Certified MBSE / SysML for AI Security (CSAIS)
1. Training Introduction
The Certified MBSE / SysML for AI Security (CSAIS) program is designed to equip professionals with advanced knowledge in Model-Based Systems Engineering (MBSE) and SysML modeling for AI-enabled security systems. Participants will learn to model, analyze, and secure complex AI-driven systems across critical domains including cybersecurity, infrastructure, defense, and autonomous systems.
This program integrates theoretical knowledge, hands-on modeling exercises, and AI-driven security simulations to prepare participants for securing AI systems using MBSE and SysML principles.
2. Training Objective
The program aims to enable participants to:
- Apply MBSE and SysML principles to model AI systems.
- Identify and mitigate security vulnerabilities in AI-enabled systems.
- Develop, simulate, and validate secure system architectures.
- Integrate AI-based security measures into system design and operations.
- Support decision-making in cybersecurity, defense, and smart infrastructure projects.
- Achieve professional certification in MBSE / SysML for AI security.
3. Targeted Group
- Systems engineers and MBSE practitioners
- AI and cybersecurity professionals
- Project and program managers for AI systems
- Technical leads in smart infrastructure, defense, and autonomous systems
- Professionals seeking certification in MBSE, SysML, and AI security integration
4. Course Duration
12–16 Days
- Standard comprehensive programme: 16 days
- Accelerated programme for experienced professionals: 12 days
5. Training Methodology
- Instructor-led lectures with interactive discussions
- Case studies in AI security and critical system modeling
- Hands-on exercises using MBSE/SysML modeling tools (e.g., Cameo Systems Modeler, MagicDraw)
- AI-enabled security simulation exercises
- Group workshops for threat modeling, risk assessment, and mitigation planning
- Capstone project integrating MBSE, SysML, and AI security principles
- Assessment through exercises, model deliverables, and final project presentation
6. Course Content
Module 1: Introduction to MBSE, SysML, and AI Security
- Fundamentals of MBSE and SysML
- AI security challenges and opportunities
- Standards and frameworks for secure system design
Module 2: Systems Thinking for AI Security
- Systems thinking concepts
- Identifying interdependencies and security risks
- Complexity in AI-enabled systems
Module 3: MBSE and SysML Modeling Frameworks
- SysML diagrams: requirement, block, activity, sequence, and state
- Model creation, documentation, and traceability
- MBSE standards (OMG, INCOSE)
Module 4: AI System Architecture
- Functional, logical, and physical system architectures
- Interface definitions and integration of AI components
- Security considerations in architecture design
Module 5: Requirements Engineering for AI Security
- Capturing system and security requirements
- Threat modeling and vulnerability identification
- Traceability and compliance verification
Module 6: Secure System Design and Modeling
- Modeling secure AI-enabled systems
- Integration of authentication, encryption, and monitoring mechanisms
- Simulation of security controls
Module 7: Verification and Validation in AI Systems
- Model verification and validation techniques
- Testing AI models for robustness and security
- Simulation-based validation using MBSE/SysML
Module 8: AI-Driven Threat Detection
- Machine learning for anomaly detection
- Predictive security analytics
- Cyber threat modeling and mitigation
Module 9: Risk Assessment and Cybersecurity Modeling
- Risk identification, assessment, and prioritization
- AI-based risk prediction
- Modeling mitigation strategies using SysML
Module 10: Systems Integration and Secure Deployment
- Planning secure integration of subsystems
- Verification and validation of integrated AI systems
- AI-based monitoring for operational security
Module 11: Security Optimization and Resilience
- Optimization of security measures
- Multi-objective security performance assessment
- Resilient system design using MBSE + AI
Module 12: Digital Twin for AI Security
- Concept of digital twins for security testing
- Virtual modeling of AI-enabled systems
- Real-time simulation and anomaly detection
Module 13: AI Security in Cyber-Physical Systems
- Smart infrastructure, autonomous vehicles, and IoT
- Threat modeling in cyber-physical systems
- Integration of AI security controls
Module 14: Project Management for Secure AI Systems
- MBSE + AI security project planning
- Risk, schedule, and resource management
- AI-enabled project monitoring and reporting
Module 15: Capstone Project – MBSE + AI Security Implementation
- Model a selected AI system with integrated security
- Simulate threats, vulnerabilities, and mitigation strategies
- Present system design, security analysis, and recommendations
Module 16: Emerging Trends and Future Directions
- Advanced AI security techniques
- Industry applications: defense, energy, smart cities
- Preparing for next-generation AI-enabled secure systems
7. Expected Learning Outcomes
Participants will be able to:
- Model AI-enabled systems using MBSE and SysML principles.
- Identify, assess, and mitigate security risks in AI systems.
- Develop secure architectures and validate system designs.
- Apply AI-driven analytics for threat detection and system optimization.
- Contribute effectively to projects requiring MBSE + AI security expertise.
- Achieve professional recognition as Certified MBSE / SysML for AI Security (CSAIS).
8. Certificate of Completion
Upon successful completion of all modules, practical exercises, and the capstone project, participants will receive:
Certificate of Completion
Certified MBSE / SysML for AI Security (CSAIS)
Issued by FOTADE Training, Research and Resource Development Centre
This certificate validates the participant’s expertise in MBSE, SysML modeling, AI security integration, and professional competency in securing complex AI systems.
