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Duration 14 hours
Course Outline
Foundations of Secure and Ethical AI
- Introduction to AI security and ethics
- Identifying common threats and vulnerabilities in AI systems
- Navigating the regulatory environment and compliance frameworks
Security Threats Targeting AI Agents
- Data poisoning and model manipulation tactics
- Adversarial attacks against AI models
- Strategies for mitigating AI security risks
Developing Robust and Secure AI Models
- The secure AI development lifecycle
- Defensive machine learning methodologies
- Validation and testing of AI models
Ethical AI Practices and Fairness
- Detecting and reducing bias in AI models
- Promoting explainability and transparency in AI decision-making
- Safeguarding responsible AI deployment
AI Governance, Compliance, and Risk Oversight
- Compliance with GDPR, CCPA, and the AI Act
- 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-first mindset
- Monitoring AI models for anomalies and vulnerabilities
- Incident response and mitigation for AI security breaches
Case Studies and Practical Applications
- Analyzing AI security incidents and extracting key lessons
- Implementing secure AI agents in real-world contexts
- Strategies for future-proofing AI security
Conclusion and Path Forward
Requirements
- Familiarity with AI and machine learning fundamentals
- Proficiency in Python and related AI frameworks
- Foundational understanding of cybersecurity concepts
Target Audience
- AI Engineers
- Security Specialists
- Compliance Officers
