accuracy-robustness-audit

To audit AI systems for EU AI Act Article 15 compliance, you need to evaluate documentation, security measures, and testing reports to identify gaps in system safety, resilience, and protection strategies.

An AI accuracy and robustness evaluation includes checking documented metrics, benchmarks, and thresholds to validate reliability, alongside assessing system responses to faults, input errors, and operational failures.

Assessing AI cybersecurity defenses involves analyzing protections against adversarial attacks, data poisoning, and model theft to safeguard system integrity and ensure compliance with high-risk deployment standards.

You need a high-risk AI deployment validation and security audit when launching systems in regulated sectors like healthcare diagnostics to review compliance documentation, minimize legal risks, and ensure operational stability.

Yes, you can use this audit approach to check AI resilience and error handling by evaluating system responses to faults and input errors, ensuring stability across various real-world operational environments.