AI Governance, Risk, and Responsible Deployment

IBM via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

5 weeks, 2 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Part of

IBM Lead AI Transformation: Strategy, Governance & Execution

Overview

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AI is taking on more consequential decisions, increasingly through systems that act on their own. Governance defines whether your enterprise scales up or stalls out. This IBM course builds the structures leaders need to deploy AI responsibly, with no legal or technical background required. Learn why senior-led governance produces measurably better outcomes. Climb the "trust ladder" from approving every action toward auditing systems that have earned autonomy. Identify and classify AI-specific risks—model bias, data, operational, and reputational—and manage them with a framework that registers risks so you know what to expect. Learn to read the shifting regulatory landscape, using the EU AI Act, the NIST AI RMF, and ISO 42001 as reference points and mapping requirements to concrete governance decisions. Tackle what few programs cover: governing agentic AI. Decide how much autonomy to grant and where a human stays in or on the loop, using our Sample Enterprise use cases. As a final project, apply responsible AI principles to a high-stakes case and decide whether it should ship, ship with conditions, or wait. Methods are taught tool-agnostic, with IBM and other tools demonstrated by industry experts.

Syllabus

The AI Governance Imperative
- AI governance is now a core business requirement, not a compliance checkbox, and this module teaches leaders to treat it that way. Learn why enterprise AI governance, risk management, and human oversight decide whether an organization can scale AI safely or stalls between pilot and production. Understand the difference between the oversight a process is designed for and the oversight that actually operates, and how to judge whether a human control is effective. Build a clear, evidence-based oversight posture readout that makes the business case for the controls, monitoring, and accountability responsible AI deployment requires. Grounded in widely used governance frameworks and regulations such as the EU AI Act, the NIST AI Risk Management Framework, and ISO 42001, this module builds the responsible-AI leadership, AI risk, and AI compliance skills that AI, transformation, risk, and technology leaders increasingly need.
Manage AI Risk
- 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.
Read the AI Regulatory Landscape
- 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.
Govern Agentic AI and Autonomy
- 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.
Responsible Deployment in Practice
- 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.