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Course Outline
Introduction to Secure and Ethical AI
- Overview of AI security and ethics
- Common threats and vulnerabilities in AI systems
- Regulatory landscape and compliance frameworks
Security Threats in AI Agents
- Data poisoning and model manipulation
- Adversarial attacks targeting AI models
- Mitigation strategies for AI security threats
Building Robust and Secure AI Models
- Secure AI development lifecycle
- Defensive machine learning techniques
- AI model validation and testing
Ethical AI Development and Fairness
- Bias detection and mitigation in AI models
- Explainability and transparency in AI decision-making
- Ensuring responsible AI deployment
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and the AI Act
- Risk management frameworks for AI security
- Auditing AI models for security and ethical concerns
Secure AI Deployment Best Practices
- Deploying AI agents with security considerations
- Monitoring AI models for anomalies and vulnerabilities
- AI security incident response and mitigation
Case Studies and Real-World Applications
- Analysis of AI security breaches and key lessons
- Implementing secure AI agents in practical scenarios
- 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 relevant AI frameworks
- Foundational knowledge of cybersecurity principles
Target Audience
- AI developers
- Security specialists
- Compliance officers
14 Hours
