AI Powered Course
Certified AI Governance Specialist (CAIGS) Training
Lead the Future of Responsible AI
24 Mar
Program Highlights
InfosecTrain’s Certified AI Governance Specialist (CAIGS) Training is a 48-hour, live instructor-led program designed to prepare professionals for the fast-evolving world of Responsible AI. Through real-world use cases, governance scenarios, and interactive discussions, participants gain practical expertise in AI laws, risk management, ethics, and governance frameworks. Led by certified AI governance specialists, the program emphasizes hands-on learning, career mentorship, post-training support, and recorded session access—empowering professionals to confidently lead compliant, transparent, and trustworthy AI initiatives at scale.
48-Hour LIVE Instructor-led Training 
Real-World AI Use Cases & Governance Scenarios 
Practical Approach 
Highly Interactive and Dynamic Sessions 
Telegram Support Group 
Learn from Industry Experts 
Career Guidance and Mentorship 
Extended Post Training Support 
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 | |
|---|---|---|---|---|---|
| 24 Mar - 27 Apr | Online | Weekday | 19:30 - 22:00 IST | BATCH OPEN |
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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 48-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 and 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 1: 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 2: 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 3: 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 4: 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 5: AI Models, Architecture & Lifecycle
- Key Layers of AI Architecture (Data, Model, Application, Security)
- Governance in AI Architecture
- AI System Lifecycle & Governance Integration
- AI in the Cloud
- 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 6: 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 Risk
- Module 7: 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 8: 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 9: 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 10: 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 11: Auditing AI Systems
- AI Audit Frameworks & Standards
- Key Audit Areas & Techniques
- Challenges in AI Auditing (Methodologies, Data Access)
- AI Audit Simulation Exercise
- Module 12: 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
- Information Security Professionals
- Cloud Security Professionals
- Security Architects & Engineers
- GRC Professionals
- Consultants & Auditor
- Legal, Policy, & Risk Managers
- Data & AI Project Managers
- Business & Technology Leaders
- The training has no set prerequisites.
Upon successful completion of the training, participants will be able to:
- Understand the AI governance lifecycle, from data and models to risk, ethics, law, and compliance.
- Drive Responsible AI Adoption
- Learn how to navigate and comply with fast-evolving global AI regulations
- Utilize frameworks for identifying, assessing, and managing ethical, operational, and compliance risks in AI.
- Integrate Governance with Cloud AI
How We Help You Succeed
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Success
Our Expert Course Advisors
19+ Years of Experience | Microsoft & CSA Authorized Instructor
Words Have Power
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.
Success Speaks Volumes
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Frequently Asked Questions
What is AI Governance Specialist Training?
The Certified AI Governance Specialist (CAIGS) Training is a 48-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.
Who should attend the AI Governance Specialist Training course?
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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.
