Certified AI Governance Specialist (CAIGS) 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.

IT & Security leaders, GRC professionals, legal & compliance managers, consultants, auditors, AI project managers, and business/technology leaders seeking to embed responsible AI practices.

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.

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.

The course teaches bias detection, fairness, discrimination prevention, and algorithmic accountability, along with practical case studies on responsible AI adoption.

Yes. While technical aspects are covered, it’s designed for both technical and non-technical professionals such as legal, compliance, and risk managers.

Industries like finance, healthcare, telecom, government, and technology that use AI for critical decision-making benefit most, though lessons apply across all sectors.

By combining global standards with real-world case studies, the course equips you to integrate compliance into AI systems from design to deployment.

You can pursue roles such as AI Governance Specialist, Responsible AI Officer, AI Risk Manager, Compliance & Ethics Lead, or AI Auditor.

Yes. The course blends theory with exercises, governance simulations, and real-world case studies for practical learning.

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.

AI Governance is the set of policies, frameworks, processes and accountability structures that ensure artificial intelligence systems are developed and deployed responsibly, transparently and in compliance with applicable laws. In 2026, it has become one of the most commercially urgent skills in technology and risk management. The EU AI Act entered enforcement phases in 2025, creating immediate compliance obligations for organisations operating in or selling to Europe. India's evolving digital regulation landscape, combined with GDPR's existing reach into AI systems, means organisations across sectors are actively hiring AI governance professionals to manage risk, demonstrate accountability and avoid regulatory penalties. The CAIGS certification directly addresses this demand.

The EU AI Act is the world's first legally binding AI regulation, classifying AI systems by risk tier and imposing specific obligations on developers, deployers and importers of AI systems. Module 3 of the CAIGS programme covers the EU AI Act in depth, including its risk tier classifications, conformity assessment requirements, prohibited AI practices and compliance obligations for different organisational roles. You will learn how to assess where your organisation's AI systems fall within the Act's framework and what governance, documentation and accountability measures are required, making this course directly applicable to any organisation with EU market exposure.

The CAIGS course has no set prerequisites and is designed for a broad range of professionals. It is ideal for GRC professionals and auditors building AI-specific governance capability, security architects and cloud engineers responsible for AI system security, legal and policy managers navigating AI regulation, data and AI project managers needing governance frameworks, CISOs and IT leaders accountable for responsible AI adoption, and business leaders overseeing AI transformation programmes. Technical depth is built progressively, you do not need an AI or data science background to benefit. What matters is a professional interest in governing AI systems effectively.

Most AI governance courses stop at principles and frameworks. This course goes significantly deeper, covering model validation and interpretability using LIME and SHAP, adversarial attacks and AI red teaming, AI audit simulation exercises, data governance for AI including anonymisation and differential privacy, cloud AI governance across AWS, Azure and GCP, and governance integration across the full AI SDLC. It is also one of the few courses that covers Generative AI, LLMs, RAG and prompt engineering within a governance context, directly relevant to the AI technologies organisations are actually deploying today. The result is a programme that is technically credible, regulatory-complete and immediately applicable.

Generative AI and Large Language Models introduce governance challenges that traditional AI frameworks do not fully address, including hallucination risks, prompt injection, copyright and IP liability, data exfiltration through model inputs, and explainability gaps. The CAIGS curriculum addresses GenAI throughout multiple modules: Module 1 covers LLM fundamentals and common AI attacks, Module 5 covers RAG and prompt engineering within a governance context, Module 7 addresses data bias and exfiltration risks specific to AI training pipelines, and Module 8 covers model validation, drift detection and robustness testing applicable to LLM deployments. This makes the programme one of the most current and GenAI-relevant governance certifications available.

An AI Risk Register documents identified risks across an organisation's AI systems including ethical, operational, societal and security risks with assigned owners, likelihood ratings and treatment plans. An AI Impact Assessment (AIIA) evaluates the potential harms a specific AI system could cause to individuals, groups or society before and during deployment. Both are core governance artifacts that regulators, boards and certification bodies increasingly require. In Module 6 of the CAIGS course you will develop both artifacts using NIST AI RMF methodology and EU AI Act risk tier classifications, leaving you with practical templates you can adapt for real organisational use.

Module 10 of the CAIGS course is dedicated to AI security, covering the AI threat landscape, security controls across the AI lifecycle, encryption and IAM for AI systems, intrusion detection, and incident response specific to AI systems. AI red teaming is the practice of deliberately attempting to cause AI systems to behave in harmful, biased or exploitable ways, similar to penetration testing for traditional software but focused on AI-specific attack vectors like adversarial inputs, model inversion, data poisoning and prompt injection. Understanding red teaming is increasingly required for organisations deploying customer-facing AI and is a direct requirement under the EU AI Act for high-risk AI systems.

The CAIGS certification positions you for roles including AI Governance Specialist, AI Risk Manager, Responsible AI Lead, AI Compliance Officer, AI Auditor, GRC Manager, Chief AI Officer (CAIO), AI Policy Manager and AI Ethics Consultant. These are among the fastest-growing roles in enterprise technology in 2026 as virtually every major organisation deploying AI is now building internal governance capability or hiring externally for it.