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Duration 14 hours
Course Outline
Introduction to Secure and Ethical AI
- Fundamentals of AI security and ethical frameworks
- Identifying common threats and vulnerabilities within AI systems
- Overview of the regulatory environment and compliance structures
Security Threats in AI Agents
- Countering data poisoning and model manipulation tactics
- Addressing adversarial attacks targeting AI models
- Strategies for mitigating security threats in AI ecosystems
Building Robust and Secure AI Models
- Integrating security throughout the AI development lifecycle
- Applying defensive machine learning techniques
- Validating and testing AI models for security integrity
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models
- Promoting explainability and transparency in AI decision-making
- Ensuring responsible deployment of AI solutions
AI Governance, Compliance, and Risk Management
- Compliance with GDPR, CCPA, and the AI Act
- Implementing risk management frameworks for AI security
- Auditing AI models for security and ethical alignment
Secure AI Deployment Best Practices
- Deploying AI agents with a strong security focus
- Monitoring AI models for anomalies and potential vulnerabilities
- Managing AI security incidents and applying mitigation measures
Case Studies and Real-World Applications
- Analyzing AI security breaches and extracting key lessons
- Applying secure AI agent design in real-world contexts
- Adopting best practices to future-proof AI security
Summary and Next Steps
Requirements
- Familiarity with core AI and machine learning concepts
- Practical experience with Python and associated AI frameworks
- Foundational understanding of cybersecurity principles
Target Audience
- AI Developers
- Security Specialists
- Compliance Officers
