4.1 NIST AI Risk Management Framework

Provenance + human-review of AI output for critical use

Demonstrate that AI-generated outputs designated as security-critical are traceable to their source models and parameters, undergo documented human validation, and cannot bypass review gates to enter production security workflows.

Description

What this control does

This control ensures that AI-generated outputs used in security-critical decisions or operations are tagged with provenance metadata (model identity, version, timestamp, prompt hash) and subjected to mandatory human review before deployment or action. Organizations maintain an inventory of critical AI use cases, enforce technical guardrails that block unreviewed AI content from production systems, and log all review decisions with reviewer identity and rationale. This prevents over-reliance on potentially hallucinated, biased, or adversarially manipulated AI recommendations in domains such as access provisioning, threat classification, security policy generation, or vulnerability remediation.

Control objective

What auditing this proves

Demonstrate that AI-generated outputs designated as security-critical are traceable to their source models and parameters, undergo documented human validation, and cannot bypass review gates to enter production security workflows.

Associated risks

Risks this control addresses

  • Deployment of hallucinated or factually incorrect AI-generated security policies, firewall rules, or configurations that create exploitable vulnerabilities
  • Adversarial prompt injection causing AI systems to recommend weakening authentication controls or whitelisting malicious domains without human detection
  • Model drift or version regression introducing flawed threat classifications that auto-block legitimate traffic or ignore genuine attacks
  • Lack of accountability when AI-recommended access grants result in privilege escalation or insider threat incidents, with no audit trail of decision provenance
  • Poisoned training data or supply-chain compromise of AI models generating subtly malicious code or infrastructure-as-code templates that evade automated scanning
  • Over-automation of incident response leading to irreversible actions (account lockouts, data deletion) based on false-positive AI detections before human verification
  • Compliance violation when regulatory frameworks require human accountability for security decisions but provenance metadata is insufficient to trace AI involvement

Testing procedure

How an auditor verifies this control

  1. Obtain and review the organization's inventory of AI systems and use-case classifications, identifying all workflows designated as security-critical that incorporate AI-generated outputs.
  2. Select a representative sample of AI-assisted security workflows (e.g., access reviews, vulnerability triage, policy generation) spanning different criticality tiers and model types.
  3. Inspect technical implementation of provenance tagging by examining API responses, database schemas, or output files to verify presence of model name, version identifier, execution timestamp, and input parameter fingerprints.
  4. Review configuration of workflow automation tools, CI/CD pipelines, or security orchestration platforms to confirm technical controls that enforce human approval gates before AI outputs reach production.
  5. Retrieve and analyze human review logs for the sampled AI outputs, verifying each record contains reviewer identity, timestamp, decision rationale, and explicit approval or rejection status.
  6. Conduct walk-through interviews with security analysts or engineers to validate they understand review procedures, can identify AI-generated content via provenance tags, and have authority to override AI recommendations.
  7. Perform negative testing by attempting to promote unmarked or unreviewed AI-generated artifacts (e.g., mock firewall rule, draft IAM policy) through deployment pipelines to confirm rejection by technical controls.
  8. Trace a recent security incident or configuration change backward through logs to verify AI involvement is documented with complete provenance chain and corresponding human review record.
Evidence required Collect provenance metadata samples (JSON logs, database exports, or API response headers showing model identifiers and execution parameters), human review decision logs with reviewer names and timestamps, workflow configuration files or screenshots demonstrating enforcement gates, inventory documentation of critical AI use cases with assigned review requirements, and access control policies restricting deployment privileges. Include attestation from workflow owners confirming technical controls prevent bypass of review steps, and supporting artifacts from negative test attempts showing rejection of unreviewed AI outputs.
Pass criteria All sampled AI-generated outputs designated as security-critical contain complete provenance metadata, demonstrate documented human review with identifiable approvers prior to production use, and technical controls successfully prevent deployment of unreviewed or untagged AI content.

Where this control is tested

Audit programs including this control