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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 on 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 decisions
- Ensuring responsible AI deployment
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and 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 in mind
- Monitoring AI models for anomalies and vulnerabilities
- AI security incident response and mitigation
Case Studies and Real-World Applications
- Case studies of AI security breaches and lessons learned
- Implementing secure AI agents in real-world scenarios
- Best practices for future-proofing AI security
Summary and Next Steps
Requirements
- Understanding of AI and machine learning concepts
- Experience with Python and AI frameworks
- Basic knowledge of cybersecurity principles
Audience
- AI developers
- Security specialists
- Compliance officers
14 Hours
Testimonials (1)
Trainer responding to questions on the fly.
