EU AI Act Article 15 — Accuracy, Robustness, Cybersecurity

Article 15 requires high-risk AI systems to be designed and developed to achieve an appropriate level of accuracy, robustness, and cybersecurity, and to perform consistently in those respects throughout their lifecycle. Three pillars: (1) accuracy metrics declared in instructions for use, (2) robustness against errors/faults/inconsistencies that may occur within the system or environment, (3) cybersecurity protecting against unauthorized third parties altering use, outputs, or performance.

Article 15(5) specifically addresses 'attempts by unauthorised third parties to alter their use, outputs or performance' — this includes prompt injection, model poisoning, adversarial inputs, model exfiltration, and supply-chain attacks. The technical solutions must be appropriate to the relevant circumstances and the risks. ENISA + the EU AI Office are jointly publishing technical guidance throughout 2026.

Article 15 is AI-system-specific cybersecurity; NIS2 is operator-of-essential-services cybersecurity; CRA is product-with-digital-elements cybersecurity. They overlap heavily for AI systems used in critical infrastructure (energy, transport, health, finance) — most providers will satisfy all three with one evidence pack.

Depends on the AI system class. For classifiers: precision, recall, F1, AUC, calibration. For regression: RMSE, MAE, R². For LLMs: factuality, citation accuracy, hallucination rate. The provider declares relevant metrics in the Article 13 instructions for use; auditors check the metrics match real-world performance within tolerance.

meok-mcp-injection-scan-mcp covers a critical Article 15(5) cybersecurity vector (prompt injection, tool-poisoning). meok-governance-engine-mcp ties accuracy + robustness + cybersecurity to ISO 42001 Annex A controls. /audit-prep-bundle wraps everything in a 14-day signed evidence pack.