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Course Outline

Foundations of Secure and Ethical AI

  • Overview of AI security and ethical frameworks
  • Identifying common threats and vulnerabilities in AI systems
  • Navigating the regulatory landscape and compliance frameworks

Security Threats Facing AI Agents

  • Data poisoning and model manipulation tactics
  • Adversarial attacks targeting AI models
  • Strategies for mitigating AI security threats

Developing Robust and Secure AI Models

  • Integrating security into the AI development lifecycle
  • Employing defensive machine learning techniques
  • Validating and testing AI models for security

Ethical AI Development and Fairness

  • Detecting and mitigating bias in AI models
  • Enhancing explainability and transparency in AI decisions
  • Safeguarding responsible AI deployment practices

AI Governance, Compliance, and Risk Management

  • Meeting compliance requirements under GDPR, CCPA, and the AI Act
  • Establishing risk management frameworks for AI security
  • Auditing AI models for security and ethical integrity

Best Practices for Secure AI Deployment

  • Deploying AI agents with a security-centric mindset
  • Monitoring AI models for anomalies and emerging vulnerabilities
  • Responding to and mitigating AI security incidents

Case Studies and Practical Applications

  • Reviewing AI security breach case studies and key takeaways
  • Implementing secure AI agents in real-world contexts
  • Adopting best practices to future-proof AI security

Conclusion and Path Forward

Requirements

  • A solid grasp of AI and machine learning fundamentals
  • Practical experience with Python and relevant AI frameworks
  • Foundational knowledge of cybersecurity principles

Intended Audience

  • AI Developers
  • Security Specialists
  • Compliance Officers
 14 Hours

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