📐ISO/IEC 42001 42001-8.4Rule: ISO42001-8-003medium

AI image registry policy

Description

Clause 8.4 — Data and software components sourced from approved registries; integrity verified.

⚠️ Risk Impact

AI workloads pulling from arbitrary container registries (or arbitrary HuggingFace repos) bring in unverified code + weights — direct supply-chain attack vector.

🔍 How EchelonGraph Detects This

ISO42001-8-003Automated scanner rule

EchelonGraph's Tier 1 Cloud Scanner automatically checks for this condition across all connected cloud accounts. Violations are flagged as medium-severity findings with remediation guidance.

🖥️ Manual Verification

terminal
cosign verify --certificate-identity-regexp 'https://github.com/your-org' ghcr.io/your/model:tag

🔧 Remediation

Use admission policy (Kyverno / OPA Gatekeeper) to restrict imageRegistry field. Sign images with cosign. Verify model-weight signatures where available (e.g. HuggingFace verified providers).

💀 Real-World Attack Scenario

A data-science team pulled a 'community fine-tune' from a random HuggingFace user. The model included a backdoor trigger that activated on specific input patterns, exfiltrating data via DNS. Detected only via outbound traffic analysis 5 weeks later.

💰 Cost of Non-Compliance

AI supply-chain compromise: avg $4.6M (IBM 2024). HuggingFace token-leak exposure (2024): 100+ orgs.

📋 Audit Questions

  • 1.Show me the admission policy restricting AI container registries.
  • 2.How are model weights verified at load time?
  • 3.What is your approved-registry list?
  • 4.When was an unverified model last blocked?

🎯 MITRE ATT&CK Mapping

T1195.002 — Compromise Software Supply ChainMITRE_ATLAS-AML.T0010 — ML Supply Chain Compromise

🏗️ Infrastructure as Code Fix

main.tf
resource "kyverno_policy" "approved_ai_registries" {
  metadata { name = "ai-approved-registries-only" }
  spec = jsonencode({
    validationFailureAction = "enforce"
    rules = [{
      name = "restrict-image-registry"
      match = { resources = { kinds = ["Pod"], namespaces = ["ai", "ml", "kserve"] } }
      validate = {
        message = "AI workload images must come from approved registries"
        pattern = { spec = { containers = [{ image = "ghcr.io/your-org/* | gcr.io/your-project/* | huggingface.co/verified/*" }] } }
      }
    }]
  })
}

⚡ Common Pitfalls

  • ⛔No admission policy — images pulled from anywhere
  • ⛔Approved registries that include public registries by default
  • ⛔Skipping signature verification because 'we trust HuggingFace'

📈 Business Value

Approved-registry policy prevents the highest-frequency AI supply-chain attack vector. One avoided incident pays for the programme.

⏱️ Effort Estimate

Manual

1-2 weeks for admission-policy deployment

With EchelonGraph

EchelonGraph ships pre-built Kyverno/OPA policies + signature verification per workload

🔗 Cross-Framework References

EUAIA-ART15-CYBERSECMITRE_ATLAS-AML.T0010

Automate ISO/IEC 42001 42001-8.4 compliance

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