AI Governance, Risk & Responsible AI

Understand what organizations actually need to govern.

• AI, machine learning, Generative AI and AI agents
• How enterprise AI systems are used
• AI lifecycle
• Organizational AI use cases
• Shadow AI
• AI system inventory
• Ownership and accountability
• Governance roles and decision rights
• Approved and prohibited uses
• Governance operating models

Practical work: Create an AI inventory for a sample organization.

Deliverable: Enterprise AI Inventory & Responsibility Map

Identify where AI can create business, legal, operational or customer risk.

• Accuracy
• Hallucinations
• Bias and fairness
• Privacy
• Security
• Intellectual property
• Confidential information
• Explainability
• Transparency
• Human oversight
• Reputational risk
• Model and operational risk
• Risk classification
• Risk scoring
• Human approval requirements

Practical work: Assess multiple AI use cases and classify their risks.

Deliverable: AI Risk Register & Control Matrix

Apply major governance frameworks to practical organizational decisions.

• NIST AI Risk Management Framework
• ISO/IEC 42001
• AI management systems
• Governance policies
• Roles and committees
• Risk ownership
• Control selection
• Documentation
• Governance evidence
• Internal review processes

Practical work: Map a company's AI governance structure against major framework requirements.

Deliverable: AI Governance Framework Map

Build a working understanding of regulatory requirements.

• EU AI Act
• Risk-based AI regulation
• GDPR intersections
• India Digital Personal Data Protection Act
• Indian AI governance direction
• Data privacy
• Personal data
• Sensitive business information
• Copyright and AI-generated content
• AI transparency requirements
• Record keeping
• Sector-specific considerations
• Legal counsel and governance responsibilities
• BFSI
• Healthcare
• HR
• Marketing
• Education
• Customer service

Examples from BFSI, healthcare, HR, marketing, education and customer service. Apply requirements relevant to the organization and review current legal sources with counsel.

Practical work: Review AI use cases against regulatory and organizational requirements.

Deliverable: AI Compliance & Regulatory Assessment

Set practical rules for employee and enterprise Generative AI use.

• Employee use of public AI tools
• Approved vs unapproved accounts
• Prompt and file handling
• Confidential data
• Customer data
• Training-data concerns
• Data retention
• Copyright
• Generated content
• Hallucination risk
• Human verification
• RAG and organizational knowledge
• Enterprise GenAI deployment
• Access control
• Usage monitoring
• Employee AI policies

Systems discussed: ChatGPT, Microsoft Copilot, Claude, Gemini and enterprise Generative AI platforms.

Practical work: Create company rules governing employee Generative AI usage.

Deliverable: Generative AI Acceptable Use Policy + Employee AI Usage Guide

Assess AI vendors before approval and throughout their use.

• Selecting AI vendors
• Vendor questionnaires
• Model providers
• Data storage
• Retention
• Training on customer data
• Security
• Privacy
• IP ownership
• Sub-processors
• Service availability
• Model changes
• Monitoring
• Exit plans
• Contractual considerations
• Vendor risk scoring

Practical work: Assess two AI vendors for organizational adoption.

Deliverable: AI Vendor Assessment & Approval Checklist

Control AI agents that access company tools, data and systems.

• What AI agents are
• Agents vs assistants
• Tool access
• Database access
• Email access
• Financial actions
• External actions
• Permissions
• Role-based access
• Action limits
• Human approval gates
• Agent identity
• Audit trails
• Memory
• Logging
• Monitoring
• Escalation
• Emergency suspension
• Multi-agent systems
• Agent-to-agent actions
• Accountability

Control chain: User → AI Agent → Tool → Data → Proposed Action → Human Approval → Execution → Logging → Monitoring. Required approval must happen before execution; record approvals, denials and failures.

Practical work: Design governance controls for an AI agent that can access company systems.

Deliverable: AI Agent Permission & Human Approval Matrix

Turn governance into an operating process.

• Pre-deployment AI testing
• Acceptance criteria
• Hallucination testing
• Bias checks
• Reliability checks
• Red-team exercises
• Monitoring
• User complaints
• Failure reporting
• Incident classification
• Containment
• Suspension
• Root-cause review
• Corrective actions
• Governance reviews
• AI governance board
• Policy review cycles
• Management reporting
• Organizational rollout

Practical work: Create an enterprise AI governance implementation plan.

Deliverable: Enterprise AI Governance Program

The professional program runs for 8 weeks. Organizations can request customized half-day to 4-day workshops based on their needs.

No. The main program focuses on governance, risk, controls, policies and organizational implementation.

No. Responsible AI is one part of the program. The course also covers risk management, regulation, GenAI governance, AI vendors, AI agents, monitoring, incident management and enterprise implementation.

Yes. AI agent permissions, system access, action limits, human approval, audit trails and monitoring form a dedicated module.

The program covers knowledge areas used in AI governance roles and certifications. It is not an IAPP-authorized course or official AIGP preparation program.

Yes. Corporate programs can be built around approved organizational AI systems, policies, workflows and risk scenarios.

No. The program teaches governance working methods. Legal policies and regulatory interpretations should be approved by qualified organizational or external counsel.