Scan codebases for AI SDK usage. Map compliance risks to NIST AI RMF, ISO 42001, and EU AI Act.
pip install aibom-scanner
aibom-scanner scan --path /path/to/your/repo
aibom-scanner detects AI SDKs in your codebase and generates an AI Bill of Materials (AIBOM) with compliance risk findings mapped to three frameworks.
| 70 detection patterns | OpenAI, Anthropic, Google AI, AWS Bedrock, Cohere, Mistral, Groq, HuggingFace, and 21 more |
| Languages | Python, JS/TS, Go, Java, Rust, Ruby, Swift, C#/.NET — imports, API calls, and dependency manifests |
| 10 Chinese AI providers | 3 BIS Entity-Listed (Zhipu, iFlytek, SenseTime = CRITICAL), 7 data sovereignty flagged |
| Agentic AI detection | CrewAI, AutoGen, LangGraph, Semantic Kernel, MCP |
| 34 risk rules | 8 categories with evidence-qualified severity adjustment |
| 48 compliance controls | NIST AI RMF (23), ISO 42001 (15), EU AI Act (10) |
| Secrets detection | Hardcoded API keys, Vault, AWS Secrets Manager, dotenv |
| Dev tool detection | Cursor, GitHub Copilot, Claude Code, Aider, TabNine |
| Coverage accounting | Every scan reports what it read — and what it could not |
| Zero dependencies | Pure Python stdlib. Nothing to install but Python. |
An AI Bill of Materials that silently omits what it could not parse is not incomplete, it is wrong. Every scan reports its own denominator:
Scanned 86 of 173 source files (49.7%) — 86 test/fixture files excluded, 98.9% of source attempted
Two ratios, because either one alone misleads:
| Field | Meaning |
|---|---|
coverage_pct |
files_scanned / source_files_seen — the honest headline. Every source file in the tree is in the denominator. |
readable_coverage_pct |
files_scanned / (source_files_seen - skipped_by_path) — isolates scanner capability from the tests and fixtures deliberately excluded. |
Quoting only the second would report ~99% for a scan that read half a repo.
Every unread source file is attributed to exactly one reason:
skipped_by_path— intentional exclusion (tests, fixtures, examples, vendor).unscanned_by_extension— this scanner version cannot parse the language.
Languages the scanner cannot read are reported, never treated as clean. When any
turn up, a warning is printed to stderr, the table output shows an INCOMPLETE COVERAGE block, and SARIF carries a toolExecutionNotification so the run reads as
partial rather than silently green. Currently unreadable: .php, .scala, .clj,
.ex, .exs, .dart, .cpp, .c, .h, .hpp, .m, .mm, .pl, .lua, .r,
.jl, .ps1, .kts.
Every finding carries an evidence_basis:
observed— backed by something in the scanned source (a detection, a provider).inferred— a governance checklist item. Nothing in the source backs it.
Inferred findings reflect absence of evidence, not evidence of absence. Table output
separates them into their own section, --severity-threshold ignores them, and SARIF
does not emit them as code-scanning alerts — a governance checklist item is not a
defect at a code location.
# Table output (default in terminal)
aibom-scanner scan --path . --format table
# JSON (default when piped) — includes coverage and evidence_basis
aibom-scanner scan --path . --format json > aibom.json
# SARIF for GitHub Code Scanning — observed findings only, coverage in run properties
aibom-scanner scan --path . --format sarif > results.sarif
# Fail CI on high/critical OBSERVED findings (exit 1)
aibom-scanner scan --path . --severity-threshold high
# Fail CI when the scanner could not read some languages (exit 3)
aibom-scanner scan --path . --fail-on-incomplete-coverage| Exit code | Meaning |
|---|---|
0 |
Scan completed, no gate tripped |
1 |
Observed findings at or above --severity-threshold |
2 |
Bad arguments, unreadable path, or interrupted |
3 |
--fail-on-incomplete-coverage set and languages were unreadable |
If both gates trip, 1 wins — an observed finding is more actionable than a coverage gap.
The coverage warning always goes to stderr, so piping --format json or
--format sarif to a file yields clean, valid output.
Add AI compliance scanning to every PR:
# .github/workflows/aibom-scan.yml
name: AIBOM Scan
on: [push, pull_request]
jobs:
scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: saasvista/aibom-scanner@v1
with:
severity-threshold: high| Category | Providers |
|---|---|
| Major | OpenAI, Anthropic, Google AI, AWS Bedrock, Azure OpenAI, Cohere, Mistral, Groq |
| Open Source | HuggingFace, Together AI, Fireworks, Replicate |
| Chinese (BIS Entity List) | Zhipu AI, iFlytek, SenseTime |
| Chinese (Data Sovereignty) | DeepSeek, Alibaba Qwen, Baidu ERNIE, Moonshot, MiniMax, Baichuan, Yi |
| Agentic | CrewAI, AutoGen, LangGraph, Semantic Kernel |
| Protocol | MCP (Model Context Protocol) |
| Orchestration | LangChain, LlamaIndex |
| Category | Rules | Examples |
|---|---|---|
| Data Privacy | 4 | Missing DPA, data classification, prompt retention |
| Model Governance | 8 | No inventory, no versioning, supply chain risk |
| Security | 4 | Hardcoded keys, no input/output validation |
| Transparency | 3 | No AI disclosure, no decision logging |
| Accountability | 3 | No risk owner, no incident response plan |
| Bias & Fairness | 2 | No bias testing, no fairness evaluation |
| Export Compliance | 4 | BIS Entity List, data sovereignty, EU AI Act |
| Agentic AI | 5 | No HITL, no access controls, no observability |
| Framework | Controls | Coverage |
|---|---|---|
| NIST AI RMF | 23 | GOVERN, MAP, MEASURE, MANAGE functions |
| ISO 42001 | 15 | AI management system requirements |
| EU AI Act | 10 | Articles 5-52, high-risk classification |
Your Codebase
│
▼
┌─────────────┐ ┌──────────────┐ ┌─────────────┐ ┌────────────────┐
│ File Walker │────▶│ AI SDK │────▶│ Risk Engine │────▶│ Control Mapper │
│ git ls-files │ │ Detector │ │ 34 rules │ │ 48 controls │
│ os.walk │ │ 70 patterns │ │ 8 categories│ │ 3 frameworks │
└─────────────┘ └──────────────┘ └─────────────┘ └────────────────┘
│ │ │
Detections + Risk findings Gap analysis
model names + with severity NIST / ISO /
dependencies qualification EU AI Act
│
▼
Table / JSON / SARIF
We scanned 5 popular open-source AI repos (470K combined GitHub stars):
- 389 AI SDK detections
- 116 compliance findings
- 0 governance controls fully mapped
One enterprise security company had a BIS Entity-Listed Chinese AI provider inherited silently through an acquisition.
EU AI Act enforcement starts August 2026. If you don't know what AI SDKs are in your codebase, you can't govern them.
Contributions welcome. See CONTRIBUTING.md for guidelines.
- Add detection patterns — new AI providers, SDKs, or frameworks
- Improve risk rules — better severity calibration, new categories
- New output formats — CycloneDX, SPDX, HTML reports
- Language support — Go, Rust, Java detection improvements
Apache-2.0. See LICENSE.
Built by SaaSVista — AI Risk & Compliance Copilot