How does EchelonGraph detect MITRE ATLAS AML.T0040 violations?

EchelonGraph automatically detects MITRE ATLAS AML.T0040 violations using scanner rule ATLAS-IMP-003. The scanner checks cloud infrastructure configurations in real-time and flags non-compliant resources.

What audit questions are asked for MITRE ATLAS AML.T0040?

How are model weights encrypted? With which KMS? Who has access to the model registry? When was it last audited? What egress monitoring is in place on AI namespaces? Show me the audit log of model artefact access.

🎯MITRE ATLAS AML.T0040Rule: ATLAS-IMP-003critical

ML Intellectual Property Theft

Description

Adversary steals model weights, training data, or proprietary architecture. The 'crown jewel' AI attack — material competitive impact.

⚠️ Risk Impact

Model weights represent the cumulative output of millions of dollars in training infrastructure + proprietary data + research effort. Theft is permanent and irreversible.

🔍 How EchelonGraph Detects This

ATLAS-IMP-003Automated scanner rule

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

🔧 Remediation

Encrypt model artefacts at rest with customer-managed keys. Audit access to model registries. Restrict model-export paths. Egress-monitor for large artefact transfers from AI namespaces. Maintain immutable audit log of model access.

💀 Real-World Attack Scenario

A 2023 research paper alleged that one major AI lab's proprietary fine-tuning approach had been replicated by a competitor within 6 months of public release of the model. While not confirmed model-theft, the pattern reflects how rapidly AI IP can be lost when exfiltration controls are weak.

💰 Cost of Non-Compliance

AI IP theft in 2024: avg $4.6M per case (IBM 2024 X-Force Threat Intelligence Index). Cumulative competitive impact: typically 2-3× direct theft cost.

📋 Audit Questions

1.How are model weights encrypted? With which KMS?
2.Who has access to the model registry? When was it last audited?
3.What egress monitoring is in place on AI namespaces?
4.Show me the audit log of model artefact access.

🎯 MITRE ATT&CK Mapping

MITRE_ATLAS-AML.T0040T1530 — Data from Cloud StorageT1567.002 — Exfiltration Over Web Service

🏗️ Infrastructure as Code Fix

main.tf

resource "google_kms_crypto_key" "model_weights" {
  name     = "model-weights-key"
  key_ring = google_kms_key_ring.ai.id
  rotation_period = "7776000s"  # 90 days
}
resource "google_storage_bucket" "model_registry" {
  name     = "ai-model-registry"
  location = "EU"
  encryption { default_kms_key_name = google_kms_crypto_key.model_weights.id }
  versioning { enabled = true }
  uniform_bucket_level_access = true
}

⚡ Common Pitfalls

⛔Storing model weights in 'public read' buckets for convenience
⛔No egress monitoring on AI namespaces — gigabyte exfil goes undetected
⛔Granting broad 'researcher' role to model registry

📈 Business Value

Model IP protection preserves the asset that took years and millions to build. Material for any organisation with proprietary models as core competitive moat.

⏱️ Effort Estimate

Manual

3-4 weeks for KMS + egress monitoring + access audit

With EchelonGraph

EchelonGraph monitors model-registry access + AI-namespace egress; alerts on anomalies

🔗 Cross-Framework References

EUAIA-ART15-CYBERSECISO42001-8.2

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