Programme Parameters
Advanced Postgrad
5 Days (7.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This 5-day hands-on course is designed for professionals seeking to understand and implement AI Governance & Risk Management (GRC) frameworks. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 5-day hands-on course is designed for professionals seeking to understand and implement AI Governance & Risk Management (GRC) frameworks.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The Regulatory Map and What It Demands
• 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
Model Inventory and Risk Tiering
• 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
Model Risk Management in Practice
• 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
Testing, Monitoring and Incident Response
• 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
Operating the Function
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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