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Duration 14 hours (2 days)
Course Outline
Introduction to Secure and Ethical AI
- Overview of AI security and ethical considerations
- Identification of common threats and vulnerabilities in AI systems
- Analysis of the regulatory landscape and compliance frameworks
Security Threats Facing AI Agents
- Data poisoning and model manipulation risks
- Adversarial attacks targeting AI models
- Strategies for mitigating AI security threats
Constructing Robust and Secure AI Models
- Implementing a secure AI development lifecycle
- Techniques in defensive machine learning
- Processes for AI model validation and testing
Ethical AI Development and Fairness
- Methods for detecting and mitigating bias in AI models
- Ensuring explainability and transparency in AI decision-making
- Guidelines for responsible AI deployment
AI Governance, Compliance, and Risk Management
- Compliance strategies for GDPR, CCPA, and the AI Act
- Risk management frameworks tailored to AI security
- Auditing procedures for AI model security and ethics
Best Practices for Secure AI Deployment
- Deploying AI agents with a strong security focus
- Monitoring AI models for anomalies and vulnerabilities
- Incident response and mitigation strategies for AI security
Case Studies and Real-World Applications
- Review of AI security breaches and key lessons learned
- Application of secure AI agent design in real-world scenarios
- Best practices for future-proofing AI security measures
Summary and Next Steps
Requirements
- Familiarity with core AI and machine learning concepts
- Proficiency with Python and common AI frameworks
- Foundational understanding of cybersecurity principles
Target Audience
- AI Developers
- Security Specialists
- Compliance Officers
