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Course Outline

Foundations of Secure and Ethical AI

  • Core principles of AI security and ethics
  • Identifying common threats and vulnerabilities in AI ecosystems
  • Navigating the regulatory landscape and compliance frameworks

Threat Landscape for AI Agents

  • Risks associated with data poisoning and model manipulation
  • Understanding adversarial attacks targeting AI models
  • Strategies for mitigating AI-specific security threats

Developing Robust and Secure AI Models

  • Integrating security into the AI development lifecycle
  • Applying defensive machine learning methodologies
  • Conducting rigorous validation and testing of AI models

Ethical AI Practices and Fairness

  • Detecting and mitigating bias within AI models
  • Enhancing explainability and transparency in AI decision-making
  • Ensuring responsible deployment of AI solutions

AI Governance, Compliance, and Risk Oversight

  • Adhering to GDPR, CCPA, and the EU 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-conscious approach
  • Monitoring AI models to detect anomalies and vulnerabilities
  • Managing AI security incidents and implementing mitigation strategies

Case Studies and Practical Applications

  • Analyzing past AI security breaches and extracting key lessons
  • Implementing secure AI agents in real-world operational scenarios
  • Adopting best practices to future-proof AI security strategies

Conclusion and Future Pathways

Requirements

  • A solid grasp of core AI and machine learning principles
  • Practical experience with Python and major AI frameworks
  • Fundamental understanding of cybersecurity concepts

Intended Audience

  • AI Developers
  • Security Specialists
  • Compliance Officers
 14 Hours

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Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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