AI Powered Course
Certified AI Governance Specialist Training
Lead the Future of Responsible AI
12 Jan
Program Highlights
The Certified AI Governance Specialist (CAIGS) Training is a comprehensive, instructor-led program designed for professionals who want to govern AI responsibly, securely, and at scale. Covering the entire AI governance lifecycle from ethics, regulations, and risk management to architecture, data governance, and auditing this program blends theory, frameworks, and real-world case studies. By the end of the training, you will be equipped to design and operationalize AI governance programs that ensure fairness, transparency, compliance, and business alignment, while future-proofing your career in the rapidly evolving AI landscape.
40-Hour LIVE Instructor-led Training 
Case-Study based Learning 
Real-world AI Projects 
Immersive & Dynamic Learning 
AI Governance Lifecycle 
Certified Experts 
Career Guidance & Mentorship 
Dedicated Telegram Support Group 
Access to Recorded Sessions
Training Schedule
- upcoming classes
- corporate training
- 1 on 1 training
| Start - End Date | Training Mode | Batch Type | Start - End Time | Batch Status | |
|---|---|---|---|---|---|
| 12 Jan - 16 Feb | Online | Weekday | 20:00 - 22:00 IST | BATCH OPEN |
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- Get personalized attention
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Certified AI Governance Specialist (CAIGS) is an advanced, end-to-end program designed to help professionals master the frameworks, tools, and strategies needed to govern Artificial Intelligence systems responsibly, securely, and at scale.
This 40-hour intensive program covers the full lifecycle of AI governance—from ethical foundations, legal and regulatory compliance, data governance, risk management, assessment and model accountability to the integration of AI systems within cloud environments. Participants will gain practical expertise in aligning AI adoption with business goals while ensuring fairness, transparency, security, and compliance with global standards
By combining theoretical knowledge, real-world case studies, this course equips professionals to design and operationalize trustworthy AI governance programs that are both future-proof and business-ready.
- Module 01: AI Foundations
- Types of AI (Functionality & Capabilities)
- Branches & Applications of AI across industries
- AI Technology Stack
- Machine Learning Components, Processes, and Types
- Generative AI & Large Language Models (LLMs)
- Common AI Attacks & Mitigation
- Ethical Considerations
- Module 02: Ethics, Responsible AI & Societal Impact
- Principles of Responsible AI
- Bias, Fairness, and Discrimination
- Privacy & Security Concerns
- Job Displacement & Economic Impact
- Bias: Use Cases
- Types of AI Discrimination
- Addressing algorithmic bias and fairness
- Privacy concerns and data protection.
- Responsible AI Development and Deployment
- Key principles of Responsible AI
- Case Studies
- Module 03: Global AI Laws & Regulations
- Overview of existing AI laws and regulations
- Legal and ethical considerations: Data privacy, bias, transparency, accountability
- Emerging trends in AI legislation
- How do AI regulations affect the adoption of AI in different industries
- Categories of AI Law
- Legal and ethical considerations: Data privacy, bias, transparency, accountability
- OECD AI Principles: Fairness, transparency, and accountability.
- EU AI Act
- ISO/IEC 42001:2021 for Artificial Intelligence
- Assessing the regulatory impact on AI systems.
- Managing cross-border compliance
- Intellectual Property Rights:
- Copyright and patent issues related to AI models and data.
- Ownership of AI-generated content
- Liability and Accountability:
- Determining liability for AI-related harms.
- Ensuring accountability for AI decisions.
- Algorithmic Accountability:
- Establishing mechanisms for auditing and reviewing AI systems.
- Module 04: AI Governance
- Governance & Types
- Enterprise AI Governance Vs. Responsible AI Governance
- AI Governance Models (Centralized, Decentralized, Federated)
- Trustworthy AI
- Responsible Artificial Governance (RAG)
- Transparency, explainability & Liability
- Designing AI Governance Committees & Councils
- Aligning AI with Business Objectives
- Building & Measuring AI Governance Programs
- Identifying and Engaging Stakeholders
- Aligning Stakeholder Interests with Governance Objectives
- Managing Expectations & Communication
- Role-Based Exercises
- Module 05: AI Architecture & Lifecycle
- Key Layers of AI Architecture (Data, Model, Application, Security)
- Governance in AI Architecture
- AI System Lifecycle & Governance Integration
- AI in the Cloud
- Module 06: AI Model
- Understanding AI Models
- Model Evaluation & Interpretability (LIME, SHAP, Rule-Based, Visualizations)
- Explainability & Accountability (GDPR Right to Explanation)
- RAG & Prompt Engineering
- Model Drift, Degradation, Monitoring
- Model Cards & Documentation
- Module 07: AI Risk Management
- AI Risk Categories: Ethical, Operational, Societal
- NIST AI RMF & MIT AI Risk Repository
- AI Risk Register & AI Impact Assessment (AIIA)
- Risk Assessment Methodologies (FMEA, FTA)
- EU AI Act Risk Tiers
- Bias Identification & Mitigation
- Third-Party AI Risk Management
- AI Governance Maturity Models
- Case Study: AI-Powered Chatbot Risks
- Module 08: Data Governance for AI
- Data Strategy for AI
- Data Governance Policy
- Data quality, Data Gathering
- Data Cleansing
- Data Labelling, Data privacy & security, Data ethics
- Data Bias
- Data Validation and Testing Data
- Data lifecycle management for AI projects
- Data collection, processing, storage, and use for AI systems
- Data exfiltration
- Data Anonymization, Pseudonymization, and Differential Privacy techniques
- Case Study: AI recommendation engine
- Implementing data governance frameworks for AI
- AI data security
- Module 09: AI and Privacy
- Foundation Principles of Data Privacy
- Privacy Concerns in Data Collection and Processing
- Privacy risks associated with AI: Data breaches, surveillance, bias, discrimination
- Privacy-enhancing technologies (PETs) for AI
- Data Minimization
- Balancing innovation with privacy rights
- Case Study: Addressing privacy concerns in facial recognition technology
- Module 10: AI Model Validation & Testing
- Understanding AI Models
- Model Evaluation & Interpretability (LIME, SHAP, Rule-Based, Visualizations)
- Explainability & Accountability (GDPR Right to Explanation)
- Retrieval Augmented Generation (RAG) & Prompt Engineering
- Model Drift, Degradation, and Monitoring
- Model Validation & Testing (Bias, Robustness, Failures)
- Model Cards & Documentation
- Module 11: AI on Cloud
- Cloud Computing Fundamentals
- Role of Cloud in AI
- AI Hosting Models on Cloud
- Key considerations for choosing CSP for AI Workloads
- Leveraging Native Cloud Security for AI
- Addressing AI-Specific Security Vectors in the Cloud
- Integrating AI Governance into Cloud Infrastructure
- Case Study: AI Application Lifecycle
- Module 12: AI Security
- AI Threat Landscape
- Security Controls Across AI Lifecycle
- Encryption, IAM, and Intrusion Detection
- AI Red Teaming & Adversarial Attacks
- Incident Response for AI Systems
- Module 13: Auditing AI Systems
- AI Audit Frameworks & Standards
- Key Audit Areas & Techniques
- Challenges in AI Auditing (Methodologies, Data Access)
- AI Audit Simulation Exercise
- Module 14: SDLC for AI Systems
- SDLC Methodologies (Agile, DevOps, Waterfall)
- Governance in Each SDLC Phase
- Planning, Design, Development, Testing, Deployment, Maintenance
This training is ideal for:
- IT & Security Leaders
- Cloud Security Professionals
- Security Architects & Engineers
- GRC Professionals
- Consultants & Auditors
- Legal, Policy, & Risk Managers
- Data & AI Project Managers
- Business & Technology Leaders
The training has no set prerequisites.
Upon completion, participants will be able to:
- Navigate the Full AI Governance Lifecycle – covering ethics, law, risk, and compliance.
- Champion Responsible AI Adoption – embed fairness, transparency, and accountability in AI systems.
- Stay Ahead of Global AI Regulations – master frameworks like EU AI Act, ISO/IEC 42001, OECD principles.
- Mitigate Ethical & Operational Risks – use proven methodologies for risk identification, monitoring, and control.
- Integrate AI Governance into Cloud – secure AI workloads and ensure compliance across cloud environments.
- Audit & Validate AI Systems – implement governance, testing, and monitoring for explainability and trustworthiness.
- Align AI with Business Goals – drive innovation responsibly while maintaining compliance and security.
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It was a very good experience with the team. The class was clear and understandable, and it benefited me in learning all the concepts and gaining valuable knowledge.
I loved the overall training! Trainer is very knowledgeable, had clear understanding of all the topics covered. Loved the way he pays attention to details.
I had a great experience with the team. The training advisor was very supportive, and the trainer explained the concepts clearly and effectively. The program was well-structured and has definitely enhanced my skills in AI. Thank you for a wonderful learning experience.
The class was really good. The instructor gave us confidence and delivered the content in an impactful and easy-to-understand manner.
The program helped me understand several areas I was unfamiliar with. The instructor was exceptionally skilled and confident in delivering content.
The program was well-structured and easy to follow. The instructor’s use of real-life AI examples made it easier to connect with and understand the concepts.
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Frequently Asked Questions
What is AI Governance Specialist Training?
The Certified AI Governance Specialist (CAIGS) Training is a 40-hour, instructor-led program covering the entire AI governance lifecycle—ethics, risk, compliance, data governance, and auditing—equipping professionals to govern AI responsibly and securely at scale.
Why is AI Governance important for organizations?
It ensures AI adoption is fair, transparent, secure, and compliant with global laws, reducing risks of bias, regulatory fines, and reputational damage while aligning AI with business goals.
Who should attend the AI Governance Specialist Training course?
IT & Security leaders, GRC professionals, legal & compliance managers, consultants, auditors, AI project managers, and business/technology leaders seeking to embed responsible AI practices.
Does this course cover ISO/IEC 42001 and the EU AI Act?
Yes. The program covers ISO/IEC 42001:2021, the EU AI Act, OECD principles, and other global AI laws and standards to help professionals navigate regulatory compliance.
Does the course include AI risk management frameworks like NIST AI RMF?
Yes. You’ll learn NIST AI RMF, MIT AI Risk Repository, EU AI Act risk tiers, and AI risk registers to effectively assess and mitigate AI risks.
How does the training address AI ethics and bias?
The course teaches bias detection, fairness, discrimination prevention, and algorithmic accountability, along with practical case studies on responsible AI adoption.
Is this course suitable for non-technical professionals?
Yes. While technical aspects are covered, it’s designed for both technical and non-technical professionals such as legal, compliance, and risk managers.
What industries benefit most from AI Governance Training?
Industries like finance, healthcare, telecom, government, and technology that use AI for critical decision-making benefit most, though lessons apply across all sectors.
How does the training prepare professionals for regulatory compliance?
By combining global standards with real-world case studies, the course equips you to integrate compliance into AI systems from design to deployment.
What career opportunities are available after AI Governance Training?
You can pursue roles such as AI Governance Specialist, Responsible AI Officer, AI Risk Manager, Compliance & Ethics Lead, or AI Auditor.
Does this training include case studies and practical exercises?
Yes. The course blends theory with exercises, governance simulations, and real-world case studies for practical learning.
What are the benefits of enrolling with InfosecTrain?
40-hour live training, real-world projects, a custom course crafted to tackle today’s vast AI landscape, access to recordings, a Telegram support group, and post-training mentorship & career guidance.
