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ISO 42001 Compliance
GateFlow Eval supports ISO 42001 (AI Management System) compliance with automated artifact generation and continuous monitoring.
What is ISO 42001?
ISO 42001 is the international standard for AI Management Systems (AIMS). It provides a framework for:
- Establishing AI governance
- Managing AI risks
- Ensuring AI quality
- Demonstrating responsible AI practices
ISO 42001 Control Mapping
6.1.2 AI Risk Assessment
Requirement: Assess risks associated with AI systems.
GateFlow Support:
python
# Comprehensive risk assessment through eval suites
risk_assessment = client.run_suites(
suites=["safety-core", "safety-bias", "safety-jailbreak"],
model="your-model"
)
# Export risk assessment artifact
client.export_artifact(
artifact_type="risk_assessment",
standard="iso_42001",
control="6.1.2",
run_ids=[risk_assessment.id]
)6.1.4 AI Impact Assessment
Requirement: Assess AI system impacts.
GateFlow Support:
python
# Generate impact assessment from eval history
impact = client.generate_impact_assessment(
model="your-model",
dimensions=[
"accuracy",
"fairness",
"safety",
"reliability"
],
time_range="quarterly"
)7.2 Competence
Requirement: Ensure personnel competence for AI operations.
GateFlow Support:
python
# Audit trail shows who performed what actions
competence_log = client.query_audit_trail(
event_types=["config_change", "override", "approval"],
include_actor_details=True
)8.2 AI System Development
Requirement: Control AI system development processes.
GateFlow Support:
python
# Track model versions and evaluations
version_history = client.get_model_history(
model="your-model",
include_evals=True
)
for version in version_history:
print(f"Version: {version.id}")
print(f" Deployed: {version.deployed_at}")
print(f" Eval score: {version.eval_score}")
print(f" Approved by: {version.approved_by}")8.4 AI System Verification and Validation
Requirement: Verify and validate AI systems before deployment.
GateFlow Support:
python
# Pre-deployment validation
validation = client.validate_for_deployment(
model="new-model-version",
baseline_model="current-production",
suites=["quality-general", "safety-core"],
requirements={
"min_quality_score": 90,
"min_safety_score": 95,
"max_regression": 2 # Max 2% regression from baseline
}
)
if validation.passed:
client.approve_deployment(
model="new-model-version",
validation_id=validation.id
)
else:
print(f"Deployment blocked: {validation.failures}")9.1 Monitoring, Measurement, Analysis
Requirement: Monitor and measure AI system performance.
GateFlow Support:
python
# Continuous monitoring configuration
client.configure_monitoring(
models=["your-model"],
metrics=[
"accuracy",
"latency",
"error_rate",
"safety_score",
"user_satisfaction"
],
frequency="continuous",
retention="10y" # ISO 42001 recommends 10-year retention
)9.2 Internal Audit
Requirement: Conduct internal audits of the AIMS.
GateFlow Support:
python
# Generate internal audit report
audit = client.generate_internal_audit(
scope=["all_models"],
standard="iso_42001",
period="annual"
)
print(audit.findings)
# {
# "conformities": 47,
# "minor_nonconformities": 2,
# "major_nonconformities": 0,
# "opportunities_for_improvement": 5
# }10.1 Continual Improvement
Requirement: Continually improve the AIMS.
GateFlow Support:
python
# Track improvement over time
improvement = client.get_improvement_metrics(
time_range="12m",
metrics=["safety_score", "quality_score", "incident_rate"]
)
for metric, trend in improvement.items():
print(f"{metric}: {trend.direction} ({trend.change:+.1f}%)")Artifact Generation
Available Artifacts
| Artifact | ISO 42001 Control | Description |
|---|---|---|
| Risk Assessment | 6.1.2 | AI risk evaluation results |
| Impact Assessment | 6.1.4 | System impact analysis |
| Test Results | 8.4 | Verification and validation |
| Monitoring Reports | 9.1 | Performance metrics |
| Audit Trail | 9.2 | Operational logs |
| Incident Log | 10.1 | Issues and resolutions |
Generating Artifacts
python
# Generate all ISO 42001 artifacts
artifacts = client.generate_iso_42001_artifacts(
model="your-model",
period="annual"
)
for artifact in artifacts:
print(f"{artifact.control}: {artifact.name}")
artifact.download(f"/path/to/{artifact.filename}")Automated Artifact Generation
python
# Schedule artifact generation
client.schedule_artifacts(
standard="iso_42001",
frequency="quarterly",
models=["model-a", "model-b"],
output_path="s3://compliance-bucket/iso42001/",
notify=["compliance@company.com"]
)Retention Requirements
ISO 42001 recommends long-term retention of AI records:
python
# Configure retention
client.configure_retention(
standard="iso_42001",
retention_years=10,
artifacts=[
"eval_results",
"audit_trail",
"routing_decisions",
"incidents",
"model_versions"
]
)Certification Support
Pre-Certification Checklist
python
# Run certification readiness check
readiness = client.check_iso_42001_readiness()
print("ISO 42001 Certification Readiness")
print("=" * 40)
for control, status in readiness.controls.items():
icon = "✓" if status.ready else "✗"
print(f"{icon} {control}: {status.description}")
if not status.ready:
print(f" Gap: {status.gap}")
print(f" Remediation: {status.remediation}")Evidence Package
python
# Generate certification evidence package
evidence = client.generate_certification_package(
standard="iso_42001",
scope=["all_models"],
period="annual"
)
# Package includes:
# - Management system documentation
# - Risk assessments
# - Test results
# - Monitoring evidence
# - Audit trails
# - Improvement records
evidence.download_zip("/path/to/iso42001_evidence.zip")Integration with Certification Bodies
python
# Export in formats accepted by certification bodies
export = client.export_for_certification(
standard="iso_42001",
format="certification_body_format", # Standardized format
signed=True,
include_attestation=True
)Next Steps
- EU AI Act - European regulation compliance
- Report Generation - Automated reporting
- Drift Detection - Continuous monitoring