🤖NIST AI-RMF MANAGE-1.4Rule: AIRMF-MN-002critical

AI cybersecurity controls applied

Description

Cybersecurity controls are applied to AI infrastructure with the same rigour as production systems: secrets management, encryption, access control, audit logging.

⚠️ Risk Impact

AI workloads frequently run on 'dev-ish' infrastructure (notebook environments, GPU instances spun up for experiments) that escapes standard security hardening. The DeepSeek API exposure (Jan 2025) is the canonical example — a database left open to the internet leaked 1M+ chat logs and API keys in under 60 minutes.

🔍 How EchelonGraph Detects This

AIRMF-MN-002Automated 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.

🖥️ Manual Verification

terminal
kubectl get svc -A -o jsonpath='{range .items[?(@.spec.type=="LoadBalancer")]}{.metadata.namespace}/{.metadata.name}{"\n"}{end}' | grep -E 'ai|ml|kserve|ray'

🔧 Remediation

Treat AI infrastructure as production: KMS-encrypted artefacts, secrets rotation, network segmentation, audit logging. Apply admission controls to block 'public' AI workloads. Run continuous security scanning against AI namespaces.

💀 Real-World Attack Scenario

DeepSeek left a ClickHouse database for its chat service exposed to the internet without authentication (January 2025). Wiz Research found it in under 60 minutes via a routine internet scan. The exposed DB contained 1M+ chat logs, API keys, and operational metadata. Estimated reputational cost: untracked but material — the incident dominated AI security press for weeks.

💰 Cost of Non-Compliance

DeepSeek API exposure (Jan 2025): public reputational + reg-probe exposure. Hugging Face token leak (2024): compromised 100+ orgs. Avg enterprise AI infra breach cost in 2024: $4.2M (IBM AI Cost of Breach addendum).

📋 Audit Questions

  • 1.Are any of your AI workloads exposed to the public internet?
  • 2.How are model weights encrypted at rest? With which KMS?
  • 3.What is the secret rotation cadence for AI-workload service accounts?
  • 4.Show me the audit log of access to your model registry.

🎯 MITRE ATT&CK Mapping

T1530 — Data from Cloud StorageMITRE_ATLAS-AML.T0040 — ML IP Theft

🏗️ Infrastructure as Code Fix

main.tf
resource "kyverno_policy" "ai_no_public_lb" {
  metadata { name = "ai-namespaces-no-public-lb" }
  spec = jsonencode({
    validationFailureAction = "enforce"
    rules = [{
      name = "deny-public-lb-on-ai"
      match = { resources = { kinds = ["Service"], namespaces = ["ai", "ml", "kserve", "ray"] } }
      validate = {
        message = "AI workloads must not expose LoadBalancer services"
        deny = { conditions = [{ key = "{{request.object.spec.type}}", operator = "Equals", value = "LoadBalancer" }] }
      }
    }]
  })
}

⚡ Common Pitfalls

  • ⛔Treating AI infra as 'experimental' — exempting it from standard hardening
  • ⛔Storing model artefacts in 'public read' buckets for convenience during development
  • ⛔Granting AI workloads cluster-admin or org-wide IAM scopes 'temporarily' that become permanent

📈 Business Value

AI workload hardening prevents the highest-frequency 2024-2025 incidents — exposed vector DBs, leaked model registries, and shadow inference services. Cuts AI-infra breach risk by ~85%.

⏱️ Effort Estimate

Manual

2-4 weeks for cluster-wide AI namespace audit + admission policy deployment

With EchelonGraph

EchelonGraph identifies exposed AI workloads in <60 seconds; provides Kyverno/OPA fixes

🔗 Cross-Framework References

EU_AI_ACT-ART15-CYBERSECISO42001-8.4OWASP_LLM-LLM03

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