AI-BOM Discovery
Inventory every AI asset in your environment — models, ML dependencies, notebooks, prompts, vector stores, agents, MCP servers, and inference endpoints — into one tenant-scoped AI bill of materials.
AI-BOM Discovery
You can't govern AI assets you don't know you have. AI-BOM discovery builds a continuously-updated inventory of every AI asset across your code, cloud, and connected SaaS — the bill of materials that AI-SPM's posture scoring, model scanning, and governance all build on.
What it inventories
Discovery runs across three surfaces and catalogues each asset in your inventory:
| Surface | What is discovered | Asset types |
|---|---|---|
| Code | Imports of AI SDKs (openai, anthropic, langchain, transformers, llama_index); model weight files (.pt, .safetensors, .gguf); prompt templates; notebooks; MCP server configuration | Models, services, prompts, notebooks, MCP servers |
| Cloud | Managed inference endpoints — SageMaker, Bedrock, Vertex AI — discovered through your cloud connectors | Endpoints, models |
| SaaS | Connected but unsanctioned AI tools surfaced through shadow-AI detection | Services |
Vector databases (Pinecone, Weaviate, pgvector) are inventoried as vector stores, and agent definitions as agents. Discovery brings AI signals that were previously scattered across separate checks into one place rather than duplicating them.
The AI-BOM
Every discovered asset is catalogued with its type, name, source, format, and metadata, and the assets for a product are assembled into an AI-BOM: a tenant/org-scoped snapshot of your AI footprint at a point in time. The AI-BOM is the foundation the rest of AI-SPM builds on: model scanning attaches findings to the model assets it inventories, and the posture score reads the inventory for its AI-visibility factor (you can't have good posture over assets you can't see).
Findings it produces
Discovery itself is an inventory pass, so its primary output is the AI-BOM rather than findings. The assets it surfaces feed the engines that do emit findings:
- Model assets are handed to AI-SPM model scanning, which produces AI-security findings (malware and hidden code in model files) in the unified findings model.
- Shadow-AI services surface unsanctioned tools for governance review.
- Missing-visibility gaps lower your AI-visibility posture score.
How to enable
AI-BOM discovery is part of the AI-SPM engine and is admin-toggleable via the ff.aispm feature flag, enforced server-side. Once enabled, discovery runs across connected repositories, cloud accounts, and SaaS integrations through the standard scan flow, and the AI-BOM is queryable in the app and through the API.
Related
- AI-SPM — model-artifact malware scanning over the models this inventory finds.
- AI Gateway — runtime guardrails for the LLM apps and endpoints in the AI-BOM.
- AI-BOM & Model Security — model provenance, signing, and lineage governance.
- Asset Discovery — discovery across the rest of your estate.
- Unified Findings & Feature Flags — where AI findings land and how the engine is toggled.
AI Security Posture Management (AI-SPM)
Detect malware and hidden code in AI model files — across Pickle, PyTorch, safetensors, GGUF, and ONNX formats — before untrusted weights ever load into your environment.
AI Gateway (LLM Firewall)
A runtime guardrail layer for LLM apps and agents — prompt-injection, jailbreak, and PII/secret-egress checks in monitor or enforce mode, with fail-open safety so the protected app never breaks.