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Duration 14 hours
Course Outline
Introduction to Secure and Ethical AI
- Foundations of AI security and ethics.
- Identification of common threats and vulnerabilities in AI systems.
- Navigating the regulatory landscape and compliance frameworks.
Security Threats in AI Agents
- Understanding data poisoning and model manipulation.
- Analysis of adversarial attacks on AI models.
- Strategies for mitigating AI security threats.
Building Robust and Secure AI Models
- Implementing a secure AI development lifecycle.
- Applying defensive machine learning techniques.
- Conducting rigorous AI model validation and testing.
Ethical AI Development and Fairness
- Detecting and mitigating bias in AI models.
- Enhancing explainability and transparency in AI decision-making.
- Ensuring responsible AI deployment practices.
AI Governance, Compliance, and Risk Management
- Ensuring compliance with GDPR, CCPA, and the AI Act.
- Developing risk management frameworks for AI security.
- Auditing AI models for security and ethical integrity.
Secure AI Deployment Best Practices
- Deploying AI agents with a security-first mindset.
- Monitoring AI models for anomalies and potential vulnerabilities.
- Managing AI security incidents and implementing mitigation strategies.
Case Studies and Real-World Applications
- Reviewing case studies of AI security breaches and key lessons learned.
- Applying secure AI agent design in real-world scenarios.
- Establishing best practices for future-proofing AI security.
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning fundamentals.
- Practical experience with Python and various AI frameworks.
- Foundational knowledge of cybersecurity principles.
Target Audience
- AI developers.
- Security specialists.
- Compliance officers.
