This program guides cybersecurity professionals in applying maturity model frameworks to AI systems. Learn to secure the AI supply chain and ensure model integrity. Understand Zero Trust principles adapted for AI environments. Develop skills in AI-specific threat modeling and applying effective cyber controls. The curriculum maps maturity levels (1-5) to established frameworks like the NIST and DoD AI Risk Management Frameworks (RMF). Enhance your ability to assess and improve the cybersecurity posture of AI technologies within your organization. Prepare for advanced roles in AI security governance.
Audience: Cybersecurity professionals seeking to specialize in AI security assessment and maturity modeling.
Learning Objectives: Upon completion, participants will be able to:
- Understand cybersecurity maturity models and their application to AI.
- Assess and secure the AI supply chain.
- Verify AI model integrity and provenance.
- Apply Zero Trust Architecture principles to AI systems.
- Conduct threat modeling specific to AI vulnerabilities.
- Map AI security controls to maturity levels based on NIST/DoD AI RMF.
Program Modules:
Module 1: Foundations of AI Cybersecurity Maturity
- Introduction to AI Cybersecurity concepts.
- Overview of CMMC and its relevance.
- Understanding the NIST AI Risk Management Framework.
- DoD Responsible AI principles and guidelines.
- The role of maturity models in AI security.
- Defining the scope of CAIMMC.
Module 2: AI Supply Chain Security Management
- Identifying risks in the AI supply chain.
- Security requirements for data acquisition.
- Securing AI development environments.
- Third-party AI component risk assessment.
- Monitoring and auditing AI supply chain activities.
- Best practices for secure AI deployment pipelines.
Module 3: Ensuring AI Model Integrity and Provenance
- Threats to AI model integrity (e.g., poisoning, evasion).
- Techniques for verifying model robustness.
- Establishing data and model provenance tracking.
- Secure model storage and access controls.
- Detecting model tampering and unauthorized changes.
- Cryptographic methods for model protection.
Module 4: Implementing Zero Trust Architecture for AI
- Core principles of Zero Trust Architecture (ZTA).
- Adapting ZTA concepts for AI systems and data.
- Identity and access management for AI components.
- Network segmentation and micro-segmentation for AI workloads.
- Continuous monitoring and validation in AI environments.
- Data security within a Zero Trust AI framework.
Module 5: AI Threat Modeling and Vulnerability Analysis
- Common AI attack vectors and surfaces.
- Threat modeling methodologies (STRIDE, etc.) applied to AI.
- Identifying vulnerabilities in AI algorithms and data.
- Assessing risks associated with AI failures.
- Adversarial machine learning techniques overview.
- Developing mitigation strategies for AI threats.
Module 6: CAIMMC Levels and Assessment
- Detailed breakdown of CAIMMC Maturity Levels 1-5.
- Mapping controls to NIST/DoD AI RMF requirements.
- Assessment methodologies for determining maturity levels.
- Evidence gathering and documentation for assessments.
- Planning and conducting a CAIMMC assessment.
- Reporting findings and developing remediation plans.
Exam Domains:
- AI Security Governance and Risk Frameworks
- Secure AI Development Lifecycle Practices
- AI Model Trustworthiness and Assurance
- Resilient AI Infrastructure Security
- AI Threat Landscape and Response Strategies
- Maturity Level Assessment and Reporting
Course Delivery: The course is delivered through a combination of lectures and interactive discussions, facilitated by experts in AI cybersecurity. Participants will have access to online resources, including readings and case studies.
Assessment and Certification: Participants will be assessed through quizzes and assignments. Upon successful completion of the course requirements and exam, participants will receive an AI Cybersecurity Maturity Model Certification (CAIMMC).
Question Types:
- Multiple Choice Questions (MCQs)
- True/False Statements
- Scenario-based Questions
- Fill in the Blank Questions
- Matching Questions (Matching concepts or terms with definitions)
- Short Answer Questions
Passing Criteria: To pass the AI Cybersecurity Maturity Model Certification (CAIMMC) exam, candidates must achieve a score of 70% or higher.
Master AI cybersecurity maturity assessment. Enroll in the CAIMMC program today and become certified to secure AI systems using leading frameworks.