AI and ML Security addresses the novel attack surface introduced when organisations deploy machine learning models — covering training-data poisoning, model inversion, adversarial inputs, and the governance of AI decision pipelines that now influence fraud detection, clinical triage, and autonomous control systems. The EU AI Act, NIST AI RMF, and emerging SEC guidance on algorithmic risk are driving enterprises to treat AI model integrity with the same rigour applied to application security and data governance.
Related: CNAPP · Machine Learning Security · CAASM · Cloud Security Management · GDPR