Building Secure and Ethical AI Agents

• 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

AI security is a critical aspect of AI development that ensures AI agents operate safely, ethically, and in compliance with regulations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers, security specialists, and compliance officers who wish to design and implement secure AI agents while addressing ethical concerns and robustness.

By the end of this training, participants will be able to:

• Understand the security risks and ethical challenges in AI agent development.
• Implement security-first design principles for AI models.
• Apply adversarial robustness techniques to prevent attacks on AI agents.
• Ensure compliance with ethical AI guidelines and regulatory standards.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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