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Course Outline
Introduction to Secure and Ethical AI
- Overview of AI security landscapes and ethical considerations.
- Identification of common threats and vulnerabilities in AI systems.
- Navigating the regulatory environment and compliance frameworks.
Security Threats Facing AI Agents
- Analyzing data poisoning and model manipulation tactics.
- Understanding adversarial attacks targeting AI models.
- Developing mitigation strategies for specific AI security threats.
Developing Robust and Secure AI Models
- Integrating security into 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 within AI models.
- Enhancing explainability and transparency in AI decision-making.
- Ensuring responsible deployment practices for AI systems.
AI Governance, Compliance, and Risk Management
- Meeting compliance requirements for GDPR, CCPA, and the AI Act.
- Implementing risk management frameworks specific to AI security.
- Auditing AI models to address security and ethical concerns.
Best Practices for Secure AI Deployment
- Deploying AI agents with security as a primary design priority.
- Monitoring AI models for anomalies and emerging vulnerabilities.
- Managing AI security incidents and applying effective mitigation measures.
Case Studies and Real-World Applications
- Reviewing case studies of AI security breaches and deriving key lessons.
- Implementing secure AI agents in practical, real-world scenarios.
- Adopting best practices to future-proof AI security strategies.
Summary and Next Steps
Requirements
- A solid grasp of core AI and machine learning concepts.
- Practical experience with Python and standard AI frameworks.
- Familiarity with foundational cybersecurity principles.
Target Audience
- AI Developers
- Security Specialists
- Compliance Officers
14 Hours
