ISO/IEC 42001

Map Inkog findings to ISO/IEC 42001:2023 AI Management System requirements.

Roadmap Feature: Full ISO/IEC 42001 clause-level mapping is planned for Q2 2025. Currently, Inkog provides CWE, OWASP, and EU AI Act mappings. The guidance below shows how Inkog findings align conceptually with ISO 42001 requirements.

ISO/IEC 42001 is the first international standard for AI Management Systems. It provides a framework for establishing, implementing, and improving AI governance within organizations.

Overview

ISO/IEC 42001:2023 specifies requirements for an AI Management System (AIMS). Inkog helps organizations meet technical security requirements by detecting vulnerabilities in AI agent code.

Coverage Matrix

ISO 42001 Clause	Requirement	Inkog Coverage
6.1	Risk Assessment	Automated vulnerability detection
6.2	AI System Objectives	Security baseline enforcement
8.2	AI Risk Assessment	Finding severity and CWE mapping
8.4	AI System Security	Code-level vulnerability detection
9.1	Monitoring	CI/CD integration for continuous scanning
10.2	Continual Improvement	Trend analysis via scan history

Clause 6.1: Actions to Address Risks

“The organization shall determine the risks and opportunities that need to be addressed.”

Inkog Support:

Inkog automatically identifies risks in AI agent code:

Risk Category	Inkog Detection	Severity
Resource exhaustion	Infinite loops, token bombing	CRITICAL
Data leakage	Cross-tenant vulnerabilities	CRITICAL
Unauthorized actions	Unsafe code execution	CRITICAL
Prompt manipulation	Prompt injection patterns	HIGH
System availability	Memory overflow, unbounded operations	HIGH

Evidence Collection:


# Generate risk assessment report
inkog scan ./agents -output json > risk-assessment.json
 
# Extract risk summary
cat risk-assessment.json | jq '{
  total_risks: .all_findings | length,
  critical: [.all_findings[] | select(.severity == "CRITICAL")] | length,
  high: [.all_findings[] | select(.severity == "HIGH")] | length,
  risk_categories: [.all_findings[].pattern] | unique
}'

Clause 6.2: AI Objectives and Planning

“The organization shall establish AI objectives at relevant functions.”

Security Objectives Mapping:

Objective	Inkog Metric	Target
Zero critical vulnerabilities	`critical_count`	0
Reduce high severity	`high_count` trend	↓ 20% quarterly
Full coverage	`files_scanned`	100% of AI code
Continuous monitoring	CI/CD integration	All PRs scanned

Tracking Example:


# Track objectives over time
inkog scan . -output json | jq '{
  date: now | strftime("%Y-%m-%d"),
  critical_count: .critical_count,
  high_count: .high_count,
  files_scanned: .files_scanned,
  compliant: (.critical_count == 0)
}' >> objectives-tracking.jsonl

Clause 8.2: AI Risk Assessment

“The organization shall implement an AI risk assessment process.”

Risk Assessment Workflow:

Identify - Inkog scans detect vulnerabilities
Analyze - Severity levels (CRITICAL/HIGH/MEDIUM/LOW)
Evaluate - CWE and CVSS scoring
Treat - Remediation guidance per finding

Finding to Risk Mapping:

Inkog Finding	CWE	CVSS	Risk Level
Infinite Loop	CWE-835	7.5	High
Token Bombing	CWE-400	9.0	Critical
Code Execution	CWE-94	9.8	Critical
Prompt Injection	CWE-77	8.0	High
Data Exposure	CWE-200	7.0	High

Clause 8.4: AI System Security

“The organization shall implement security controls for AI systems.”

Control Categories:

8.4.1 Secure Development

Inkog enforces secure coding practices:

Non-Compliant

Violates 8.4 - unbounded AI execution

# No iteration limits
agent = Agent(tools=tools)
agent.run(user_input)

Compliant

Meets 8.4 - controlled execution boundaries

# Bounded execution
agent = Agent(
  tools=tools,
  max_iterations=15,
  timeout=60
)
agent.run(validated_input)

8.4.2 Input Validation


# Inkog detects unvalidated inputs
# Finding: "User input directly in LLM prompt"
# Remediation: Implement input validation
 
def compliant_handler(user_input):
    validated = sanitize_input(user_input)
    validate_format(validated)
    return process_query(validated)

8.4.3 Output Handling


# Inkog detects unsafe output handling
# Finding: "LLM output used without validation"
# Remediation: Validate before use
 
def compliant_output_handler(llm_response):
    parsed = parse_response(llm_response)
    validated = validate_output_schema(parsed)
    sanitized = sanitize_for_display(validated)
    return sanitized

Clause 9.1: Monitoring, Measurement, Analysis

“The organization shall determine what needs to be monitored.”

Continuous Monitoring Setup:


# GitHub Actions for continuous monitoring
name: ISO 42001 Compliance Scan
 
on:
  schedule:
    - cron: '0 0 * * *'  # Daily
  push:
    branches: [main]
 
jobs:
  compliance-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
 
      - name: Run Inkog Scan
        run: |
          docker run -v ${{ github.workspace }}:/scan \
            ghcr.io/inkog-io/inkog scan /scan \
            -output json > compliance-report.json
 
      - name: Check Compliance
        run: |
          CRITICAL=$(jq '.critical_count' compliance-report.json)
          if [ "$CRITICAL" -gt 0 ]; then
            echo "Non-compliant: $CRITICAL critical findings"
            exit 1
          fi
          echo "Compliant: No critical findings"
 
      - name: Archive Evidence
        uses: actions/upload-artifact@v4
        with:
          name: iso42001-evidence-${{ github.run_number }}
          path: compliance-report.json

Clause 10.2: Continual Improvement

“The organization shall continually improve the suitability and effectiveness of the AIMS.”

Improvement Tracking:


# Track improvement over releases
for version in v1.0 v1.1 v1.2; do
  git checkout $version
  inkog scan . -output json | jq "{
    version: \"$version\",
    total: .findings_count,
    critical: .critical_count,
    high: .high_count
  }"
done | jq -s '.'

Expected Output:


[
  {"version": "v1.0", "total": 15, "critical": 3, "high": 5},
  {"version": "v1.1", "total": 8, "critical": 1, "high": 3},
  {"version": "v1.2", "total": 4, "critical": 0, "high": 2}
]

Generating Evidence

Use scan results as evidence for ISO 42001 audits:


# Generate scan results
inkog -path ./agents -output json > scan-results.json
 
# View summary
cat scan-results.json | jq '{
  files_scanned: .files_scanned,
  findings_count: .findings_count,
  critical_count: .critical_count,
  high_count: .high_count
}'

A dedicated evidence package generator is on our roadmap. Currently, use JSON output for audit documentation.

Compliance Checklist

Requirement	Inkog Feature	Status
Risk identification	Vulnerability scanning	✓
Risk severity assessment	CRITICAL/HIGH/MEDIUM/LOW	✓
CWE classification	CWE IDs per finding	✓
Remediation guidance	Fix suggestions	✓
Continuous monitoring	CI/CD integration	✓
Evidence generation	JSON/HTML reports	✓
Trend analysis	Historical comparison	✓

Best Practices

Integrate into CI/CD for continuous compliance
Archive all scan results as audit evidence
Set zero tolerance for critical findings
Track trends quarter-over-quarter
Document remediation for each finding
Review periodically with security team