AuditFlow-AI

AuditFlow-AI is an experimental platform to automate compliance evidence collection, detect configuration/security drift, and generate audit-ready documentation from your DevOps footprint. Built initially for a hackathon, it’s evolving toward a community-driven, open-source framework.

Mission: Make audits effortless • Catch drift automatically • Keep teams audit-ready, always.

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Table of Contents

• Vision
• Key Capabilities
• Architecture
• Tech Stack
• Quickstart (Local)
• Configuration
• Roadmap
• Contributing
• Security & Responsible Use
• License
• Acknowledgments

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Vision

Audits today are manual and repetitive. AuditFlow-AI reimagines compliance as code + automation:

• Automated evidence collection from source control, CI/CD, and cloud accounts.
• Continuous drift detection for infra, policies, and access controls.
• Audit-ready packs that compile artifacts into clean, human-readable reports.
• Developer-first: works with Git-centric flows and modern cloud tooling.

Long term, AuditFlow-AI aims to be a modular open-source framework you can extend for SOC 2, ISO 27001, HIPAA, and more.

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Key Capabilities

• Evidence Graph (prototype): Ingests GitLab metadata, CI runs, IaC state, and cloud configs into a queryable model.
• Drift Rules: Declarative checks that alert on deviations impacting compliance.
• Report Generator: Turns raw logs/configs into structured findings and narratives.
• CLI + Web UI (planned): Dev-first commands plus a lightweight dashboard.

Status: Early prototype; APIs and schemas are subject to change.

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Architecture

monorepo/ ├─ api/ # FastAPI services: ingestion, rules, report generation ├─ web/ # Next.js dashboard (status, evidence browser, drift alerts) ├─ cli/ # Developer CLI to run scans and export reports ├─ workers/ # Background jobs (scheduled drift scans) ├─ infra/ # IaC for GCP (Cloud Run, Cloud SQL, VPC, Secrets) └─ docs/ # Specs, design notes, contribution guides (soon)

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Data Flow (high level)

1. Connect sources (GitLab, Cloud, CI).
2. Ingest & normalize to an Evidence Graph (Postgres + vector index).
3. Run rules to detect drift and map controls → evidence.
4. Generate reports (PDF/HTML/JSON) and export to auditors.

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Tech Stack

• Backend: FastAPI (Python)
• Frontend: Next.js
• DB/Storage: Postgres + Pinecone (vector search)
• Infra: Google Cloud (Cloud Run, Cloud SQL, Secret Manager)
• Pipelines: GitLab CI/CD
• AI layer: LLM-powered summarization & document generation (provider-agnostic)

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Quickstart (Local)

Requires: Python 3.11+, Node 18+, Docker, Make

# 1) Clone
git clone https://github.com/<you>/AuditFlow-AI.git
cd AuditFlow-AI

# 2) API (FastAPI)
cd api
cp .env.example .env
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload

# 3) Web (Next.js)
cd ../web
cp .env.local.example .env.local
npm install
npm run dev
Docker (optional)
bash
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# From repo root
docker compose up --build
Configuration
Minimum env (API)
Create api/.env:

ini
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DATABASE_URL=postgresql database or any
PINECONE_API_KEY=your_key
PINECONE_INDEX=auditflow-evidence
# GitLab/GitHub, Cloud creds, etc.
GITLAB_TOKEN=glpat_xxx
GCP_PROJECT_ID=your-project
Minimum env (Web)
Create web/.env.local:

ini
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NEXT_PUBLIC_API_URL=http://localhost:8000
Secrets: Use GCP Secret Manager (prod) and .env files only for local dev. Never commit secrets.

Roadmap
 Evidence Connectors: GitHub, AWS, GCP, Azure

 Standards Packs: SOC 2, ISO 27001, HIPAA mappings

 Rules DSL: Author and test drift rules as code

 Report Templates: PDF/HTML with control coverage

 RBAC & Audit Log: Workspace permissions, immutable evidence trail

 Public Release: Docs, examples, demo dataset

 Open Source: Convert to Apache-2.0, publish contribution guide

Contributing
We’re preparing public contribution guidelines. In the meantime:

Open issues with clear repro or proposal.

For features, include: problem → approach → schema impact → testing plan.

Keep changes modular and well-documented.

A CONTRIBUTING.md and CODE_OF_CONDUCT.md will be added before the first tagged OSS release.

Security & Responsible Use
Handle credentials via Secret Manager/env only.

Principle of least privilege for cloud connectors.

Redact/Hash sensitive values in logs and exported reports.

If you discover a vulnerability, please disclose responsibly via a private issue or security email (to be published).

License
Planned for Apache-2.0 on the first public OSS tag. Until then, all rights reserved by the project maintainers.