Eristotle CERTIFIED AI Governance OFFICER (ECAGO)
3–5 Years of AI, data or technology experience required.
ECAGO certifies your professional-level expertise in Artificial Intelligence governance, aligned with the AIBOK™ Guide and industry standards. Stand out with core AI governance knowledge and competencies, assessed by examination.
Prepare for the ECAGO Certification
The Eristotle Certified AI Governance Officer Pathway is designed to equip professionals with the knowledge and practical skills needed to apply, govern, and lead AI initiatives responsibly and effectively. Aligned with the Artificial Intelligence Body of Knowledge (AIBOK™), this course covers the full scope of ECAGO exam.
Register for the ECAGO Certification Exam
The Eristotle Certified AI Governance Officer (ECAGO) certification exam is now available through secure remote online proctoring, allowing you to complete your assessment from anywhere in the world, giving you the flexibility that best suits your needs.
How to Get Started?
The ECAGO credential is built on the AI Governance Body of Knowledge (AIBOK™) and assessed by examination. It is designed to evidence that you can establish AI governance structures, assess model and deployment risk, and hold accountability for AI systems running in production.
Is ECAGO the right certification for you?
The Eristotle Certified AI Governance Officer (ECAGO) credential demonstrates that you have the technical proficiency and strategic insight to apply AI solutions effectively across a wide range of business environments. It validates your ability to design, implement, and govern AI initiatives using established frameworks and methodologies, regardless of industry sector or geographic location.
What roles is the ECAGO designed for?
ECAGO is written for practitioners who hold, or are moving into, accountability for how AI is governed — risk and compliance leads taking on AI oversight, data and model owners, and security professionals extending into AI assurance. The examination is drawn from the AIBOK™ and tests governance judgement rather than model-building skill.
Where do the ECAGO competencies apply?
The AIBOK™ is written to be sector-neutral and regulation-aware rather than tied to a single jurisdiction, so the competencies transfer across finance, healthcare, technology, the public sector and critical infrastructure.
The examination is delivered by remote online proctoring, so candidates can sit it from any country. Certified members are listed in the Eristotle Certification Registry, which is public and verifiable by an employer.
Exam Details
Exam Length 180 questions
Exam Duration 4 hrs (230 minutes for attempting the questions, plus 10 mins initial study time).
Where to get started?
What do I need to become an ECAGO?
ECAGO Eligibility
To earn the ECAGO designation, candidates must:
- Complete a minimum of 7,500 hours of Information Technology work experience in the last 10 years.
- Within this experience, a minimum of 900 hours completed in 4 of the 6 Guide to AIBOK™ Knowledge Domains, for a total of at least 3,600 of the required 7,500 total.
- Complete a minimum of 35 hours of professional development in the last 4 years.
- Provide two references.
- Agree to Eristotle Code of Ethics.
- Agree to Eristotle Privacy Policy.
- Agree to Eristotle Terms of Use.
- Pass the exam.
Who is an ECAGO?
ECAGO Competencies
Is ECAGO right for you? The ECAGO certification is for:
- AI and Data Leaders, including Heads of AI, Chief Data Officers, CIOs, and Business Unit Leaders driving AI initiatives.
- Professionals with substantial experience in artificial intelligence, machine learning, or data science seeking to formalize and validate their expertise.
- AI and Data Product Managers responsible for shaping and delivering AI-enabled solutions.
- Senior, Managing, and Executive Consultants leading AI strategy, solution design, and enterprise AI program rollouts.
- AI and Technology Educators or Trainers seeking recognized certification to support curriculum and professional development.
What is AN ECAGO CERTIFICATION?
The Eristotle Certified AI Governance Officer (ECAGO) certification validates professional-level expertise in artificial intelligence based on the Artificial Intelligence Body of Knowledge (AIBOK™) Guide.
It demonstrates that you possess the core knowledge and executive competencies required to lead in the dynamic and continuously evolving field of artificial intelligence.
Set yourself apart from your peers, showcase your leadership capabilities and be certified by the trusted authority in cybersecurity education, Eristotle.
Steps to Become an ECAGO?
- Read the Certification Guide to understand the requirements.
- Sign in to access the Guide to Artificial Intelligence Body of Knowledge (AIBOK™) interactive guide.
- Connect with other members on a similar journey through the Eristotle Communities.
- You can join the ECAGO Pathway, if you feel the need.
- Once you are ready, purchase and schedule the Eristotle Certified AI Governance Officer (ECAGO) Certification Exam.
- Take the exam and qualify as an Eristotle Certified AI Governance Officer (ECAGO).
- Get listed in our Certification Registry.
Certification Guide
- Review the Exam Fees.
- Understand the Certification Process requirements.
- Understand the Re-Certification Process requirements.
Test Your Knowledge
- Study the Guide to the Artificial Intelligence Body of Knowledge (AIBOK™).
- Find out about the exam requirements through the ECAGO Exam Blueprint.
- Attempt ECAGO Sample Exam Questions.
Exam Blueprint
Below are some of the topics, based on the Guide to the Artificial Intelligence Body of Knowledge (AIBOK™), that the candidates can expect to be tested on during the exam:
AI Foundations and Governance
Core principles, societal context, and responsible AI adoption
- AI governance and risk oversight models.
- Definitions, history, and scope of AI
- AI ethics, governance, fairness, accountability, and societal impact
- Regulatory frameworks and compliance considerations
- Global AI standards, policies, and best practices
Machine Learning and Deep Learning
Core technologies powering modern AI applications
- Model performance metrics and improvement strategies
- Supervised, unsupervised, semi-supervised, and reinforcement learning
- Algorithm design, model training, evaluation, and optimization
- Neural networks, CNNs, RNNs, transformers, and generative AI models
Natural Language Processing and Computer Vision
AI capabilities for language and perception
- Text classification, sentiment analysis, named entity recognition (NER), and conversational AI
- Language models, chatbots, and virtual assistants
- Image processing, object detection, segmentation, and video analysis
- Real-world applications in autonomous systems and visual recognition
AI Development Lifecycle and Data Management
End-to-end pipeline from data to deployed model
- Problem definition, data collection, and preprocessing
- Feature engineering, model selection, and training
- Model deployment, monitoring, maintenance, and retraining
- Data governance, data labeling, privacy, security, and bias mitigation
- Big data handling and ethical data usage
Explainable, Trustworthy, and Secure AI
Building confidence, resilience, and integrity in AI systems
- Interpretability, explainability, and transparency in AI decision-making
- Techniques for fairness, accountability, and bias reduction
- Secure AI practices and defenses against adversarial attacks
- Risk management, robustness, and model validation strategies
AI Applications, Trends, and Ecosystem Tools
Practical use, industry alignment, and evolving capabilities
- Use cases across industries (finance, healthcare, cybersecurity, etc.)
- AI in business intelligence, process automation, and decision support
- Emerging technologies: generative AI, LLMs, quantum AI, and edge computing
- AI development tools and frameworks (e.g., TensorFlow, PyTorch, Hugging Face)
- Cloud-based AI services and deployment environments
- Community-accepted techniques and case studies demonstrating real-world impact
Sample Exam Questions
1. Which of the following best reflects a core principle of responsible AI adoption within a corporate governance framework?
A. Maximizing data collection to enhance model accuracy B. Prioritizing algorithmic complexity over explainability C. Ensuring transparency and accountability in automated decisions D. Minimizing compliance with external regulatory standards
Correct Answer: C
2. A model is trained on labeled data to predict credit risk scores. What type of machine learning is being applied?
A. Unsupervised learning B. Reinforcement learning C. Semi-supervised learning D. Supervised learning
Correct Answer: D
3. Which NLP technique is most suitable for automatically extracting entities such as names, dates, and locations from unstructured tex
A. Sentiment analysis B. Text classification C. Named Entity Recognition (NER) D. Topic modeling
Correct Answer: C
4. Which activity is most appropriate during the model monitoring phase of the AI development lifecycle?
A. Performing hyperparameter tuning B. Selecting initial training data C. Detecting model drift in production D. Conducting exploratory data analysis
Correct Answer: C
5. What is the primary purpose of using SHAP (SHapley Additive exPlanations) in AI systems?
A. To detect adversarial attacks B. To visualize training loss curves C. To explain the contribution of features to model output D. To anonymize sensitive training data
Correct Answer: C
6. Which of the following frameworks is most commonly used for training transformer-based models in natural language understanding tasks?
A. OpenCV B. Scikit-learn C. Hugging Face Transformers D. Keras Tuner
Correct Answer: C
Earn Your Digital Badge
Earn a verified digital credential when you complete the course, and secure the distinguished ECAGO Certification Badge after passing the certification exam, perfect for highlighting your achievement on LinkedIn, your email signature, or your professional portfolio.