📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI Governance & Risk Management (AI GRC), from foundations to a certified capstone project.
Landscape
• EU AI Act risk tiers and the obligations that attach to each
• NIST AI Risk Management Framework: govern, map, measure, manage
• ISO/IEC 42001 as a certifiable management system, and how it differs from a framework
• India's DPDP Act and sectoral regulators, and where they bind AI systems
Inventory
• Building a defensible inventory: what counts as an AI system in scope
• Risk tiering by impact, autonomy and reversibility rather than by technology
• Intake and approval gates that do not simply become a rubber stamp
Controls
• SR 11-7 model risk principles and their transfer beyond banking
• Independent validation: what a second line actually tests
• Documentation that survives an audit: model cards, datasheets, intended-use statements
• Human oversight designed so the human can realistically intervene
Assurance
• Pre-deployment evaluation: performance, robustness, bias and misuse testing
• Drift and degradation monitoring with thresholds tied to decisions, not metrics
• AI incident classification, escalation and regulatory notification duties
Programme
• Three lines of defence applied to AI, and where it breaks down
• Third-party and foundation-model risk: what you cannot inspect
• Board reporting, risk appetite statements and evidencing effectiveness
Earn government-registered certification in AI Governance & Risk Management (AI GRC)
e-Certificate and e-Marksheet issued on successful completion.
