التقنية : الميتافيرس
- AR Version
- Tech : Intelligence / AI GRC Course
الرئيس التنفيذي للتدقيق على الذكاء الاصطناعي™
Chief AI Auditor (CAIA)™ Workshop
Equip professionals with the expertise to audit AI systems for compliance, risk management, and performance assurance using the LEAP-GDC methodology.
- Last updated : February 12, 2025
- 4.721 students
About the Workshop
The Chief AI Auditor (CAIA)™ Workshop is an immersive training program designed to equip professionals with the skills and knowledge necessary to audit AI systems effectively. Participants will gain expertise in algorithmic governance, risk management, compliance evaluation, and assurance methodologies to ensure responsible AI deployment. The workshop aligns with global AI standards such as ISO 42001, NIST AI RMF, and the EU AI Act, preparing attendees for current and future regulatory challenges.
Using the LEAP-GDC methodology, this workshop combines theoretical knowledge, practical exercises, and advanced auditing tools to prepare professionals for the critical role of auditing AI systems.
The course is designed for those interested in the design, implementation and responsible use of artificial intelligence systems and products within their organization.
- – Compliance Officers and Risk Managers
- – AI Professionals overseeing system accountability
- – Governance and Ethics Specialists
- – Business Leaders integrating AI technologies
- – IT Auditors and Quality Assurance Teams
- – Chief AI Auditor
- – AI Governance Specialist
- – Compliance Manager for AI Systems
- – Algorithmic Risk Analyst
- – AI Quality Assurance Lead
- – Designing and conducting AI audits aligned with global standards
- – Techniques to identify and mitigate risks in AI systems
- – Methods for evaluating algorithmic fairness, transparency, and accountability
- – Best practices for ensuring compliance with AI regulations and ethical frameworks
- – Build expertise in the critical and high-demand field of AI auditing
- – Position yourself as a leader in responsible AI governance
- – Learn from industry experts through interactive sessions and case studies
- – Gain hands-on experience with AI audit tools and methodologies
- – Proficiency in AI auditing frameworks and processes
- – Competence in bias detection and algorithmic risk management
- – Ability to evaluate compliance and ethical alignment in AI systems
- – Expertise in preparing and presenting detailed audit reports
– Algorithmic Risk Management
- – Identifying potential risks in AI systems, including biases, security vulnerabilities, and operational inefficiencies
- – Developing frameworks to assess and mitigate algorithmic risks
- – Conducting impact analysis to evaluate the consequences of algorithm failures
– Ethical AI Governance
- – Establishing governance structures to oversee AI system accountability
- – Ensuring fairness, transparency, and inclusivity in AI models and processes
- – Aligning AI systems with ethical principles and organizational values
– Compliance with Global Standards
- – Understanding and applying frameworks like ISO 42001, NIST AI RMF, and the EU AI Act
- – Evaluating data privacy and quality compliance in AI systems
- – Preparing comprehensive audit reports that meet regulatory requirements
– Practical Auditing Tools and Techniques
- – Leveraging advanced tools for algorithm testing and performance validation
- – Conducting bias detection and fairness evaluations with auditing software
- – Implementing best practices for automated and manual AI audits
– AI Integration into Business Operations
- – Analyzing past AI system failures and their organizational impact
- – Exploring real-world examples of effective AI governance and compliance
- – Learning through simulated scenarios to audit high-risk AI applications
– Design and Execute AI Audit Workflows
- – Create structured audit plans tailored to specific AI applications
- – Define objectives, scope, and key performance indicators for AI audits
- – Coordinate cross-functional teams to conduct audits efficiently
– Conduct Bias and Fairness Evaluations for AI Models
- – Identify and mitigate biases in training data and algorithms
- – Evaluate fairness across different demographic or user groups
- – Implement tools and techniques for bias detection and remediation
– Ensure Regulatory Compliance and Accountability
- – Apply global standards such as ISO 42001, EU AI Act, and NIST AI RMF
- – Monitor AI system adherence to data privacy and ethical principles
- – Document compliance efforts through detailed audit reports
– Develop AI Project Roadmaps and Success Metrics
- – Analyze audit findings to identify areas for improvement
- – Provide recommendations for enhancing AI system transparency and accountability
- – Collaborate with stakeholders to implement governance frameworks
– Validate and Monitor AI System Performance
- – Assess AI model accuracy, reliability, and robustness
- – Establish ongoing monitoring protocols for AI systems in production
- – Leverage analytics to ensure continuous performance improvements
- – Basic experience in auditing is required.
- – Familiarity with basic AI concepts is recommended.
- – Open to professionals from diverse fields including compliance, governance, and technology.
Training Guide
1. Course Handbook
- Comprehensive guide covering all course modules.
- Detailed explanations of ISO/IEC 42001 principles, requirements, and implementation strategies.
2. Lecture Slides:
- Visual aids used during lectures for better understanding.
- Key points, diagrams, and examples.
3. Study Notes
- Summarized notes for quick reference.
- Important points highlighted from each lecture.
Service Guide
1. Implementation Manual
- Step-by-step instructions on how to implement the ISO/IEC 42001 standard.
- Detailed processes, templates, and best practices.
2. Risk Assessment Templates
- Templates to help identify, evaluate, and mitigate AI-related risks.
- Examples and guidelines on how to fill out the templates.
3. Compliance Checklists
- Checklists to ensure all necessary steps are taken for compliance.
- Items covering legal, ethical, and operational aspects.
Practical Tools
1. Case Studies
- Real-world examples of successful ISO/IEC 42001 implementation.
- Analysis of challenges faced and solutions applied.
2. Interactive Exercises
- Practical exercises to apply concepts learned.
- Group activities, role-playing scenarios, and problem-solving tasks.
3. Certification preparation guides and practice exercises.
- Access to AI management software or platforms used during the course.
- Training on how to use these tools effectively.
Templates and Forms
1.Policy Templates
1. Printed Materials
- – Physical copies of guides, templates, and notes.
2. Digital Resources
- – Downloadable PDFs and editable documents.
- – Online access to tools and software platforms.
3. Workshops and Webinars
- – Interactive workshops for hands-on practice.
- – Live and recorded webinars for remote learning.
4. Certification Exam Preparation
- – Practice exams and sample questions.
- – Study guides focused on certification requirements.
Practices
Practices for the ISO/IEC 42001 Course
English
1. Interactive Workshops:
Hands-on sessions where participants work on real-world scenarios.
Group activities to encourage collaboration and knowledge sharing.
Practical exercises to apply concepts learned.
2. Case Study Analysis:
In-depth examination of successful ISO/IEC 42001 implementations.
Discussion of challenges faced and solutions applied.
Lessons learned and best practices.
3. Risk Assessment Exercises:
Identifying and evaluating AI-related risks.
Developing risk mitigation strategies.
Using provided templates and tools for risk management.
4. Compliance Audits:
Conducting mock internal audits.
Reviewing compliance checklists and documentation.
Identifying areas for improvement and developing action plans.
5. Role-Playing Scenarios:
Simulating real-world situations to practice decision-making.
Role-playing different stakeholders to understand various perspectives.
Problem-solving tasks to reinforce learning.
6. Policy and Procedure Development:
Creating AI management policies using provided templates.
Developing standard operating procedures for AI tasks.
Customizing documents to fit organizational needs.
7. Ethical and Legal Considerations Workshops:
Discussing ethical implications and legal requirements of AI.
Analyzing case studies on ethical dilemmas.
Developing strategies for ethical AI management.
8. Software Tools Training:
Hands-on training with AI management software or platforms.
Demonstrating how to use tools effectively.
Practice sessions to build proficiency.
9. Q&A Sessions and One-on-One Consultations:
Scheduled sessions for participants to ask questions.
Opportunities for individual consultations with instructors.
Addressing specific concerns and providing tailored advice.
10. Certification Exam Preparation:
Practice exams and sample questions.
Study guides focused on certification requirements.
Tips and strategies for successful exam performance.
Arabic
1. ورش العمل التفاعلية:
جلسات عملية حيث يعمل المشاركون على سيناريوهات واقعية.
أنشطة جماعية لتعزيز التعاون وتبادل المعرفة.
تمارين عملية لتطبيق المفاهيم المكتسبة.
2. تحليل دراسات الحالة:
فحص متعمق لتنفيذات ناجحة لمعيار ISO/IEC 42001.
مناقشة التحديات التي واجهتها والحلول المطبقة.
الدروس المستفادة وأفضل الممارسات.
3. تمارين تقييم المخاطر:
تحديد وتقييم المخاطر المتعلقة بالذكاء الاصطناعي.
تطوير استراتيجيات التخفيف من المخاطر.
استخدام القوالب والأدوات المقدمة لإدارة المخاطر.
4. تدقيق الامتثال:
إجراء تدقيقات داخلية وهمية.
مراجعة قوائم التحقق ووثائق الامتثال.
تحديد مناطق التحسين وتطوير خطط العمل.
5. سيناريوهات لعب الأدوار:
محاكاة مواقف واقعية لممارسة اتخاذ القرارات.
لعب أدوار أصحاب المصلحة المختلفين لفهم وجهات النظر المختلفة.
مهام حل المشكلات لتعزيز التعلم.
6. تطوير السياسات والإجراءات:
إنشاء سياسات إدارة الذكاء الاصطناعي باستخدام القوالب المقدمة.
تطوير إجراءات التشغيل القياسية لمهام الذكاء الاصطناعي.
تخصيص الوثائق لتناسب احتياجات المنظمة.
7. ورش العمل حول الاعتبارات الأخلاقية والقانونية:
مناقشة التداعيات الأخلاقية والمتطلبات القانونية للذكاء الاصطناعي.
تحليل دراسات الحالة حول المعضلات الأخلاقية.
تطوير استراتيجيات لإدارة الذكاء الاصطناعي بشكل أخلاقي.
8. تدريب على أدوات البرمجيات:
تدريب عملي على إدارة البرمجيات أو المنصات المستخدمة في الذكاء الاصطناعي.
عرض كيفية استخدام الأدوات بشكل فعال.
جلسات تدريب لبناء الكفاءة.
9. جلسات الأسئلة والأجوبة والمشاورات الفردية:
جلسات مجدولة لطرح الأسئلة من قبل المشاركين.
فرص للتشاور الفردي مع المدربين.
معالجة القضايا المحددة وتقديم نصائح مخصصة.
10. التحضير لامتحان الشهادة:
اختبارات تجريبية وأسئلة نموذجية.
أدلة دراسية تركز على متطلبات الشهادة.
نصائح واستراتيجيات لتحقيق أداء ناجح في الامتحان.
By incorporating these practices into the course, participants will gain practical experience and confidence in implementing the ISO/IEC 42001 standard, preparing them effectively for real-world application and certification.
- Course Introduction
- Certificate Introduction & Exam Details.
📌 Module 1: Understanding AI
1.1 Introduction to AI in Business.
1.2 Basic Concepts of AI.
1.3 History and Evolution of AI in Business.
1.4 Importance of AI in Modern Business Practices.
1.5 AI Trends, Terminology, and Applications.
1.6 The Impact of AI on Industries and Economies.
📌 Module 2: Introduction to AI Auditing
2.1 Understanding the role of AI auditors in governance and risk management
2.2 The importance of AI audits in ensuring ethical and responsible AI
2.3 Key challenges in AI auditing: Bias, transparency, and security vulnerabilities
📌 Module 3: AI Regulations, Standards, and Compliance Frameworks
3.1 Overview of global AI regulations: ISO 42001, EU AI Act, NIST AI RMF, IEEE 7000
3.2 AI governance principles and their impact on audits
3.3 How AI audit requirements differ across industries (finance, healthcare, etc.)
📌 Module 4: AI Risk Management & Algorithmic Governance
4.1 Fundamentals of AI risk assessment: Identifying and mitigating AI risks
4.2 Reviewing AI governance policies in organizations
4.3 Risk-based AI audit methodologies: ISO 42001 risk assessment (Clause 6.1.2)
📌 Module 5: AI System Lifecycle & Audit Techniques
- 5.1 Understanding AI model development, training, and deployment phases
- 5.2 AI system documentation and audit checklists
- 5.3 Methods of AI auditing: Documentation review, adversarial testing, black-box vs. white-box testing
📌 Module 6: AI Model Bias & Fairness Auditing
- 6.1 Identifying and mitigating biases in AI datasets and algorithms
- 6.2 Fairness metrics: Disparate impact, equalized odds, demographic parity
- 6.3 Tools for bias detection (e.g., IBM AI Fairness 360, Google What-If Tool)
📌 Module 7: AI Security & Adversarial Testing
- 7.1 Evaluating AI model robustness and security threats
- 7.2 Adversarial attack simulations (e.g., MITRE ATLAS)
- 7.3 AI model explainability and interpretability techniques
📌 Module 8: AI Audit Reporting & Risk Communication
- 8.1 Preparing structured AI audit reports
- 8.2 Communicating AI risk findings to stakeholders
- 8.3 AI regulatory compliance assessment case studies
📌 Module 9: Ethical & Responsible AI Auditing
- 9.1 Ensuring fairness, accountability, and transparency (FAT principles)
- 9.2 Auditing AI decision-making systems for ethical compliance
- 9.3 Establishing AI ethics review boards in organizations
📌 Module 10: AI Governance and Policy Implementation
- 10.1 Designing AI governance structures within organizations
- 10.2 Establishing AI compliance monitoring frameworks
- 10.3 Mapping AI risk management strategies to corporate policies
📌 Module 11: AI Audit Framework Development
- 11.1 Developing an AI auditing roadmap for enterprises
- 11.2 Implementing AI risk control frameworks
- 11.3 Integration of AI auditing into cybersecurity and data privacy programs
📌 Module 12: Governance in AI Auditing
- 12.1 Establishing AI Audit Governance Boards.
- 12.2 Managing Roles and Responsibilities in AI Auditing.
- 12.3 Ensuring Transparency and Accountability in AI Audit Processes.
📌 Module 13: AI Auditor Certification & Accreditation
- 13.1 Understanding AI auditor certification requirements (ISO 42001 & NIST AI RMF)
- 13.2 Establishing independent AI auditing functions
- 13.3 Licensing and registering certified AI auditors
📌 Module 14: AI Auditing Case Studies & Best Practices
- 14.1 Real-world AI audit failures and lessons learned
- 14.2 Best practices for conducting independent AI audits
- 14.3 Future trends in AI governance and risk management
📌 Module 15: Deployment and Monitoring
- 15.1 Establishing Feedback Loops for Continuous Auditing.
- 15.2 Leveraging AI for Real-Time Audit Automation.
- 15.3 End-to-end AI audit case study
- 15.4 Presenting AI audit findings and recommendations
📌 Module 16: Certification Preparation
- 16.1 Review of Key Concepts and Competencies.
- 16.2 Exam Preparation and Practice Scenarios.
Module 1: Introduction to ISO/IEC 42001
- Understanding AI and its implications
- Key concepts and terminology
- The need for responsible AI development
- Introduction to ISO/IEC 42001 Certification and its benefits
The Chief AI Auditor (CAIA)™ Workshop includes practical toolkits that participants can use to perform AI audits, ensure compliance, and implement governance frameworks effectively. These toolkits are structured based on MetaServ ME’s LEAP-GDC methodology and align with international AI governance standards.
1️⃣ AI Audit Planning & Risk Assessment Toolkit
📌 Includes:
- ✔ AI Audit Checklist Template (Pre-Audit Questionnaire)
- ✔ Risk Identification & Mitigation Framework (aligned with ISO 42001 Clause 6.1.2)
- ✔ AI Model Risk Scoring Matrix (Assessing likelihood vs. impact)
- ✔ AI System Impact Assessment Guide (Mapping AI failures to business risks)
- ✔ Governance & Compliance Readiness Report Template
🔹 Purpose: Helps AI auditors systematically plan and assess AI system risks before an audit.
2️⃣ AI Compliance & Regulatory Framework Toolkit
📌 Includes:
- ✔ AI Regulatory Compliance Checklist (ISO 42001, NIST AI RMF, EU AI Act)
- ✔ AI Ethics & Governance Evaluation Form (Fairness, Accountability, Transparency)
- ✔ Data Privacy & Security Audit Template (GDPR, AI Data Protection)
- ✔ AI Model Documentation & Explainability Framework
🔹 Purpose: Ensures AI systems comply with international standards and ethical requirements.
3️⃣ AI Model Bias & Fairness Evaluation Toolkit
📌 Includes:
- ✔ Bias Detection & Fairness Audit Checklist
- ✔ Algorithmic Bias Scoring Tool (Measuring disparate impact, equalized odds)
- ✔ Adversarial Testing Guide (Robustness & Bias Stress Testing)
- ✔ Fair AI Model Reporting Template
🔹 Purpose: Helps auditors identify and mitigate bias in AI models while ensuring fairness.
4️⃣ AI Security & Adversarial Testing Toolkit
📌 Includes:
- ✔ AI Security Audit & Threat Assessment Guide
- ✔ Adversarial Testing Framework (MITRE ATLAS methodology)
- ✔ AI Model Explainability & Robustness Testing Checklist
- ✔ Secure AI Development Guidelines (Aligning AI security with cybersecurity frameworks)
🔹 Purpose: Evaluates the security vulnerabilities of AI models and ensures robust deployment.
5️⃣ AI Audit Execution & Reporting Toolkit
📌 Includes:
- ✔ AI Audit Workflow & Execution Plan
- ✔ AI Risk Heatmap (Visualizing AI system risks)
- ✔ AI Audit Findings & Recommendations Template
- ✔ AI Governance Maturity Assessment Report
🔹 Purpose: Provides structured templates for executing AI audits and reporting findings effectively.
6️⃣ AI Governance & Certification Toolkit
📌 Includes:
- ✔ AI Governance Framework Implementation Guide
- ✔ AI Audit Accreditation & Certification Criteria
- ✔ AI Auditor Code of Ethics & Independence Guidelines
- ✔ Continuous AI Monitoring & Audit Strategy
🔹 Purpose: Helps organizations establish governance frameworks and certify AI audit professionals.
🚀 Why These Toolkits Matter
- 📌 Standardization: Ensures AI audits follow a structured and repeatable process.
- 📌 Compliance Assurance: Aligns AI audits with ISO 42001, NIST AI RMF, and the EU AI Act.
- 📌 Practical Implementation: Provides real-world tools to conduct audits efficiently.
- 📌 Certification Support: Helps AI auditors document and validate their findings for official certification.
These toolkits will be available for all participants during the Chief AI Auditor (CAIA)™ Workshop to ensure they can immediately apply what they learn in real-world AI auditing scenarios. 🚀
Certificate/accreditation and examination
1. The importance of certification:
- Journalists and content creators who work in various media outlets and want to keep up with technological developments.
2. The Exam:
- Journalists and content creators who work in various media outlets and want to keep up with technological developments.
3. Certificate and accreditation:
Attendance Testimonials
1. مقدمة عن التقنيات الحديثة في الإعلام:
- تعريف بالذكاء الاصطناعي والميتافيرس.
- أهمية تطبيقاتهم في مجالات الصحافة والإعلام والثقافة.
Q&A
1. مقدمة عن التقنيات الحديثة في الإعلام:
- تعريف بالذكاء الاصطناعي والميتافيرس.
- أهمية تطبيقاتهم في مجالات الصحافة والإعلام والثقافة.
Registrations is Closed, please Watch the Recording or Contact us for another class
Evaluate to Obtain a Certificate of Attendance
التقييم انتهى ، نراكم في محاضرات أخرى
Ramy AlDamati
General Informations
- – Delivery Languages : Arabic or English.
- – Material Languages : Arabic or English.
- – Delivery Format: Inperson , or Self-teaching video lectures.
- – Access from any device and from anywhere.
- – Internationally recognized certificate of attendance.
- – CPE/CPD credits.
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Plan Your Next Steps
Get a Full 360° ISO/IEC 42001 & AI Auditing Offering : These next steps provide Customers with a clear pathway to certification, practical tools for success, and continued professional development as Chief AI Auditor (CAIA)™.
Join Classroom
Attend live, interactive sessions either online or Recorded inperson for a collaborative learning experience. Sessions led by industry experts, offering opportunities for networking, hands-on exercises, and real-time discussions to deepen their understanding of AI governance and oversight.
Take the Exam
Attendees who complete the program exam successfully will earn the prestigious Chief AI Auditor (CAIA)™ certification. This globally recognized credential validates their expertise in AI governance, strategic oversight, and implementation. Achieving this certification demonstrates a professional's ability to lead AI initiatives ethically and effectively, and alignment with organizational objectives.
Study Materials
Receive comprehensive study materials and CAIA Body of Knowledge (BoK)— a curated repository of essential concepts, frameworks, and tools in AI governance, strategy, and deployment. Resources are designed to reinforce learning and provide ongoing support throughout their professional journey.
ISO 42001 Services
To implement an AIMS, you will need a solid understanding of ISO/IEC 42001's requirements. We have training to provide the required knowledge. Once the system is in place, we can offer a gap assessment. After the successful completion of an audit, we will issue your certificate, confirming the effective implementation of the standard's requirements. An audit against ISO/IEC 42001 from us will help your organization to stand out from the crowd by supporting you to develop and improve performance.
CAIA Preparedness Tooling™
The CAIA Toolkit equips Professionals with practical resources to oversee AI initiatives. It includes templates for governance policies, risk assessment frameworks, compliance checklists, and tools for managing AI projects and teams. This toolkit ensures Professionals are prepared to apply their skills immediately within their organizations.
CAIA Knowledge Resources
The CAIA Knowledge Guides are a series of detailed, expert-authored documents that delve into specialized topics such as AI risk management, ethical AI practices, and regulatory compliance. These guides serve as valuable references, helping professionals stay informed and make informed decisions as they oversee AI initiatives.
Hire a CAIA
Hire an experienced Chief AI Auditor (CAIA). Outsource your AI leadership to an independent expert for top-tier AI strategy and guidance.
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Meet our Instructors
Ramy AlDamati
ServME ACADEMY
Noor Zahran
Nidal Talib
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