- An understanding of basic cybersecurity principles
- Experience with IT governance or risk management frameworks
- Familiarity with general AI concepts is helpful but not required
Audience
- IT security teams
- Risk managers
- Compliance professionals
AI Security and Risk Management is the practice of identifying, mitigating, and governing security threats, compliance risks, and operational exposures in AI-driven systems and workflows.
This instructor-led, live training (online or onsite) is aimed at beginner-level IT security, risk, and compliance professionals who wish to understand foundational AI security concepts, threat vectors, and global frameworks such as NIST AI RMF and ISO/IEC 42001.
By the end of this training, participants will be able to:
- Understand the unique security risks introduced by AI systems.
- Identify threat vectors such as adversarial attacks, data poisoning, and model inversion.
- Apply foundational governance models like the NIST AI Risk Management Framework.
- Align AI use with emerging standards, compliance guidelines, and ethical principles.
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.
Foundations of AI and Security
- What makes AI systems unique from a security perspective
- Overview of AI lifecycle: data, training, inference, and deployment
- Basic taxonomy of AI risks: technical, ethical, legal, and organizational
AI-Specific Threat Vectors
- Adversarial examples and model manipulation
- Model inversion and data leakage risks
- Data poisoning during training phases
- Risks in generative AI (e.g., LLM misuse, prompt injection)
Security Risk Management Frameworks
- NIST AI Risk Management Framework (NIST AI RMF)
- ISO/IEC 42001 and other AI-specific standards
- Mapping AI risk to existing enterprise GRC frameworks
AI Governance and Compliance Principles
- AI accountability and auditability
- Transparency, explainability, and fairness as security-relevant properties
- Bias, discrimination, and downstream harms
Enterprise Readiness and AI Security Policies
- Defining roles and responsibilities in AI security programs
- Policy elements: development, procurement, use, and retirement
- Third-party risk and supplier AI tool usage
Regulatory Landscape and Global Trends
- Overview of the EU AI Act and international regulation
- U.S. Executive Order on Safe, Secure, and Trustworthy AI
- Emerging national frameworks and sector-specific guidance
Optional Workshop: Risk Mapping and Self-Assessment
- Mapping real-world AI use cases to NIST AI RMF functions
- Performing a basic AI risk self-assessment
- Identifying internal gaps in AI security readiness
Summary and Next Steps
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