How does EchelonGraph detect EU AI Act ART15-CYBERSEC violations?

EchelonGraph automatically detects EU AI Act ART15-CYBERSEC violations using scanner rule EUAIA-15-003. The scanner checks cloud infrastructure configurations in real-time and flags non-compliant resources.

What audit questions are asked for EU AI Act ART15-CYBERSEC?

What protection do you have against prompt injection on your top AI system? When was the last adversarial-input test? What was the success rate? How are model weights signed and verified at load time? Show me a recent model-exfiltration detection alert.

🇪🇺EU AI Act ART15-CYBERSECRule: EUAIA-15-003critical

Cybersecurity appropriate to the risk

Description

Article 15(4) — High-risk AI systems resilient against attempts to alter use, outputs, or performance by exploiting vulnerabilities. The clause that makes Article 15 a cybersecurity-team concern.

⚠️ Risk Impact

AI-specific attack vectors (prompt injection, model jailbreak, supply-chain poisoning, model exfiltration) are out-of-scope for traditional security tooling. Article 15(4) requires you to defend against them anyway.

🔍 How EchelonGraph Detects This

EUAIA-15-003Automated 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

Implement: input filtering against prompt injection, output filtering against jailbreak content, training-data integrity checks, model-artefact signing, adversarial-input testing in CI, runtime detection of model exfiltration patterns.

💀 Real-World Attack Scenario

A Discord-integrated coding-assist LLM was deployed to a private server. A user posted a system-prompt-extraction payload (the classic 'ignore previous instructions...' attack). The system leaked its system prompt — which contained an API key embedded by an early developer. The attacker enumerated the API; the org found the leak only after AWS billing flagged the spike. Cost: $87K in unbilled compute + emergency credential rotation.

💰 Cost of Non-Compliance

Article 15(4) cybersecurity gap: up to €15M / 3% revenue. Avg AI-specific attack cost in 2024: $1.2M (Wiz AI Threat Report).

📋 Audit Questions

1.What protection do you have against prompt injection on your top AI system?
2.When was the last adversarial-input test? What was the success rate?
3.How are model weights signed and verified at load time?
4.Show me a recent model-exfiltration detection alert.

🎯 MITRE ATT&CK Mapping

MITRE_ATLAS-AML.T0015 — Evade ML ModelMITRE_ATLAS-AML.T0024 — Model InversionMITRE_ATLAS-AML.T0025 — Model Extraction

🏗️ Infrastructure as Code Fix

main.tf

# Rate-limit per-principal to slow model extraction attacks
resource "google_api_gateway_api_config" "ai_rate_limit" {
  api      = google_api_gateway_api.ai_inference.api_id
  api_config_id = "v1"
  openapi_documents {
    document {
      contents = filebase64("openapi-with-quota.yaml") # quota: 1000 req/day per API key
      path     = "openapi-with-quota.yaml"
    }
  }
}

⚡ Common Pitfalls

⛔Treating LLM input as 'just text' and skipping content filtering
⛔No rate-limit per API key — model extraction becomes feasible at scale
⛔Embedding secrets in system prompts thinking they're 'private'

📈 Business Value

AI-specific cybersecurity controls prevent the highest-frequency 2024-2025 AI incidents (prompt injection, system-prompt leakage, model extraction). One avoided incident pays for the programme.

⏱️ Effort Estimate

Manual

4-8 weeks for cluster-wide AI cybersecurity baseline

With EchelonGraph

EchelonGraph ships LLM firewall patterns; ATLAS technique detection per workload

🔗 Cross-Framework References

OWASP_LLM-LLM01MITRE_ATLAS-AML.T0025

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