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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

  • Familiarity with AI and machine learning concepts.
  • Proficiency in Python and AI frameworks.
  • Foundational knowledge of cybersecurity principles.

Target Audience

  • AI developers.
  • Security specialists.
  • Compliance officers.
 14 Hours

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