AI Security

Duration: 2 Days

Description

Generative AI introduces a new and rapidly evolving set of security challenges that every organization must understand. This course gives leaders and security-minded professionals a practical foundation in AI security: the threats unique to AI systems, how attackers target them, and the controls organizations can put in place to defend against misuse and data exposure. Participants examine real attack patterns such as prompt injection and data leakage, learn how AI changes the security landscape on both offense and defense, and leave able to assess and strengthen the security of AI use in their organization.
Class time is divided between discussion and threat case studies (65 percent) and applied risk-assessment exercises (35 percent).

Audience

Security leaders, IT and risk managers, compliance professionals, and decision-makers responsible for the safe adoption of AI. A general understanding of organizational security is helpful; deep technical security expertise is not required.

Objectives

• Explain how generative AI changes the organizational security landscape
• Identify the primary threats targeting AI systems and AI users
• Describe common attack techniques such as prompt injection and data exfiltration
• Evaluate data privacy and confidentiality risks in AI workflows
• Apply security controls and best practices for safe AI adoption
• Assess and improve the security posture of AI use in your organization

Prerequisites

There are no strict prerequisites. The following will help participants get the most from the course:

• A general understanding of generative AI concepts
• Basic familiarity with organizational security or IT practices

Course Outline

• Module 1 – Course Introduction
  • Welcome and objectives
  • How AI reshapes the security landscape
  • Offense and defense: AI on both sides
• Module 2 – The AI Threat Landscape
  • What makes AI systems different to secure
  • Threats to AI users versus AI systems
  • The expanding attack surface
  • Realistic risk versus hype
• Module 3 – Attacks Against AI Systems
  • Prompt injection and jailbreaking
  • Data leakage and exfiltration
  • Training-data and model risks
  • Supply-chain and integration risks
• Module 4 – Data Privacy and Confidentiality
  • What happens to data shared with AI tools
  • Sensitive data, secrets, and IP exposure
  • Retention, logging, and third-party handling
  • Safe-use practices for staff
• Module 5 – Defending AI Use
  • Security controls and guardrails
  • Access, identity, and least privilege
  • Monitoring, detection, and incident response
  • Vendor evaluation and configuration
• Module 6 – Assessing Your AI Security Posture
  • Inventorying AI use across the organization
  • Identifying gaps and prioritizing controls
  • Policies, training, and shared responsibility
  • Hands-on exercise: conduct an AI security assessment
• Module 7 – Course Wrap-Up
  • Key takeaways
  • Resources for continued learning
  • Next steps