Implementing the NIST AI Risk Management Framework

As artificial intelligence becomes central to modern software, managing its unique security, ethical, and operational risks is more critical than ever. This course guides you through the industry-standard NIST AI Risk Management Framework (AI RMF) to help you deploy trustworthy AI technologies. You will transition from understanding basic AI risks to actively applying a structured governance framework. You will learn how to identify vulnerabilities, establish organizational policies, and implement continuous monitoring strategies for modern AI deployments, including generative models. What you will learn: Understand the core principles of AI safety, trustworthiness, and the unique risk profile of machine learning systems; Map AI system risks by identifying data dependencies, model vulnerabilities, and potential societal impacts; Measure risk factors using qualitative and quantitative assessment methods aligned with industry standards; Manage deployment risks through practical mitigation strategies, incident response planning, and bias reduction; Govern AI initiatives by establishing clear organizational policies, accountability structures, and ethical guidelines; Analyze modern AI challenges, including generative AI security, prompt injection vulnerabilities, and data privacy concerns. The course begins with foundational definitions of AI risk and the core structure of the NIST framework before moving into step-by-step guidance on mapping, measuring, and managing risks in real-world scenarios. This course is designed for cybersecurity professionals, risk managers, and technology leaders who are new to AI governance, with no prior experience in AI engineering or risk frameworks required. Start reading today to build a proactive strategy for secure and ethical AI deployment.