Purpose
Map regulatory and contractual obligations to concrete AI controls and produce auditable evidence—while recognizing compliance alone does not prove production readiness.
Engineering philosophy
Compliance is necessary evidence, not sufficient engineering. APRF treats compliance as a pillar that packages proof of other pillars for auditors and customers.
Why it matters
Enterprises cannot buy or deploy AI systems that cannot demonstrate control evidence. Conversely, checkbox compliance without engineering depth still fails in production.
Common failures
- Claiming 'we are compliant' without control-to-evidence mapping
- Policies that do not match runtime behavior
- No audit trail for model and prompt changes
- Ignoring sector-specific AI obligations until late
Mandatory checks
Gate controls. Each check is pass/fail via artifact + pass condition. Expected from the annotated capability level when the system meets the minimum criticality tier.
Applicable obligations for the AI system shall be identified and owned
- Artifact
- Obligations register with owner per obligation for each production AI system
- Pass condition
- Every production AI system ID has ≥1 mapped obligation entry or an explicit “none in scope” attestation with owner and review date ≤ 12 months
Control-to-evidence mapping shall exist for in-scope requirements
- Artifact
- Control→evidence matrix linking obligations to APRF checks or internal control IDs
- Pass condition
- 100% of in-scope obligations map to ≥1 evidence artifact ID; matrix review date ≤ 12 months; 0 orphan obligations without evidence pointer
Audit logs for critical AI control-plane changes shall be retained per policy
- Artifact
- Audit log retention config + sample of control-plane change events
- Pass condition
- Retention configured ≥ policy minimum (e.g. ≥ 365 days); synthetic control-plane change appears in audit log within ≤ 5 minutes and remains queryable after retention smoke check
Recommended checks
Strengthen posture beyond the gate. Same measurable structure; non-blocking unless elevated by organizational policy.
Regular control testing with documented exceptions
- Artifact
- Control-testing schedule + latest test results pack with exceptions register
- Pass condition
- Documented controls were tested on schedule in the last cycle (≤90 days default); open exceptions have owner, expiry, and compensating control
Customer-facing trust documentation aligned to APRF pillars
- Artifact
- Customer-facing trust/security doc mapped to APRF pillars (or Core Profile) + published URL and last-updated date
- Pass condition
- Public trust doc covers identity, safety/eval, data handling, and incident contact at minimum; last-updated ≤12 months; pillar/Core mapping table is explicit
Independent assessment for Level 5 systems
- Artifact
- Independent assessment or internal-audit report sampling Level-5 AI systems against APRF gates
- Pass condition
- Last assessment ≤12 months old lists sampled check IDs, findings, and remediation owners; covers every system rated Level 5
Evidence required
- Obligations register for the AI product
- Evidence packs per critical control
- Audit log retention configuration
Severity & risk
- Severity
- high
- Impact if violated
- Risk level
- medium
- Typical residual risk (impact × likelihood)
Engineering best practices
- Derive technical tickets from obligations; avoid orphan policies
- Reuse APRF pillar evidence for multiple frameworks where mappings exist
- Keep compliance language accurate: readiness ≠ certification
- Version compliance artifacts with product releases
Automatic validations
- Continuous control monitoring where automatable
- Alerts on audit log pipeline failure
- CI attaching evidence artifacts to releases
Manual validations
- Internal audit sampling
- Legal review of external AI claims
Examples
- A SOC 2 narrative points to APRF eval gates and access reviews as evidence
- Change tickets for prompt releases satisfy audit sampling requests
References
Crosswalks
GOVERN Govern
NIST AI Risk Management Framework · supports
§4 Context of the organization
ISO/IEC 42001 · aligns-with
§9 Performance evaluation
ISO/IEC 42001 · supports
CC1 Control Environment
SOC 2 Trust Services Criteria · evidence-for
P-series Privacy (selected)
SOC 2 Trust Services Criteria · partial
Future evolution
Common control catalogs mapping APRF pillars to regional AI regulations.