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Course Outline

Introduction to Secure and Ethical AI

  • Overview of AI security and ethical considerations
  • Common threats and vulnerabilities within AI systems
  • The regulatory landscape and associated compliance frameworks

Security Threats Facing AI Agents

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

Constructing Robust and Secure AI Models

  • The secure AI development lifecycle
  • Defensive machine learning methodologies
  • Validation and testing processes for AI models

Ethical AI Development and Fairness

  • Detecting and mitigating bias in AI models
  • Promoting explainability and transparency in AI decision-making
  • Safeguarding responsible AI deployment

AI Governance, Compliance, and Risk Management

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

Best Practices for Secure AI Deployment

  • Deploying AI agents with a strong focus on security
  • Monitoring AI models to detect anomalies and vulnerabilities
  • Responding to and mitigating AI security incidents

Case Studies and Practical Applications

  • Analysis of AI security breaches and key lessons learned
  • Implementation of secure AI agents in real-world contexts
  • Best practices for future-proofing AI security

Summary and Next Steps

Requirements

  • Familiarity with core AI and machine learning concepts
  • Practical experience with Python and popular AI frameworks
  • Fundamental understanding of cybersecurity principles

Intended Audience

  • AI Developers
  • Security Specialists
  • Compliance Officers
 14 Hours

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