Neural Supply Chain Risk & AI Dependency Mapping (NSCR-AI) Certification Program by Tonex
This course focuses on securing the AI lifecycle by enhancing supply chain visibility and understanding model and data dependencies. Like an SBOM for AI, it equips professionals to identify third-party model risks, trace dataset provenance, and detect embedded threats. Participants learn to evaluate AI assets across vendors, uncover model reuse, and adopt Zero Trust principles in shared AI environments. The program helps organizations reduce risk, increase transparency, and stay compliant in complex AI systems. NSCR-AI prepares you to safeguard AI-driven operations with clarity and confidence.
Audience:
- AI and ML engineers
- Cybersecurity professionals
- Risk management specialists
- Compliance and audit professionals
- Data scientists and AI architects
- Government and defense technologists
Learning Objectives:
- Understand AI software bill of materials (AIBOM) and its importance
- Analyze third-party AI model dependencies and risks
- Trace dataset sourcing, lineage, and evaluate risk exposure
- Detect embedded threats and model leakage
- Implement Zero Trust for multi-tenant AI systems
- Improve AI system transparency and accountability
Program Modules:
Module 1: Foundations of Neural Supply Chain Risk
- AI supply chain vs. traditional software supply chain
- Overview of NSCR-AI framework
- Mapping dependencies in AI development
- Common AI lifecycle vulnerabilities
- Regulatory considerations for AI transparency
- Role of AIBOM in modern AI systems
Module 2: AI Software Bill of Materials (AIBOM)
- Structure and components of AIBOM
- Creating and maintaining an AIBOM
- Vendor disclosure and verification practices
- AIBOM compliance and audit use
- Integration of AIBOM into DevOps pipelines
- Case studies on AIBOM failures and lessons
Module 3: Model and Dataset Dependency Analysis
- Identifying third-party model components
- Dependency graphing for neural networks
- Dataset origin tracing and lineage tools
- Risks of using pretrained and shared models
- Detecting proprietary content leakage
- Dataset risk scoring techniques
Module 4: Embedded Threats and Model Reuse
- Techniques for detecting embedded threats
- Examples of model reuse and risk impacts
- AI poisoning and adversarial manipulation
- Watermarking and fingerprinting models
- IP issues with reused or stolen models
- Managing model lifecycle securely
Module 5: Zero Trust for AI Environments
- Principles of Zero Trust in AI systems
- Multi-tenant AI architecture risks
- Isolating tenants in LLM deployments
- API access control for model sharing
- Authentication and encryption strategies
- Monitoring and logging AI activity
Module 6: Operationalizing NSCR-AI Framework
- Integrating NSCR-AI into workflows
- Aligning with organizational risk policies
- Automating risk detection pipelines
- Real-time alerting for supply chain issues
- Building AI trust scorecards
- Reporting and executive dashboards
Exam Domains:
- AI Risk Governance and Policy Integration
- Model and Data Provenance Assessment
- AI Threat Intelligence and Detection
- AI Ecosystem Security Architecture
- Supply Chain Risk Communication
- Compliance, Auditing, and Legal Implications
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of Neural Supply Chain Risk & AI Dependency Mapping (NSCR-AI). Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Neural Supply Chain Risk & AI Dependency Mapping (NSCR-AI).
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 Neural Supply Chain Risk & AI Dependency Mapping (NSCR-AI) Certification Training exam, candidates must achieve a score of 70% or higher.
Secure your AI systems from the inside out. Enroll in Tonex’s NSCR-AI Certification Program and lead with confidence in AI supply chain integrity.