Date: 6/2/2026
Time: 12:00 PM - 2:00 PM EST
CPE Credit: 2 hours

This course provides a concise overview of the security challenges and solutions in safeguarding artificial intelligence systems, with a particular focus on protecting machine learning models and sensitive data across the entire AI lifecycle. As AI becomes deeply embedded in financial, legal, and operational workflows, the risks posed by adversarial attacks—ranging from data poisoning and model theft to prompt injection and synthetic media—are escalating rapidly. Participants will learn how these emerging threats impact model integrity, decision accuracy, and organizational trust, and will explore practical strategies for mitigating these risks through governance, human oversight, and AI-specific cybersecurity frameworks.

Topics Covered:

• Nine dimensions of adversarial machine learning threats
   
• Data poisoning and bias injection techniques
   
• AI supply chain vulnerabilities and model theft
   
• Deepfakes, synthetic media, and disinformation
   
• Securing machine learning pipelines and operations
   
• Best practices for AI security governance
   
• AI risk frameworks for accountants and advisors
   

• Identify key adversarial threats targeting AI systems
   
• Explain how data poisoning compromises model integrity
   
• Evaluate the impact of prompt injection on large language models
   
• Analyze vulnerabilities across AI supply chains and model types
   
• Apply governance controls across the AI lifecycle
   
• Implement human-in-the-loop safeguards for critical AI workflows
   
• Design AI security strategies aligned with trust, ethics, and accuracy

Prerequisite Knowledge: Basic understanding of artificial intelligence concepts and cybersecurity fundamentals is recommended.
Field of Study: Information Technology