How do you tell a real AI control from one that only looks reassuring? This module builds practical AI risk management skill for leaders: identify and classify AI-specific risks — model bias, data and privacy risk, security and misuse, operational and human-factors risk, supplier and third-party risk, and reputational risk — and apply a simple risk-register framework to a real use case. You will learn to distinguish inherent risk from residual risk, test whether a proposed control actually reduces the exposure, evaluate human-oversight and monitoring controls under real operating conditions, and support a residual-risk rating with evidence. Grounded in recognized AI risk-management and governance frameworks and responsible-AI practice, the module turns diffuse, unowned AI risk into a risk register that is visible, assignable, and defensible — no coding background required.

How do you stay compliant when AI regulation changes faster than your systems do? This module builds a practical skill for AI leaders, risk and compliance professionals, and legal and governance teams. It teaches how to read the AI regulatory landscape and keep pace with shifting AI laws, regulations, and policy. Learners scope which instruments and legal actors — provider, deployer, importer, distributor — a given AI use touches. They classify high-risk and prohibited uses under frameworks such as the EU AI Act. And they translate a regulatory obligation into a decision, a control, an owner, and a date. Grounded in primary law, recognized standards including the NIST AI Risk Management Framework and ISO/IEC 42001, and responsible-AI practice, the module teaches a repeatable regulatory reading discipline: source verification, actor mapping, auditable compliance claims, and dated evidence learners can defend to an auditor, a customer, or a board. No legal or coding background required.

This module builds a practical AI governance skill for AI and transformation leaders, risk and compliance professionals, and technology and operations teams: how to govern agentic AI, autonomous AI agents, and machine autonomy inside the enterprise. It covers how to define what actions an AI agent is permitted to take, assess consequence, exposure, and recoverability, place preventive human oversight and authorization controls before unacceptable effects occur, and design attributable identity, audit trails, least-privilege access, monitoring, interruption, and recovery. Grounded in international cyber-agency guidance, primary regulatory text such as the EU AI Act, and responsible-AI practice, the module teaches a repeatable AI authorization discipline — an agent authorization summary and action register a leader can defend to an auditor, a regulator, a customer, or a board. No coding background required.

This module helps senior leaders, product and program owners, risk and compliance professionals, and data and analytics leads decide whether a high-stakes AI model should ship, ship with conditions, or wait. Learners translate responsible AI principles and AI ethics into an operational deployment gate. They rate each gate item pass, flag, or block against evidence. They interpret bias and fairness findings using named fairness metrics, internal risk thresholds, and applicable law. They also write enforceable go-live conditions, test whether human oversight can work at real operating volume, and capture the decision in a defensible deployment decision record. The module draws on AI governance and risk management practice, primary regulation including the EU AI Act and U.S. credit law, recognized frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001, and IBM's Pillars of Trustworthy AI. No legal or coding background is required.