MLSecOps: Operationalizing Security, Fairness & Privacy in AI Systems

This whitepaper introduces MLSecOps, a practical framework for embedding security, fairness, and privacy into the AI/ML lifecycle. It highlights how techniques like red teaming, input preprocessing, and continuous monitoring can help public sector teams mitigate bias, prevent adversarial attacks, and meet transparency requirements. For government practitioners deploying AI in high-stakes environments, it provides a roadmap to build systems that are resilient, compliant, and trusted by the public.