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Course Outline
Foundations of Secure and Ethical AI
- Introduction to AI security and ethical principles
- Identifying common threats and vulnerabilities in AI systems
- Navigating the regulatory environment and compliance structures
Security Threats Facing AI Agents
- Countering data poisoning and model manipulation
- Defending against adversarial attacks on AI models
- Strategies for mitigating AI security risks
Developing Robust and Secure AI Models
- Integrating security into the AI development lifecycle
- Applying defensive machine learning methodologies
- Validating and testing AI models for security
Ethical AI Development and Equity
- Detecting and reducing bias within AI models
- Enhancing explainability and transparency in AI decision-making
- Promoting responsible AI deployment practices
AI Governance, Compliance, and Risk Oversight
- Meeting requirements under GDPR, CCPA, and the AI Act
- Implementing risk management frameworks for AI security
- Auditing AI models for security and ethical integrity
Best Practices for Secure AI Deployment
- Deploying AI agents with security as a core priority
- Monitoring AI models to detect anomalies and weaknesses
- Managing AI security incidents and executing mitigation plans
Case Studies and Practical Applications
- Analyzing AI security breaches and extracting key lessons
- Applying secure AI agent strategies in real-world contexts
- Adopting best practices to future-proof AI security
Conclusion and Forward-Looking Steps
Requirements
- Familiarity with AI and machine learning fundamentals
- Proficiency with Python and relevant AI frameworks
- Foundational knowledge of cybersecurity concepts
Target Audience
- AI developers
- Security professionals
- Compliance managers
14 Hours
