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Duration 14 hours
Course Outline
Introduction to Secure and Ethical AI
- Overview of AI security and ethical considerations
- Common threats and vulnerabilities within AI systems
- The regulatory landscape and associated compliance frameworks
Security Threats Facing AI Agents
- Data poisoning and model manipulation tactics
- Adversarial attacks targeting AI models
- Strategies for mitigating AI security threats
Constructing Robust and Secure AI Models
- The secure AI development lifecycle
- Defensive machine learning methodologies
- Validation and testing processes for AI models
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Promoting explainability and transparency in AI decision-making
- Safeguarding responsible AI deployment
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and the AI Act
- Risk management frameworks tailored for AI security
- Auditing AI models for security and ethical integrity
Best Practices for Secure AI Deployment
- Deploying AI agents with a strong focus on security
- Monitoring AI models to detect anomalies and vulnerabilities
- Responding to and mitigating AI security incidents
Case Studies and Practical Applications
- Analysis of AI security breaches and key lessons learned
- Implementation of secure AI agents in real-world contexts
- Best practices for future-proofing AI security
Summary and Next Steps
Requirements
- Familiarity with core AI and machine learning concepts
- Practical experience with Python and popular AI frameworks
- Fundamental understanding of cybersecurity principles
Intended Audience
- AI Developers
- Security Specialists
- Compliance Officers
