Certified Data Privacy and AI Management Professional - CPAMP

This course provides a professional understanding of data privacy related concepts and AI and ML concepts. This essential knowledge is to enable an information security professional to perform better in the age of technology and compliance.

Core Concepts in Data Privacy:

• Defining Privacy: Historical evolution, philosophical underpinnings, and modern interpretations of privacy.
• Types of Data: Understanding personal data, sensitive personal data, non-personal data, anonymized, and pseudonymized data.
• Fundamental Privacy Principles: Deep dive into core principles like consent, purpose limitation, data minimization, accuracy, storage limitation, integrity & confidentiality (security), and accountability.
• Privacy Harms: Exploring different types of privacy violations and their individual and societal impact.
• Introduction to Privacy Enhancing Technologies (PETs) - Overview: A high-level introduction to the concept and categories of PETs."

• Introduction to AI: Definitions, history, and subfields of AI (e.g., Machine Learning, Natural Language Processing, Computer Vision).
• Machine Learning Fundamentals:
• Types of ML: Supervised (classification, regression), Unsupervised (clustering, dimensionality reduction), Reinforcement Learning.
• Key algorithms and their working principles.
• The Machine Learning Pipeline: Data collection, preprocessing, feature engineering, model selection, training, evaluation, and deployment.
• Deep Learning Basics: Neural networks, activation functions, backpropagation, and popular architectures (e.g., CNNs, RNNs).
• Ethical Considerations in AI - Overview: Brief introduction to bias, fairness, transparency, and accountability in AI systems.

The Intersection:

• AI's Demands and Privacy's Constraints
• Data-Intensive AI: Understanding why AI, especially ML, requires vast amounts of data and the implications for data sourcing.
• Privacy Risks Amplified by AI:
• Re-identification: How AI can de-anonymize data.
• Inference Attacks: AI models inferring sensitive attributes or membership in a dataset.
• Model Inversion & Stealing: Extracting training data or model parameters.
• Data Leakage: Unintentional exposure during the AI lifecycle.
• Profiling and Automated Decision-Making: Privacy concerns with AI-driven categorization and judgment.
• Bias in AI and its Link to Privacy: How data collection practices and inherent biases can lead to privacy harms and discriminatory outcomes.

• Privacy by Design in AI Systems: Methodologies for embedding privacy throughout the AI lifecycle.
• Fairness, Accountability, and Transparency (FAT/XAI) in AI:
• Techniques for achieving and measuring fairness.
• Methods for model explainability and interpretability.
• Establishing accountability mechanisms.
• How these concepts support or conflict with privacy.
• Auditing AI for Privacy and Bias: Frameworks and tools for assessing AI systems.
• Case Studies: Analyzing real-world examples of AI systems and their privacy implications across various sectors (e.g., healthcare, finance, surveillance, social media, generative AI)

• Privacy Challenges in Generative AI: Data sourcing, memorization, and output ownership in LLMs and image generators.
• The Ethics of AI Nudging and Persuasion.
• Neuro-rights and Mental Privacy.
• Quantum Computing's Impact on Privacy and AI.
• The Evolving Role of Data Fiduciaries and Privacy Professionals in an AI World.
• Open Research Problems and Societal Debates"

Case Studies and Practical Exercises: Analyzing real-world scenarios

IT and IT security professionals, security analysts, engineers, and architects
IT Security Professionals
Information Security Managers
Network Engineers and Administrators
System Administrators
DevOps Engineers
IT Managers seeking to understand cloud security risks.
Aspiring CIOs / CISOs
Consultants and auditors
Anyone who is looking to specialize in the rapidly evolving field of cloud security

Basic Knowledge of IT and Information Security.
A minimum of 3 years of working experience in IT and information security areas.

1. Enhanced Knowledge and Understanding in both data privacy principles (e.g., GDPR, CCPA, consent, data minimization) and AI/ML fundamentals (e.g., model types, training processes, ethical AI)
2. Learn to identify and analyze the inherent privacy risks associated with AI systems, such as re-identification, inference attacks, bias amplification, and data leakage
3. Gain a deep and integrated understanding of two of the most critical and rapidly evolving fields in technology today
4. Gain the competence to be at leadership levels. Professionals with skills at the intersection of AI and data privacy are in extremely high demand across various industries (tech, healthcare, finance, government, etc.).
5. Opens doors to specialized roles such as AI Privacy Officer, AI Ethicist, Privacy Engineer (with AI focus), Data Governance Manager (for AI), Responsible AI Lead, and AI Policy Advisor.

• Question type: Multiple Choice (MCQs)
• Number of questions: 50 Questions
• Duration: 75 minutes
• Passing criteria: 65% to be scored

1. What is the mode of this course?
The course is available in either modes, classroom or virtual.

2. Is VILT a live training, or do I get access to watch pre-recorded videos?
This is a live virtual instructor-led training (VILT) session where you can interact with the trainer. Pre-recorded videos are not available or permitted.

3. Will I get a refund if I cancel my enrolment?
Please check Cancellation and Refund Policy page.

4. Can the dates of the training be customized?
The training dates are published in advance, although you may let us know your preferred dates for exclusive training by mailing us on [email protected].

5. How does Data Privacy and AI Management Training help?
The training adequately equips you with the necessary knowledge and understanding of data privacy and AI & ML related concepts.

6. To whom is this training relevant?
IT and IT security professionals, security analysts, engineers, and architects
Network Engineers and Administrators
DevOps Engineers
IT Managers seeking to understand cloud security risks
Aspiring CIOs / CISOs
Consultants and auditors

7. Are there any prerequisites to attending the training?
Basic Knowledge of IT and Information Security.
A minimum of 3 years of working experience in IT and information security areas.

8. How are the examinations hosted?
Remotely proctored Online exam.

9. What is the duration of the examination?
Exam duration is 75 minutes.

10. How are participants assessed during the course?
Participants must appear for an online examination at the end of the course, which is remotely proctored.

11. What is the passing criterion for a written examination?
Minimum passing criteria is 65%.
The exam consists of 50 multiple-choice questions.

12. Will I be awarded a certificate for attending the training course?
The Certificate of Successful Completion will be awarded by TUV SUD to participants.

13. Is it possible to retake the online final exam? Under what conditions is this possible?
Students who fail the online examination are allowed one retake examination at no cost.

14. Will I be charged for an online exam retake?
You will have to contact one of the TÜV SÜD representatives to avail one retake exam at no cost.