AI Security Readiness
Duration: 1 Day | 6-8 Hours
Delivery: Instructor-Led Virtual or Onsite
Prepare your organization to adopt and use artificial intelligence securely. AI Security Readiness helps participants identify security, privacy, data protection, and governance risks associated with AI technologies and generative AI tools. The course explores how to evaluate current AI use, identify gaps in policies and controls, assess third-party AI services, protect sensitive information, and develop a practical roadmap for secure AI adoption.
Students Will Learn To:
Ideal for: CISOs, cybersecurity leaders, IT managers, risk and compliance professionals, AI program leaders, government organizations, and teams responsible for AI adoption and governance.
Delivery: Instructor-Led Virtual or Onsite
Prepare your organization to adopt and use artificial intelligence securely. AI Security Readiness helps participants identify security, privacy, data protection, and governance risks associated with AI technologies and generative AI tools. The course explores how to evaluate current AI use, identify gaps in policies and controls, assess third-party AI services, protect sensitive information, and develop a practical roadmap for secure AI adoption.
Students Will Learn To:
- Assess how AI is currently being used across an organization
- Identify security and privacy risks associated with generative AI adoption
- Identify sensitive-data risks and potential AI data leakage scenarios
- Evaluate existing security policies and controls for AI-related gaps
- Assess risks associated with third-party AI platforms and services
- Identify appropriate governance and oversight responsibilities
- Develop practical guidelines for acceptable AI use
- Understand considerations for government, regulated, and sensitive environments
- Prioritize security improvements based on organizational AI risk
- Develop an actionable AI security readiness roadmap
Ideal for: CISOs, cybersecurity leaders, IT managers, risk and compliance professionals, AI program leaders, government organizations, and teams responsible for AI adoption and governance.
AI Fundamentals for Cybersecurity Professionals
Duration: 1 Day | 6-8 Hours
Delivery: Instructor-Led Virtual or Onsite
Understand how artificial intelligence is changing both cyber defense and the threat landscape. AI Fundamentals for Cybersecurity Professionals introduces the essential AI and generative AI concepts security professionals need without requiring a data science or machine learning background. Participants will explore how AI and large language models work, how attackers and defenders are using AI, emerging AI-specific threats, and practical applications for cybersecurity operations.
Students Will Learn To:
Ideal for: Cybersecurity professionals, SOC analysts, incident responders, security engineers, IT professionals, security managers, and government and DIB cybersecurity teams.
Delivery: Instructor-Led Virtual or Onsite
Understand how artificial intelligence is changing both cyber defense and the threat landscape. AI Fundamentals for Cybersecurity Professionals introduces the essential AI and generative AI concepts security professionals need without requiring a data science or machine learning background. Participants will explore how AI and large language models work, how attackers and defenders are using AI, emerging AI-specific threats, and practical applications for cybersecurity operations.
Students Will Learn To:
- Understand foundational AI, machine learning, generative AI, and LLM concepts
- Explain how large language models operate at a practical level
- Recognize how threat actors can use generative AI
- Identify emerging attacks targeting AI-enabled systems
- Understand prompt injection, data exposure, model manipulation, and related threats
- Use AI to assist with threat research and analysis
- Explore AI-assisted security operations and incident response
- Apply AI to vulnerability and security analysis activities
- Evaluate AI-generated cybersecurity information for accuracy and reliability
- Identify security and governance considerations when introducing AI into cyber operations
Ideal for: Cybersecurity professionals, SOC analysts, incident responders, security engineers, IT professionals, security managers, and government and DIB cybersecurity teams.
AI Red Team & Secure AI Applications
Duration: 2 Days | 12-16 Hours
Delivery: Instructor-Led Virtual or Onsite | Hands-On
Learn how to identify, test, and reduce security weaknesses in AI-enabled applications. AI Red Teaming & Secure AI Applications introduces participants to adversarial testing techniques for generative AI and large language model applications. Through practical scenarios and exercises, participants learn how to evaluate AI safeguards, identify vulnerabilities, document findings, and apply secure design principles to build more resilient AI applications.
Students Will Learn To:
Ideal for: Security engineers, penetration testers, red team professionals, application security teams, developers, AI engineers, security architects, and technical professionals responsible for developing or evaluating AI-enabled systems.
Delivery: Instructor-Led Virtual or Onsite | Hands-On
Learn how to identify, test, and reduce security weaknesses in AI-enabled applications. AI Red Teaming & Secure AI Applications introduces participants to adversarial testing techniques for generative AI and large language model applications. Through practical scenarios and exercises, participants learn how to evaluate AI safeguards, identify vulnerabilities, document findings, and apply secure design principles to build more resilient AI applications.
Students Will Learn To:
- Understand the objectives and methodology of AI red teaming
- Identify attack surfaces in LLM and generative AI applications
- Test applications for direct and indirect prompt injection vulnerabilities
- Evaluate AI systems for sensitive information disclosure
- Identify weaknesses associated with insecure AI integrations and tools
- Explore risks created by excessive permissions and autonomous AI actions
- Test safeguards and evaluate whether security controls behave as intended
- Develop realistic adversarial test cases for AI applications
- Document, prioritize, and communicate AI security findings
- Apply secure design and development principles to AI-enabled applications
- Understand recognized AI and LLM application security guidance
- Incorporate AI security testing into development and security processes
Ideal for: Security engineers, penetration testers, red team professionals, application security teams, developers, AI engineers, security architects, and technical professionals responsible for developing or evaluating AI-enabled systems.
