Definition
Compliance in ML Systems refers to the adherence to established rules, regulations, standards, and ethical guidelines governing the development, deployment, and operation of machine learning models. It ensures that ML systems are built and used in a manner that is secure, fair, transparent, and accountable. This involves implementing robust data governance, privacy controls, bias detection and mitigation strategies, and audit trails throughout the ML lifecycle. Adhering to compliance requirements is crucial for mitigating legal risks, maintaining customer trust, and ensuring the responsible use of AI technology. For instance, GDPR compliance dictates how personal data used for training ML models must be handled and protected. A common point of confusion is conflating compliance with mere security. while security is a component, compliance encompasses a broader set of legal and ethical obligations. This concept is relevant for certifications like CISSP and in professional roles focused on AI governance and cybersecurity.