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Duration 14 hours
Course Outline
Foundations of Secure and Ethical AI
- Overview of AI security and ethical frameworks
- 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
Developing Robust and Secure AI Models
- Integrating security into the AI development lifecycle
- Employing defensive machine learning techniques
- Validating and testing AI models for security
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Enhancing explainability and transparency in AI decisions
- Safeguarding responsible AI deployment practices
AI Governance, Compliance, and Risk Management
- Meeting compliance requirements under GDPR, CCPA, and the AI Act
- Establishing risk management frameworks for AI security
- Auditing AI models for security and ethical integrity
Best Practices for Secure AI Deployment
- Deploying AI agents with a security-centric mindset
- Monitoring AI models for anomalies and emerging vulnerabilities
- Responding to and mitigating AI security incidents
Case Studies and Practical Applications
- Reviewing AI security breach case studies and key takeaways
- Implementing secure AI agents in real-world contexts
- Adopting best practices to future-proof AI security
Conclusion and Path Forward
Requirements
- A solid grasp of AI and machine learning fundamentals
- Practical experience with Python and relevant AI frameworks
- Foundational knowledge of cybersecurity principles
Intended Audience
- AI Developers
- Security Specialists
- Compliance Officers
