MBSE for AI/LLM Security Essentials Training by Tonex
Model-Based Systems Engineering (MBSE) provides a structured approach to design, integrate, and secure complex AI and large language model (LLM) systems. This program delivers essential knowledge for engineers, project leaders, and technical managers seeking to apply MBSE in safeguarding AI/LLM solutions. It emphasizes system modeling, lifecycle management, and interoperability for secure architectures.
With rising threats in AI-driven systems, MBSE helps identify vulnerabilities early and ensure resilient deployments. The training also highlights how MBSE supports cybersecurity strategies, enabling stronger protection against adversarial AI risks and ensuring compliance with modern security frameworks in sensitive and high-assurance environments.
Learning Objectives:
- Understand MBSE fundamentals for AI/LLM systems
- Apply modeling to improve AI security design
- Explore system lifecycle alignment with security needs
- Develop secure integration strategies using MBSE practices
- Strengthen decision-making through model-driven analysis
- Enhance resilience against adversarial risks in AI with a focus on cybersecurity
Audience:
- Systems Engineers
- AI and ML Engineers
- Cybersecurity Professionals
- Project Managers
- IT Architects
- Technology Strategists
Course Modules:
Module 1: MBSE Foundations
- Principles of Model-Based Systems Engineering
- Core MBSE methodologies and standards
- Systems modeling tools overview
- Role of MBSE in AI/LLM security
- Key benefits of structured modeling
- Security considerations in foundational MBSE
Module 2: AI/LLM Security Landscape
- Overview of AI/LLM attack vectors
- Threat modeling in AI systems
- Data poisoning and adversarial input risks
- Model extraction and inversion threats
- Impact of LLM misuse scenarios
- Role of MBSE in mitigating AI threats
Module 3: System Lifecycle Alignment
- MBSE-driven requirements analysis
- Secure architecture planning for AI/LLMs
- Verification and validation strategies
- Integration of security across lifecycle phases
- Risk assessment and prioritization methods
- Maintaining compliance in evolving AI systems
Module 4: Secure Integration Strategies
- Secure design of AI pipelines
- Interface and dependency modeling
- Trusted data sourcing and validation
- Ensuring robust model deployment
- Aligning integration with security standards
- Leveraging MBSE for continuous monitoring
Module 5: Model-Driven Risk Management
- Identifying vulnerabilities in AI/LLM workflows
- Risk visualization using MBSE diagrams
- Quantitative and qualitative risk scoring
- Building resilient architectures through modeling
- Scenario-based analysis of attack surfaces
- Using MBSE to guide security investments
Module 6: Future-Proofing with MBSE
- Trends in AI/LLM evolution
- Adaptive MBSE frameworks for emerging threats
- Cybersecurity alignment with AI regulations
- Incorporating Zero Trust principles in MBSE
- Sustainable governance for AI-driven ecosystems
- Preparing for quantum and next-gen AI security challenges
Elevate your expertise by mastering MBSE for AI/LLM security. Enroll today with Tonex to strengthen your role in building resilient, secure, and future-ready AI systems.