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Course Outline
Foundations of Secure and Ethical AI
- Core concepts in AI security and ethics
- Identifying common threats and vulnerabilities within AI systems
- Understanding the regulatory landscape and compliance frameworks
Security Threats Facing AI Agents
- Addressing data poisoning and model manipulation
- Countering adversarial attacks on AI models
- Developing mitigation strategies for AI security risks
Developing Robust and Secure AI Models
- The secure AI development lifecycle
- Techniques in defensive machine learning
- Validation and testing protocols for AI models
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Promoting explainability and transparency in AI decision-making
- Ensuring the responsible deployment of AI solutions
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and the AI Act
- Risk management frameworks specific to 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
- Response and mitigation strategies for AI security incidents
Case Studies and Practical Applications
- Analysis of past AI security breaches and key lessons
- Implementing secure AI agents in real-world contexts
- Strategies for future-proofing AI security measures
Conclusions and Recommended Next Steps
Requirements
- Foundational understanding of AI and machine learning principles
- Practical experience with Python and related AI frameworks
- Basic familiarity with cybersecurity fundamentals
Target Audience
- AI developers
- Security professionals
- Compliance officers
14 Hours
