Course Outline

Introduction to Secure and Ethical AI

  • Overview of AI security and ethics
  • Common threats and vulnerabilities in AI systems
  • Regulatory landscape and compliance frameworks

Security Threats in AI Agents

  • Data poisoning and model manipulation
  • Adversarial attacks on AI models
  • Mitigation strategies for AI security threats

Building Robust and Secure AI Models

  • Secure AI development lifecycle
  • Defensive machine learning techniques
  • AI model validation and testing

Ethical AI Development and Fairness

  • Bias detection and mitigation in AI models
  • Explainability and transparency in AI decisions
  • Ensuring responsible AI deployment

AI Governance, Compliance, and Risk Management

  • Compliance with GDPR, CCPA, and AI Act
  • Risk management frameworks for AI security
  • Auditing AI models for security and ethical concerns

Secure AI Deployment Best Practices

  • Deploying AI agents with security in mind
  • Monitoring AI models for anomalies and vulnerabilities
  • AI security incident response and mitigation

Case Studies and Real-World Applications

  • Case studies of AI security breaches and lessons learned
  • Implementing secure AI agents in real-world scenarios
  • Best practices for future-proofing AI security

Summary and Next Steps

Requirements

  • Understanding of AI and machine learning concepts
  • Experience with Python and AI frameworks
  • Basic knowledge of cybersecurity principles

Audience

  • AI developers
  • Security specialists
  • Compliance officers
 14 Hours

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