Data Privacy, Security, Ethics and Compliance in a Data-Driven World

Overview

Explore ethical challenges in data-driven technologies, focusing on privacy, security, and compliance. Learn tools and strategies to mitigate risks and maintain integrity in the evolving landscape of AI and big data.

Syllabus

• Introduction to Data Privacy, Security, Ethics, and Compliance
Overview of data-driven technologies
Key concepts and definitions
• Ethical Challenges in Data-Driven Technologies
Understanding ethical dilemmas in AI
Case studies of ethical breaches in AI and data use
• Privacy in the Age of Big Data
Principles of data privacy
Privacy by design and default
Tools for ensuring data anonymity and minimizing collection
• Data Security Fundamentals
Common threats to data security
Best practices for data protection
Encryption and secure data storage methods
• Compliance and Legal Frameworks
Overview of global data protection regulations (GDPR, CCPA, etc.)
Understanding compliance requirements and implications
Strategies for achieving and maintaining compliance
• Mitigating Risks in AI and Big Data
Risk assessment and management in AI applications
Developing and implementing data governance frameworks
Conducting impact assessments and audits
• Case Studies: Successes and Failures
Analysis of real-world case studies on data privacy and security
Lessons learned from compliance failures and successes
• Future Trends and Challenges
Emerging trends in AI and data privacy
Anticipating and preparing for future regulation changes
The evolving role of ethics in AI development
• Practical Strategies for Maintaining Integrity
Building ethical AI models and algorithms
Fostering a culture of privacy and ethics in organizations
• Conclusion and Course Review
Recap of key concepts and strategies
Discussion: future of data privacy, security, and ethics
• Final Project
Develop a comprehensive strategy to address privacy, security, ethics, and compliance in a hypothetical organization or project scenario

Subjects

Conference Talks