Intermediate
AI 321 AI Security & Ethics Master Class
This course equips professionals with essential knowledge to navigate AI’s security risks and ethical challenges. Participants will explore foundational AI concepts, risk mitigation strategies, and governance frameworks. The programme blends technical insight with practical tools for responsible AI deployment. Ideal for leaders seeking to drive secure and ethical innovation in their organisations.
Curriculum
- 11 Sections
- 91 Lessons
- 0m Duration
Welcome
1 Lesson
- Welcome
Module 1: Introduction to AI Security and Ethics
2 Lessons
- 1: Course Introduction and Objectives
- 2: Fundamentals of Artificial Intelligence
Module 2: Ethical Principles in AI
3 Lessons
- 1: Understanding Ethics in AI
- 2: Core Ethical Principles
- 3: Bias and Fairness in AI
Module 3: AI Security Fundamentals
4 Lessons
- 1: Introduction to AI Security
- 2: Vulnerabilities in AI Systems
- 3: Securing AI Pipelines
- 3.2: Securing AI Deployment Pipelines
Module 4: Privacy and Data Protection in AI
6 Lessons
- 1: Data Privacy Principles
- 1.2: Privacy Risks in AI and How to Mitigate Them
- 2: Privacy-Preserving AI Techniques
- 2.2: Homomorphic Encryption – AI Can Compute on Encrypted Data
- 3: Ethical Data Collection and Usage
- 3.2: Balancing Data Utility and Privacy Concerns
Module 5: Governance and Accountability in AI
10 Lessons
- 1: AI Governance Frameworks
- 1.2 : Case Study: Implementing AI Governance at ACME INNOVA
- 1.3 : Stakeholder Engagement and Communication
- 2: Accountability in AI Systems
- 2.2: Transparency and Explainability
- 2.3: Building a Culture of Accountability
- 3: Building Ethical AI Organizations
- 3.2: Creating an Ethical AI Governance Committee
- 3.3: Promoting Diversity and Inclusion in AI Teams
- 3.4: Case Study: Building an Ethical AI Organization at ACME INNOVA
Module 6: AI Security Best Practices
16 Lessons
- 1: Introduction to AI Security
- 1.2: Challenges in AI Security
- 1.3: Adversarial Machine Learning
- 1.4: Incident Response and Recovery
- 2: Securing AI Models
- 2.2: What is Adversarial Training?
- 2.3: Continuous Monitoring of AI Model Performance
- 2.4: Incident Detection and Response for AI Systems
- 3: Protecting Data in AI Systems
- 3.2: Access Controls and Authentication
- 3.3: Implementing Data Access Audits
- 3.4: Secure Data Transfer Protocols
- 4: Responding to AI Security Incidents
- 4.2: Containment Strategies
- 4.3: Legal & Regulatory Response
- 4.4: Leveraging AI for Incident Response
Module 7: Ethical AI Deployment
15 Lessons
- 1: Introduction to Ethical AI Deployment
- 1.2: Key Components of Ethical AI Deployment
- 1.3: Stakeholder Engagement in Ethical Deployment
- 1.4: Case Study: Overcoming Ethical Deployment Challenges at CovenantTech Solutions
- 2: Best Practices for Ethical AI Deployment
- 2.2: Implementing Robust Data Privacy Measures
- 2.3: Leveraging Technology for Ethical Deployment
- 2.4: Best Practices Summary
- 3: Ethical Considerations in AI Deployment
- 3.2: Addressing Algorithmic Bias and Fairness
- 3.3: Fostering an Ethical AI Culture
- 4: Monitoring and Maintaining Ethical AI Systems
- 4.2: Utilizing AI for Ethical Compliance Checks
- 4.3: Fostering a Culture of Ethical Continuous Improvement
- 4.4: Addressing Emerging Ethical Challenges
Module 8: Ethical AI in Decision-Making
13 Lessons
- 1: Introduction to Ethical AI in Decision-Making
- 1.2: Case Study: Ethical Decision-Making at Nexora Systems
- 1.3: Ensuring Transparency and Explainability
- 1.4: Case Study: Overcoming Ethical Challenges in AI Decision-Making at Nexora Systems
- 2: Implementing Fairness in AI Decision-Making
- 2.2: Tools and Techniques for Fairness Implementation
- 2.3: Inclusive Design and Development Practices
- 3: Transparency and Explainability in AI Decisions
- 3.2: Communicating AI Decisions to Stakeholders
- 3.3: Case Study: Enhancing Explainability in AI Decision-Making at Nexora Systems
- 4: Accountability and Responsibility in AI Decision-Making
- 4.2: Implementing Accountability Mechanisms
- 4.3: Case Study: Ensuring Accountability in AI Decision-Making at Nexora Systems
Module 9: Intellectual Property (IP) Considerations for Corporations in the AI Era
20 Lessons
- 1: Introduction to Intellectual Property in AI
- 1.2: Case Study: IP Strategy at Agape Tech Solutions
- 2: Patents and AI Innovations
- 2.2: The Patent Application Process
- 2.3: Best Practices for Patent Protection in AI
- 3: Copyright and AI-Generated Content
- 3.2: Challenges of Copyright in AI Creations
- 3.3: Best Practices for Copyright Protection in AI
- 4: Trade Secrets and AI
- 4.2: Protecting Trade Secrets in AI Development
- 4.3: Tools and Resources for Trade Secret Protection
- 5: Licensing and Collaboration in AI Development
- 5.2: Collaborative Development and IP Sharing
- 5.3: Tools and Resources for Licensing Management
- 6: IP Management Strategies for AI Corporations
- 6.2: IP Portfolio Management
- 6.3: Tools and Resources for IP Management
- 7: Legal Frameworks and Compliance in IP for AI
- 7.2: Navigating International IP Protection
- 7.3: Tools and Resources for Legal Compliance and IP Protection
Module 10: Course Conclusion and Key Takeaways
1 Lesson
- Final Section: Course Conclusion and Key Takeaways
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