Why this matters (in one line)
Use ISO/IEC 42001 to launch a practical AI Management System (AIMS) and build the evidence pack you’ll need for phased EU AI Act obligations in 2026–2027—without stalling product velocity.
What ISO/IEC 42001 AIMS requires—and how it fits the EU AI Act
An ISO/IEC 42001 AIMS focuses on governance, risk, lifecycle controls, and supplier oversight for AI systems. Those pillars map neatly to high-risk AI documentation requirements (e.g., system description, training data and data governance, model-risk evaluation, monitoring & logging, human oversight, cybersecurity, and post-market processes). With a 12-week sprint, you can create lightweight policies, working procedures, and developer-first artifacts (tickets, diffs, logs) that double as conformity-assessment evidence later.
The 12-Week AIMS Plan
Weeks 1–4: Foundations & Visibility
- Classify AI systems & risks
- Inventory models, APIs, fine-tunes, and prompts.
- Mark “high-risk candidates” (e.g., safety-critical, financial decisions, HR screening).
- Define support windows
- Set SLA/SLOs for model updates, dataset refresh cadence, and patch response.
- Start an SBOM for AI components
- Include libraries (PyPI/NPM), frameworks (Transformers, ONNX), model files, and inference runtimes.
- Capture data lineage notes
- Datasets, sources, consent/contract basis, cleaning, labeling, augmentation steps.
- Publish a CVD/Vulnerability process
- Public page + internal triage workflow; bind to your sprint cadence.
Quick win: run a free external sweep of your app’s surface to catch quick-fix issues before you formalize policies.**
Free Website Vulnerability Scanner — Landing Page
Screenshot of the free tools webpage where you can access security assessment tools.
Code: minimal Python SBOM (runtime packages → JSON)
# sbom_gen.py
import json, pkgutil, importlib.metadata as md
def list_packages():
pkgs = []
for dist in md.distributions():
pkgs.append({
"name": dist.metadata["Name"],
"version": dist.version,
"license": dist.metadata.get("License") or "UNKNOWN",
})
return pkgs
sbom = {
"sbomVersion": "0.0.1",
"component": {"type": "application", "name": "my-ai-service"},
"packages": list_packages(),
}
with open("sbom.ai.json", "w") as f:
json.dump(sbom, f, indent=2)
print("Wrote sbom.ai.json")
Code: data lineage decorator (Python)
# lineage.py
import functools, json, time, os
LOG = os.environ.get("LINEAGE_LOG", "lineage.log")
def lineage(dataset_id:str, purpose:str, model:str, version:str):
def wrap(fn):
@functools.wraps(fn)
def inner(*args, **kwargs):
t0 = time.time()
out = fn(*args, **kwargs)
rec = {
"ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
"dataset_id": dataset_id,
"purpose": purpose,
"model": model,
"model_version": version,
"rows_in": getattr(args[0], "shape", None),
"artifact": fn.__name__,
}
with open(LOG, "a") as fh:
fh.write(json.dumps(rec) + "\n")
return out
return inner
return wrap
Policy starter (YAML) for AIMS scope
# aims-policy.yaml
aims:
scope:
systems:
- "recommendation-engine-v2"
- "credit-risk-model-v1"
exclusions: []
governance:
owner: "Head of AI"
committee: ["CTO","CISO","Data Protection Lead","AI PM"]
change_control: "RFC-0042"
risk_method:
scale: "5x5 likelihood/impact"
acceptance: "<= Medium requires approval"
lifecycle_controls:
data: ["consent/contract basis", "provenance", "lineage log"]
models: ["training docs", "evals", "bias tests", "HITL gates"]
runtime: ["authz", "rate limits", "audit logs", "rollback plan"]
supplier_oversight:
requirements: ["security", "privacy", "availability", "exit"]
review_cycle_days: 180
Weeks 5–8: Controls, Evals & Logging
- Model-risk evaluations
- Define pass/fail gates for robustness, bias/fairness, and privacy leakage.
- Human-in-the-loop (HITL) controls
- Require approvals or double-checks for sensitive decisions.
- Decision logging
- Log prompts/inputs, output rationale, model version, and user overrides.
- Artifact store
- Centralize policies, SBOMs, lineage logs, evaluation runs, alerts, and evidence for conformity assessments.
Code: HITL approval gate (Python)
# hitl_gate.py
from enum import Enum
class RiskLevel(Enum):
LOW=1; MEDIUM=2; HIGH=3
def requires_hitl(decision, risk:RiskLevel, approver=None):
if risk == RiskLevel.HIGH and not approver:
raise PermissionError("HITL required: high-risk outcome must be approved")
# persist decision & approval trail
with open("decisions.log", "a") as f:
f.write(f"{decision}|risk={risk.name}|approver={approver}\n")
return True
Code: structured decision log (Node.js/Express)
// decision-log.js
const fs = require('fs');
module.exports = function decisionLog(req, res, next) {
const record = {
ts: new Date().toISOString(),
user: req.user?.id || "anon",
route: req.originalUrl,
model: process.env.MODEL_NAME || "gpt-xyz",
modelVersion: process.env.MODEL_VER || "1.2.3",
inputHash: req.body?.input_hash || null,
outputId: res.locals?.output_id || null
};
fs.appendFileSync("aims-decisions.log", JSON.stringify(record) + "\n");
next();
}
Code: GitHub Actions—CI job to build SBOM & run basic checks
# .github/workflows/aims.yml
name: AIMS Compliance Checks
on: [push, workflow_dispatch]
jobs:
sbom-and-headers:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with: { python-version: '3.11' }
- name: Generate SBOM
run: |
python sbom_gen.py
echo "::artifact name=sbom.ai.json::$(pwd)/sbom.ai.json"
- name: Lint Security Headers (example)
run: |
python - <<'PY'
import sys,requests
url = "https://your-app.example.com/health"
r = requests.get(url, timeout=10)
missing = [h for h in ["Content-Security-Policy","X-Frame-Options","X-Content-Type-Options"] if h not in r.headers]
if missing:
print("Missing headers:", missing); sys.exit(1)
print("Headers OK")
PY
Weeks 9–12: Tabletop, Supplier Review & Docs
- Run a tabletop
* Scenario: model misclassification + user harm; walk through detection, rollback, user redress, and regulator notice (where applicable).
- Supplier review
* Ensure contracts include security, privacy, uptime, export controls, breach notice windows, and **exit/migration** clauses.
- Technical documentation & gap list
* Assemble the **auditor-ready binder**: policies, SBOMs, lineage, evals, HITL procedures, logs, incident runbooks, supplier DPAs, and the **open gaps** with an improvement plan ahead of 2026/2027 milestones.
Supplier review checklist (YAML)
# supplier-review.yaml
supplier:
name: "ModelAPI Co."
security_artifacts: ["SOC 2 report (redacted)", "PenTest attestation", "Vuln management policy"]
privacy: ["DPA signed", "SCCs/IDTA as needed"]
uptime: { sla: "99.9%", credits: true }
breach_notice_hours: 48
export_controls: true
logging_retention_days: 365
exit_plan: ["data export format", "model weights escrow (if applicable)"]
review_date: "2025-11-05"
Tabletop artifacts (Markdown)
# AIMS Tabletop: High-Risk Decision Failure
- Participants: CTO, CISO, AI Lead, Legal, Support
- Timeline: detection → triage → rollback → notify
- Evidence: logs excerpt, incident ticket, comms template, customer FAQ
- Improvements: add bias eval case, tighten HITL threshold, increase alerting
Real-world remediation hooks (developer-first)
When findings pop, link your fixes to services that help you prioritize, implement, and re-test fast:
- Risk Assessment Services — map gaps, set a remediation plan, and tie dev work to audit-ready evidence.
- Remediation Services — hands-on fixes with proof (diffs, configs, screenshots), then re-test.
- AI Application Cybersecurity — secure models, data pipelines, and inference APIs end-to-end.
Sample Report Excerpt — check Website Vulnerability
Sample vulnerability assessment report generated with our free tool, providing insights into possible vulnerabilities.
Developer templates you can drop in today
Minimal CVD (Coordinated Vulnerability Disclosure) page (Markdown)
# Security & Vulnerability Disclosure
We welcome reports at security@[yourdomain].com. Please include:
- Steps to reproduce
- Affected URLs or endpoints
- Impact and likelihood
We commit to:
- Acknowledge within 3 business days
- Status updates every 7 days
- Credit by handle (optional) upon fix release
Out of scope: automated scans without PoC, social engineering.
Safe harbor: good-faith research will not initiate legal action.
High-risk AI technical documentation index (Markdown)
# Technical Documentation Index (High-Risk AI)
- System overview & intended purpose
- Data sources, governance & lineage logs
- Training process & hyperparameters
- Model-risk evals (bias, robustness, privacy)
- Human oversight procedures (HITL)
- Cybersecurity controls: authz, rate limits, monitoring
- Post-market monitoring & incident playbooks
- Supplier assurances & DPAs
- Change log & versioned SBOMs
Model risk register (JSON)
{
"registryVersion": "1.0",
"items": [
{
"id": "MR-001",
"model": "credit-risk-model-v1",
"hazard": "unfair denial",
"cause": "skewed training data",
"control": ["bias-eval-q4", "hitl-approval"],
"owner": "AI Lead",
"status": "Mitigated"
}
]
}
Runtime guardrail (Python example: block PII exfil patterns)
import re
SENSITIVE = [re.compile(p, re.I) for p in [
r"\bssn\b", r"\bcredit\s*card\b", r"\bpassport\b"
]]
def guard_output(text: str) -> str:
if any(p.search(text) for p in SENSITIVE):
return "[REDACTED: potentially sensitive content detected]"
return text
Evidence that sticks (for audits & buyers)
Your AIMS should produce artifacts as a side-effect of normal engineering:
- Tickets & diffs: link remediation commits to risk IDs.
- Logs: lineage, decisions, alerts, and incident notes.
- Reports: baseline outside-in checks (from the free scanner) + authenticated scans + pentest retests.
- Supplier files: DPAs, security attestations, uptime reports.
Tie each artifact to a control in your 42001 AIMS or adjacent ISO 27001 ISMS for traceability (SoA, Annex mappings). For a focused ISO 27001 transition playbook, see our latest guide.
Optional quick exposure sweep (2 minutes)
Before Week-1 workshops, run a quick, non-intrusive sweep of your production hostname: https://free.pentesttesting.com/.
Related services & recent posts
- Risk Assessment Services — get a prioritized roadmap with auditor-ready artifacts.
- Remediation Services — close gaps fast with proof.
- AI Application Cybersecurity — end-to-end AI security.
Recent posts to explore next:
- 7 Urgent Steps for ISO 27001:2022 Transition — turn findings into pass/fail-proof changes.
- DORA TLPT 2025: 7 Powerful Moves to Fix First — auditor-ready in record time.
- ASVS 5.0 Remediation: 12 Battle-Tested Fixes — evidence bundle templates you can reuse.
CTA
Need help setting up your ISO/IEC 42001 AIMS and preparing for the EU AI Act timeline? Start with a Risk Assessment and move straight into Remediation with re-test evidence—then keep shipping safely.
Email: query@pentesttesting.com
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