AI-Powered CMMC Audit Automation Course

Module 1 – AI Foundations for CMMC Automation

• Evolution of AI technology: machine learning, deep neural networks, and NLP in compliance
• AI architectures for security: CNNs, RNNs, and transformers in CMMC automation
• Foundation models vs. large language models for compliance documentation
• Mapping AI technologies to automated evidence collection and control verification

Practical Focus

• Automating CMMC Level 1 and Level 2 control verification with AI
• Identifying high-value automation opportunities across the 110 CMMC controls
• Continuous AI-powered compliance monitoring and status tracking

Lab Exercise

• Investigating Discriminative and Generative AI
• Hands-on exploration of how different AI approaches automate CMMC compliance tasks.

Module 2 – AI Risk Management in CMMC Environments

• Security, privacy, and compliance risks of AI implementations in defense contracting
• Ethical AI deployment aligned with DoD responsible AI principles
• Risks introduced by generative AI and mitigation strategies
• Defending against AI-enhanced social engineering and deepfake attacks

CMMC-Specific Focus

• Balancing AI risk mitigation with compliance objectives
• Human autonomy vs. automated system autonomy in compliance monitoring
• Data governance for AI systems handling CUI and FCI

Lab Exercises

• Protecting Sensitive Data with DLP – Implementing DLP in AI-powered compliance systems
• Conducting an AI Risk Assessment – Evaluating AI risks in defense contractor environments

Module 3 – AI-Enhanced Security Operations for CMMC

• SecOps automation using AI-based monitoring and response
• Managing hybrid IT and cloud AI environments in defense contracting
• AI security tooling: Google AI SecOps, Microsoft Cybersecurity Copilot, SIEM/SOAR integration
• AIOps, AI networking, and telemetry-driven day-2 operations

Hands-On Tools

• Google AI SecOps Unified Data Model for evidence collection
• Microsoft Cybersecurity Copilot for investigations and documentation
• AI-driven orchestration for automated incident response and compliance validation

Lab Exercise

• Defending Security with AI
  Implementing AI-powered monitoring and automated response for CMMC control verification.

Module 4 – AI Vulnerabilities and Attack Vectors

• AI algorithms, datasets, and models as attack surfaces
• OWASP Top 10 risks for AI-powered systems
• Prompt engineering threats and injection attack defenses
• AI-enhanced social engineering and deepfake exploitation

Defense Contractor Focus

• Securing AI used in CMMC compliance automation
• Protecting intellectual property from AI-enabled reconnaissance
• AI red teaming to validate secure CMMC implementations

Lab Exercises

• Penetration Testing an AI System – Identifying vulnerabilities in AI compliance tools
• Enhancing Hacking with GenAI – Understanding attacker AI techniques targeting defense contractors

Module 5 – AI Governance and CMMC Framework Integration

• Regulatory landscape: EU AI Act and NIST AI Risk Management Framework
• OWASP governance checklist for secure AI deployment
• Responsible AI and secure AI frameworks for ethical implementation
• Federated learning and zero-trust generative AI architectures

Practical Implementation

• Establishing AI governance policies for defense contractors
• Managing generative AI risk while enabling automation
• Integrating AI with existing CMMC compliance management platforms
• Evaluating vendors and assessing AI security tool risk
• Evolution of AI Technology: Machine Learning, Deep Neural Networks, and NLP applications in compliance
• AI Architectures for Security: CNNs, RNNs, Transformers and their role in CMMC automation
• Foundation Models vs. Large Language Models: Understanding capabilities for compliance documentation
• CMMC-Specific Applications: Mapping AI technologies to automated evidence collection and control verification

Practical Focus:

• Understanding AI capabilities for automating CMMC Level 1 and Level 2 control verification
• Identifying which of the 110 CMMC controls benefit most from AI automation
• AI-powered continuous monitoring for compliance status tracking

Lab Exercise

• Investigating Discriminative and Generative AI - Hands-on exploration of how different AI approaches can automate CMMC compliance tasks