Ai Security & Governance: Nist Ai Rmf And Eu Ai Act
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.43 GB | Duration: 7h 54m
Build a real AI governance program: NIST AI RMF, EU AI Act, sector case studies, generative AI, and a full capstone.
Apply the NIST AI Risk Management Framework end-to-end across Govern, Map, Measure, and Manage
Navigate the EU AI Act's risk tiers and identify the obligations that apply to any AI system
Build an enterprise AI governance program from inventory through incident response
Conduct documented risk identification, measurement, and treatment for high-risk AI with audit-ready evidence
Apply governance patterns specific to healthcare, financial services, public sector, and generative AI
Operate a unified compliance program that satisfies multiple regulatory regimes through shared artifacts
Requirements
No prior AI or compliance experience required. Basic familiarity with enterprise IT or risk concepts is helpful but not necessary.
Description
"This course contains the use of artificial intelligence."AI is being deployed everywhere, and the rules are catching up fast. The NIST AI Risk Management Framework gives organizations a structured way to manage AI risk. The EU AI Act turns risk management into a binding regulatory obligation. Sector regulators are layering their own requirements on top. This course gives you the working knowledge to operate inside that landscape.You will learn the four core functions of the NIST framework - Govern, Map, Measure, and Manage - and how they interact in a continuous cycle that produces real evidence. You will learn the EU AI Act end to end: scope and extraterritorial reach, the risk-based approach, the eight prohibited practices, the high-risk tier with both Annex III and Route 1 sectoral product safety, limited-risk transparency obligations, and the parallel general-purpose AI track for foundation models. You will learn how to build an enterprise governance program from scratch: the body, the inventory, risk identification techniques, measurement methods, tolerance and thresholds, continuous monitoring, documentation, and incident response.Three sector case studies - healthcare, financial services, and public sector and law enforcement - show what serious AI governance looks like in regulated environments. A dedicated module covers generative AI's distinctive risks. A unified compliance lecture shows how to satisfy multiple regimes through one program. The capstone walks an end-to-end governance plan for a realistic scenario.By the end, you will be able to classify any AI system against both frameworks, design the controls each tier requires, set thresholds that hold up under audit, run incident response when something goes wrong, and explain it all to a board, a regulator, or an affected customer. The course is for AI risk and compliance professionals, security and privacy leaders, governance officers, ML engineers transitioning into governance, and anyone responsible for AI oversight in regulated industries. No prior AI or compliance experience required.
AI risk and compliance professionals, security and privacy leaders, governance officers, ML engineers moving into governance, and anyone responsible for AI oversight in regulated industries.