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Course Outline
Foundations of Secure and Ethical AI
- Overview of AI security and ethical considerations
- Identifying common threats and vulnerabilities in AI systems
- Navigating the regulatory landscape and 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 techniques
- Validation and testing methodologies for AI models
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Promoting explainability and transparency in AI decision-making
- Ensuring responsible AI deployment practices
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 security-first mindset
- Monitoring AI models for anomalies and emerging vulnerabilities
- Responding to and mitigating AI security incidents
Case Studies and Real-World Applications
- Analysis of AI security breaches and key lessons learned
- Implementing secure AI agents in practical scenarios
- Best practices for future-proofing AI security strategies
Conclusion and Path Forward
Requirements
- Familiarity with core AI and machine learning concepts
- Practical experience with Python and major AI frameworks
- Foundational knowledge of cybersecurity principles
Target Audience
- AI Developers
- Security Specialists
- Compliance Officers
14 Hours
