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Artificial Intelligence

AI Governance Professional

The Artificial Intelligence Governance Professional (AIGP) Training is designed for professionals responsible for governing, managing, developing, deploying, auditing, and overseeing Artificial Intelligence systems. This comprehensive AIGP training program enables participants to understand the foundations of AI governance, responsible AI principles, AI risk management, privacy requirements, global AI regulations, the EU AI Act, AI standards and frameworks, AI development governance, and responsible AI deployment.

Practical AI Governance Case Studies
Coverage of Responsible and Trustworthy AI
Understanding of the EU AI Act
Coverage of AI Laws and Privacy Requirements
AI Risk Assessment Techniques
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AI Governance Professional
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Course Overview

AI is transforming business operations, decision-making, customer experience, cybersecurity, financial services, healthcare, human resources, and digital transformation. However, AI adoption also creates significant governance challenges related to:

  • AI bias and discrimination
  • Data privacy and personal information
  • Cybersecurity threats
  • Lack of transparency
  • Explainability challenges
  • Model hallucinations
  • Intellectual property and copyright
  • Third-party AI risks
  • Automated decision-making
  • Regulatory compliance
  • Human oversight
  • Ethical and societal impact

The AIGP Training equips professionals with the knowledge required to establish governance structures, assess AI risks, understand regulatory obligations, and govern AI systems responsibly throughout their life cycle.

Who Should Attend
  • AI Governance Professionals
  • Chief Information Officers
  • Chief Information Security Officers
  • Chief Data Officers
  • Chief Privacy Officers
  • Data Protection Officers
  • AI Risk Managers
  • Enterprise Risk Professionals
  • Governance, Risk and Compliance Professionals
  • Information Security Managers
  • Privacy Professionals
  • Compliance Officers

Course Highlights

25+
Years Experienced Industry Trainers
100%
Practical and Case Study-Based Learning
100%
Live Virtual Instructor-Led Training
100%
Exam Preparation Support

Batch Schedules

Pick a cohort that matches your availability. Limited seats per batch to ensure hands-on mentor support and lab guidance.

New batches will be announced soon
Stay tuned
Upcoming schedule information is not available yet.

Course Curriculum

Module 1: Understanding Foundations of AI Governance
  • Introduction to Artificial Intelligence
  • AI, Machine Learning and Deep Learning
  • Generative AI and Large Language Models
  • Foundation models
  • Predictive AI vs. Generative AI
  • Enterprise AI applications
  • Nature and characteristics of AI systems
  • Why organizations need structured AI governance
  • Responsible AI principles
  • Trustworthy AI
  • Fairness and non-discrimination
  • Transparency and explainability
  • Accountability
  • Privacy and data protection
  • Security and resilience
  • Human oversight
  • AI governance operating models
  • AI governance policies
  • AI governance committees
  • Roles and responsibilities
  • AI system inventory
  • AI risk classification
  • AI governance across the complete AI life cycle
  • AI model retirement and decommissioning
Module 2: Understanding How Laws, Standards & Frameworks Apply to AI
  • Global AI regulatory landscape
  • Application of existing laws to AI
  • Data privacy laws and AI
  • Personal data processing in AI systems
  • Sensitive data
  • Automated decision-making
  • Profiling
  • Data subject rights
  • Transparency obligations
  • Data Protection Impact Assessments
  • Consumer protection laws
  • Anti-discrimination laws
  • Employment laws
  • Intellectual property laws
  • Copyright and AI-generated content
  • Product safety and liability
  • Cybersecurity obligations
  • Understanding the EU AI Act
  • EU AI Act risk-based classification
  • Prohibited AI practices
  • High-risk AI systems
  • Transparency obligations
  • General-purpose AI models
  • Roles of AI providers and deployers
  • Conformity assessment
  • Technical documentation
  • Human oversight
  • Post-market monitoring
  • AI incident reporting
  • ISO/IEC 42001 AI Management System
  • ISO/IEC 23894 AI Risk Management
  • NIST AI Risk Management Framework
  • OECD AI Principles
  • AI governance tools and assessment methods
Module 3: Understanding How to Govern AI Development
  • AI project initiation
  • Business justification
  • Defining intended purpose
  • AI system boundaries
  • Stakeholder identification
  • Responsible AI requirements
  • AI design risk assessment
  • Foreseeable misuse
  • AI impact assessment
  • Data sourcing and provenance
  • Data ownership
  • Data licensing
  • Data quality
  • Data representativeness
  • Training data governance
  • Dataset lineage
  • Data labeling
  • Bias in training datasets
  • Model selection
  • Model architecture decisions
  • Model versioning
  • Reproducibility
  • Privacy-by-design
  • Security-by-design
  • Explainability-by-design
  • AI model testing
  • Performance testing
  • Accuracy testing
  • Reliability testing
  • Bias and fairness testing
  • Robustness testing
  • Security testing
  • Adversarial testing
  • AI red teaming
  • Generative AI governance
  • LLM governance
  • Hallucination management
  • Prompt injection risks
  • RAG governance
  • Fine-tuning governance
  • Model validation
  • Independent review
  • Model approval
  • Release criteria
  • Continuous monitoring
  • Model drift
  • Data drift
  • Model retraining
  • Change management
Module 4: Understanding How to Govern AI Deployment & Use
  • AI deployment governance
  • Deployment readiness assessment
  • Business approval
  • Technical approval
  • AI risk acceptance
  • Production deployment controls
  • Evaluating deployment risks
  • Privacy risks
  • Security risks
  • Bias and discrimination risks
  • Operational risks
  • Reputational risks
  • Third-party AI risks
  • AI model assessment
  • Accuracy assessment
  • Reliability assessment
  • Fairness assessment
  • Explainability assessment
  • Privacy impact assessment
  • Security assessment
  • Algorithmic Impact Assessment
  • Human-in-the-loop
  • Human-on-the-loop
  • Human-in-command
  • Human intervention and override
  • Preventing automation bias
  • AI transparency
  • AI disclosures
  • Explainability for stakeholders
  • Third-party AI governance
  • AI vendor due diligence
  • AI supply-chain risk
  • Enterprise Generative AI governance
  • Shadow AI
  • Employee use of AI
  • Prompt governance
  • AI output verification
  • Post-deployment monitoring
  • AI performance monitoring
  • Model drift detection
  • Bias monitoring
  • AI incident management
  • Root cause analysis
  • Corrective action
  • Model change management
  • AI model retirement
  • AI audit and assurance
  • Governance maturity assessment
  • Continuous improvement
Career Growth Focus

Career Outcomes That Matter

Go from learning to earning with role-aligned outcomes, practical skill-building, and employer-ready positioning.

Talk to a Career Advisor
Career outcome details will be updated soon for this course.
GET THE APPLIED AI Governance Professional CERTIFICATION

Earn the Coveted Applied AI Governance Professional Certification

Sample certificate will be available soon.
Sample Certificate (JPG / PNG)

Meet Your Instructors

Y Kaushik
CISSP, CISM, Certified AI Security Specialist, ISO/IEC 27701:2025, ISO/IEC 42001:2023,ISO/IEC 27001:2022
Kaushik has 20+ years of global experience in IT, Governance, Risk & Compliance (GRC), Cybersecurity, and Business Continuity. She...
India 4.5/5

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