Identifying Cyber Risk and Vulnerabilities in AI eLearning

This course covers Cyber Risks & Vulnerabilities and will introduce you to the concepts and key terms you need to know for a basic understanding of the unique security concerns and risks associated with AI applications. Whether your job is to Lead, Drive, Create, Facilitate, Embed, or Employ AI, this course will create a firm baseline understanding of the risks and security needs related to the continued adoption, integration, and use of AI.

Public Classroom Pricing


GSA Price: $18.25

Group Rate: $25

Private Group Pricing

Have a group of 5 or more students? Request special pricing for private group training today.

Part 1: Security vs Risk

  • The relationship between security and risk
  • How the value of an asset affects the need for security
  • How the likelihood and severity of consequences influence risk appetite
  • Defense use cases where AI is being used and resulting security concerns
  • Risks associated with stolen or hacked technology
  • Why U.S. technological superiority is at risk because of AI

Part 2: The fours ways to Think about AI Security

  • Areas of society that are vulnerable to AI-related weaknesses
  • Four distinct ways to approach AI-related risk
  • Examples of basic IT security problems that threaten AI
  • An introduction to vulnerabilities specific to AI technology

Part 3: The limitations of AI systems

  • AI’s dependence on data and limitations created as a result
  • Examples of data characteristics that create specific limitations for AI
  • How AI learns and operates in very narrow ways
  • AI’s black box problem
  • Examples of common software security problems that apply to AI
  • Why the limits of AI’s narrow functionality leave it vulnerable

Part 4: Risk and vulnerabilities inherent in AI Applications

  • Types of attack specific to AI technology
  • AI input attacks
  • AI data poisoning attacks
  • Examples of how AI attacks operate
  • How increasing AI use creates a distinct attack surface
  • Risks and vulnerabilities created as a result of how AI is being used

Part 5: How Attackers are Leveraging AI and Machine Learning

  • The risks of hostile AI
  • Adversarial machine learning as a security risk
  • How hostile AI magnifies the risk of existing AI vulnerabilities
  • Common goals of an attacker
  • Examples of how AI-related attacks achieve an attacker’s goals

Part 6: Implementing better security and How AI and Machine Learning can help

  • Key concepts of IT automation
  • Four questions to identify opportunities for AI to help with security
  • IT security needs well suited for improvement through automation and AI
  • Specific types of IT automation made possible by AI
  • How AI can help identify vulnerabilities and threats
  • Examples of how data visualization can help with AI-related security concerns

Part 7: Final assessment

Professionals who would benefit from this training include:

  • Cybersecurity Analyst
  • Vulnerability Analyst
  • IT Auditor
  • IT Risk Manager
  • Vulnerability Analyst
  • IT Manager
  • Security Ops Analyst

  • Understand Security vs. Risk
  • Learn four ways to think about AI security
  • Review limitations of AI systems
  • Learn about particular risks and vulnerabilities inherent in AI applications
  • Understand how attackers are using - or could use - AI and Machine Learning against us
  • Learn how to implement better security, and learn how AI/ML can help

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