| id | SKL-model-MODELBOMSECURITY |
| name | Model Bom Security |
| description | AI Supply Chain Security extends beyond IoT device security (Skill 76-80) and LLM Security (Skill 123) to secure the entire software and AI model supply chain, from training data to deployed models. |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Model Bom Security
Skill Profile
(Select at least one profile to enable specific modules)
Overview
AI Supply Chain Security extends beyond IoT device security (Skill 76-80) and LLM Security (Skill 123) to secure the entire software and AI model supply chain, from training data to deployed models.
Why This Matters
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
- Inputs:
- <e.g., env vars, request payload, file paths, schema>
- Entry Conditions:
- <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
- Outputs:
- <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
- Artifacts Required (Deliverables):
- <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
- Acceptance Evidence:
- <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
- Success Criteria:
- <e.g., p95 < 300ms, coverage ≥ 80%>
Skill Composition
- Depends on: security
- Compatible with: None
- Conflicts with: None
- : authn, authz