README.md

DocSentinel
AI-powered SSDLC platform — Secure your software from requirements to operations

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What is DocSentinel?

DocSentinel is an AI-powered SSDLC (Secure Software Development Lifecycle) platform for security teams. It automates security activities across all six phases of the software development lifecycle using intelligent AI agents orchestrated by LangGraph and powered by LangChain. It automates the review of security-related documents, forms, and reports — from requirements and design through development, testing, deployment, and operations — comparing inputs against your policy and knowledge base to produce structured assessment reports with risks, compliance gaps, and remediation suggestions.

Instead of only reviewing documents at the pre-release stage, DocSentinel embeds security from day one:

SSDLC Phase	What DocSentinel Does
Requirements	Extract security requirements, identify compliance obligations (GDPR, PCI DSS, SOC2)
Design	Automated threat modeling (STRIDE/DREAD), security architecture review, SDR reports
Development	Secure coding assessment, SAST findings triage, coding guidance
Testing	SAST/DAST report analysis, penetration test review, vulnerability prioritization
Deployment	Configuration security review, hardening assessment, release sign-off
Operations	Vulnerability monitoring, incident response assistance, log audit

Built as a React console + headless API + MCP service, DocSentinel integrates into local security review workflows, CI/CD pipelines, AI agents (Claude Desktop, Cursor, OpenClaw), and existing security operations.

• LangGraph orchestration: Stateful, graph-based agent workflows with conditional branching per SSDLC stage.
• Multi-format input: PDF, Word, Excel, PPT, text — parsed into a unified format for the LLM.
• Knowledge base (RAG): Upload policy and compliance documents; the agent uses them as reference when assessing.
• Multiple LLMs: Use OpenAI, Claude, Qwen, or Ollama (local) via a single interface.
• Structured output: JSON/Markdown reports with risk items, compliance gaps, and actionable remediations.

Ideal for enterprises that need to scale security assessments across many projects and SSDLC stages without proportionally scaling headcount.

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Why DocSentinel?

Pain Point	DocSentinel Solution
Fragmented SSDLC coverage Most tools only address testing/deployment.	Full lifecycle agents cover all 6 SSDLC phases with dedicated AI personas.
Fragmented criteria Policies, standards, and precedents are scattered.	Single knowledge base ensures consistent findings and traceability.
No automated threat modeling Threat models are created ad-hoc.	Design Agent generates STRIDE/DREAD threat models from architecture docs.
Heavy questionnaire workflow Endless review cycles.	Automated first-pass and gap analysis reduces manual back-and-forth rounds.
SAST/DAST report overload Too many findings, too little context.	Testing Agent triages, prioritizes, and maps findings to threat models.
Pre-release review pressure Everything lands on security at the end.	Shift-left approach catches issues early in requirements and design. Structured reports help reviewers focus on decision-making.
Scale vs. consistency Manual reviews vary by reviewer.	LangGraph workflows and unified pipeline ensure consistent, auditable assessment across projects.
SSDLC coverage gaps Security involvement is uneven across lifecycle stages; early stages get less scrutiny.	Stage-aware assessment covers all 6 SSDLC stages with dedicated skills and checklists.

See the full problem statement and SSDLC phase details in SPEC.md.

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Architecture

DocSentinel is built on a React Console plus FastAPI/MCP access layer, with LangGraph for stateful agent orchestration and LangChain for unified LLM access. Six phase-specific agents are coordinated by a graph-based state machine with cross-phase context sharing. The orchestrator coordinates parsing, SSDLC stage routing, the knowledge base (RAG), skills, and the LLM. You can use cloud or local LLMs and optional integrations (e.g. AAD, ServiceNow) as your environment requires.

flowchart TB
    subgraph User["User / Security Staff"]
    end
    subgraph Access["Access Layer"]
        Console["React Console<br/>(Vite + Tailwind)"]
        API["REST API<br/>(FastAPI)"]
        MCP["MCP Server<br/>(stdio)"]
    end
    subgraph Orchestration["SSDLC Orchestration (LangGraph)"]
        Router["Phase Router"]
        A1["Requirements Agent"]
        A2["Design Agent"]
        A3["Development Agent"]
        A4["Testing Agent"]
        A5["Deployment Agent"]
        A6["Operations Agent"]
    end
    subgraph Core["Core Services"]
        KB["Knowledge Base (RAG)"]
        Parser["Parser"]
        Skill["Skills"]
        Mem["Memory"]
    end
    subgraph LLM["LLM Layer (LangChain)"]
        Abst["LLM Abstraction"]
    end
    subgraph Backends["LLM Backends"]
        Cloud["OpenAI / Claude / Qwen"]
        Local["Ollama / vLLM"]
    end

    User --> Console
    User --> API
    Console --> API
    User --> MCP
    API --> Router
    MCP --> Router
    Router --> A1 & A2 & A3 & A4 & A5 & A6
    A1 & A2 & A3 & A4 & A5 & A6 --> KB & Parser & Skill
    A1 & A2 & A3 & A4 & A5 & A6 --> Abst
    Abst --> Cloud & Local

Data flow (simplified):

1. User selects SSDLC phase(s) and uploads documents (or optionally lets the SSDLC Router auto-detect the stage).
2. Parser converts files (PDF, Word, Excel, PPT, SAST/DAST reports, etc.) to text/Markdown.
3. LangGraph Router dispatches to the appropriate Phase Agent(s), loading stage-specific skill + checklist.
4. Phase Agent queries KB (phase-specific collections) and applies Skills; Policy+Evidence run in parallel, then Drafter+Reviewer.
5. LLM (via LangChain) produces structured findings with cross-phase traceability.
6. Returns assessment report (risks, threats, gaps, remediations, confidence, SSDLC stage).

Detailed architecture: ARCHITECTURE.md and docs/01-architecture-and-tech-stack.md.

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Core Capabilities

SSDLC Full Lifecycle Coverage

Six dedicated AI agents, each with phase-specific skills, prompts, and knowledge base collections. Run individual phases or a full end-to-end SSDLC assessment:

• Requirements: Security requirements, compliance mapping, initial risk analysis.
• Design: Architecture review, STRIDE/DREAD threat modeling, SDR.
• Development: Secure coding standards, code review findings.
• Testing: SAST/DAST report triage, pen-test evaluation.
• Deployment: Release readiness, config security, hardening.
• Operations: Incident response, vulnerability monitoring, log audit.

Automated Security Assessment

Submit security questionnaires, design documents, or audit reports. DocSentinel analyzes them using configured LLMs and identifies:

• Security Risks: Classified by severity (Critical, High, Medium, Low).
• Compliance Gaps: Missing controls against frameworks like ISO 27001, PCI DSS.
• Remediation Steps: Actionable advice to fix identified issues.

Intelligent Agent Orchestration (LangGraph)

• Stateful workflows: LangGraph state machine maintains context across phases
• Cross-phase traceability: Threats from Design link to test cases in Testing and monitoring rules in Operations
• Conditional routing: Agents activate based on project risk level, compliance requirements, or user selection
• Human-in-the-loop: Interrupt points for human review at phase boundaries
• Checkpointing: Long-running assessments persist state and resume

RAG-Powered Knowledge Base

Upload your organization's security policies, standards, and past audits. Phase-specific collections ensure each agent retrieves the most relevant context:

• Requirements: compliance frameworks, security policies
• Design: threat catalogs, security patterns
• Development: secure coding standards (OWASP)
• Testing: vulnerability databases, remediation guides
• Deployment: CIS benchmarks, hardening guides
• Operations: CVE databases, incident playbooks

LangGraph Agent Orchestration

Powered by LangChain + LangGraph — stateful, graph-based agent workflows with conditional routing per SSDLC stage. Parallel execution of Policy and Evidence agents, followed by Drafter and Reviewer agents.

API-First & MCP Ready

Designed as a headless service. Integrate into CI/CD pipelines via REST API, or use as a super-tool within AI agents (Claude Desktop, Cursor, OpenClaw) via MCP.

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Agent Integration (MCP)

Connect DocSentinel to Claude Desktop, Cursor, or OpenClaw to use it as a powerful SSDLC security skill.

What can it do?

Once connected, you can ask your AI agent:

"Analyze the attached requirements.pdf for missing security requirements using DocSentinel."

"Run a STRIDE threat model on system-design.pdf using the Design Agent."

"Triage these SonarQube SAST findings and prioritize by risk."

Configuration Guide

1. Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "docsentinel": {
      "command": "/path/to/DocSentinel/.venv/bin/python",
      "args": ["/path/to/DocSentinel/app/mcp_server.py"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "CHROMA_PERSIST_DIR": "/absolute/path/to/data/chroma"
      }
    }
  }
}

2. Cursor

1. Go to Settings > Features > MCP.
2. Click + Add New MCP Server.
   • Name: docsentinel
   • Type: stdio
   • Command: /path/to/DocSentinel/.venv/bin/python
   • Args: /path/to/DocSentinel/app/mcp_server.py

See full guide in docs/06-agent-integration.md.

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Quick Start

Option A: One-Click Deployment (Recommended)

git clone https://github.com/arthurpanhku/DocSentinel.git
cd DocSentinel
chmod +x deploy.sh
./deploy.sh

• API Docs: http://localhost:8000/docs

Option B: Manual Setup

Prerequisites: Python 3.10+. Optional: Ollama (ollama pull llama2).

git clone https://github.com/arthurpanhku/DocSentinel.git
cd DocSentinel
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env        # Edit if needed: LLM_PROVIDER=ollama or openai
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

• API docs: http://localhost:8000/docs · Health: http://localhost:8000/health
• React Console: http://localhost:8000/console after building the frontend
• Legacy Review Console (HITL): http://localhost:8000/docs/review-console.html

React Console

DocSentinel includes a React + TypeScript + Vite + Tailwind CSS console for assessments, knowledge base operations, skills, and system status.

npm install --prefix frontend
npm run build --prefix frontend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Open http://localhost:8000/console. For frontend-only development, run:

npm run dev --prefix frontend

The Vite dev server proxies /api, /health, and /config to http://localhost:8000.

The Settings page can update the running server's LLM provider, model, base URL, and API key. API keys are only returned to the UI as masked previews. For persistent startup defaults, set the matching values in .env.

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Example: Submit an SSDLC assessment

# Run a Design phase assessment (threat modeling)
curl -X POST "http://localhost:8000/api/v1/assessments" \
  -F "files=@examples/architecture-doc.pdf" \
  -F "phase=design" \
  -F "scenario_id=threat-modeling"

# Response: { "task_id": "...", "status": "accepted" }
# Get the result
curl "http://localhost:8000/api/v1/assessments/TASK_ID"

Example: Upload to KB and query

# Upload a security policy to the requirements KB collection
curl -X POST "http://localhost:8000/api/v1/kb/documents" \
  -F "file=@examples/sample-policy.txt" \
  -F "collection=kb_requirements"

# Query the KB (RAG)
curl -X POST "http://localhost:8000/api/v1/kb/query" \
  -H "Content-Type: application/json" \
  -d '{"query": "What are the access control requirements?", "top_k": 5}'

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Hosted deployment

A hosted deployment is available on Fronteir AI.

Project Layout

DocSentinel/
├── frontend/             # React + TypeScript + Vite + Tailwind console
├── app/                  # Application code
│   ├── api/              # REST routes: assessments, KB, health, skills
│   ├── agent/            # LangGraph orchestrator, phase agents, skills
│   │   ├── orchestrator.py    # LangGraph state machine & phase routing
│   │   ├── agents/            # Phase-specific agent implementations
│   │   ├── ssdlc/             # SSDLC pipeline: stage router, stage skills, checklists
│   │   ├── skills_registry.py # Built-in skills per SSDLC phase
│   │   └── skills_service.py  # Skill CRUD and management
│   ├── core/             # Config, guardrails, security, DB
│   ├── kb/               # Knowledge Base (Chroma + LightRAG graph RAG)
│   ├── llm/              # LangChain LLM abstraction (OpenAI, Ollama)
│   ├── parser/           # Document parsing (Docling + SAST/DAST + legacy)
│   ├── models/           # Pydantic / SQLModel models
│   ├── main.py           # FastAPI app entry point
│   └── mcp_server.py     # MCP Server for agent integration
├── tests/                # Automated tests (pytest)
├── examples/             # Sample files (questionnaires, policies, reports)
├── docs/                 # Design & Spec documentation
│   ├── 01-architecture-and-tech-stack.md
│   ├── 02-api-specification.yaml
│   ├── 03-assessment-report-and-skill-contract.md
│   ├── 04-integration-guide.md
│   ├── 05-deployment-runbook.md
│   ├── 06-agent-integration.md
│   └── schemas/
├── .github/              # Issue/PR templates, CI (Actions)
├── Dockerfile
├── docker-compose.yml
├── docker-compose.ollama.yml
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── CHANGELOG.md
├── SPEC.md               # PRD with SSDLC phase definitions
├── ARCHITECTURE.md        # System architecture with LangGraph design
├── LICENSE
├── SECURITY.md
├── requirements.txt
├── requirements-dev.txt
└── .env.example

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Configuration

Variable	Description	Default
LLM_PROVIDER	ollama or openai	ollama
OLLAMA_BASE_URL / OLLAMA_MODEL	Local LLM	http://localhost:11434 / llama2
OPENAI_API_KEY / OPENAI_MODEL	OpenAI	--
ANTHROPIC_API_KEY / ANTHROPIC_MODEL	Anthropic Claude	-- / claude-3-5-sonnet-latest
QWEN_API_KEY / QWEN_MODEL	Qwen DashScope OpenAI-compatible API	-- / qwen-plus
DEEPSEEK_API_KEY / DEEPSEEK_MODEL	DeepSeek OpenAI-compatible API	-- / deepseek-chat
COMPAT_API_KEY / COMPAT_BASE_URL / COMPAT_MODEL	Any OpenAI-compatible hosted API	--
LOCAL_API_KEY / LOCAL_BASE_URL / LOCAL_MODEL	Local OpenAI-compatible API	-- / http://localhost:1234/v1 / local-model
CHROMA_PERSIST_DIR	Vector DB path	./data/chroma
PARSER_ENGINE	Parser: auto, docling, or legacy	auto
ENABLE_GRAPH_RAG	Enable LightRAG graph retrieval	true
LANGGRAPH_CHECKPOINT_DIR	LangGraph checkpoint persistence	./data/checkpoints
SSDLC_DEFAULT_PHASES	Default phases for full assessment	requirements,design,development,testing,deployment,operations
SSDLC_DEFAULT_STAGE	Default SSDLC stage if not specified	auto
UPLOAD_MAX_FILE_SIZE_MB / UPLOAD_MAX_FILES	Upload limits	50 / 10

See .env.example and docs/05-deployment-runbook.md for full options.

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Tech Stack

Layer	Technology	Purpose
Agent Orchestration	LangGraph	Stateful graph-based SSDLC workflow engine
LLM Framework	LangChain	Unified LLM abstraction, prompts, tools, RAG
Web/API	FastAPI	Async REST API with auto OpenAPI
Vector Store	ChromaDB + LightRAG	Hybrid vector + graph RAG
Parsing	Docling + legacy fallback	Multi-format document parsing
LLM Providers	OpenAI, Ollama	Cloud and local LLM support
Language	Python 3.10+	Primary development language

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Documentation and PRD

• ARCHITECTURE.md — System architecture: LangGraph design, SSDLC agents, data flow, deployment.
• SPEC.md — Product requirements: SSDLC phases, features, security controls.
• CHANGELOG.md — Version history; Releases.
• Design docs docs/: Architecture, API spec (OpenAPI), contracts, integration guides, deployment runbook.

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Development & Testing

Option A: One-Click Test (Recommended)

chmod +x test_integration.sh
./test_integration.sh

Option B: Manual

pip install -r requirements-dev.txt
pytest
pytest tests/test_skills_api.py   # Run specific test

Contributing

Issues and Pull Requests are welcome. Please read CONTRIBUTING.md for setup, tests, and commit guidelines. By participating you agree to the CODE_OF_CONDUCT.md.

AI-Assisted Contribution: We encourage using AI tools to contribute! Check out CONTRIBUTING_WITH_AI.md for best practices.

Submit a Skill Template: Have a great security persona for an SSDLC phase? Submit a Skill Template or add it to examples/templates/.

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Security

• Vulnerability reporting: See SECURITY.md for responsible disclosure.
• Security requirements: Follows security controls in SPEC §7.2.

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License

This project is licensed under the MIT License — see the LICENSE file for details.

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Star History

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Author and Links

• Author: PAN CHAO (Arthur Pan)
• Repository: github.com/arthurpanhku/DocSentinel
• SPEC and design docs: See links above.

If you use DocSentinel in your organization or contribute back, we'd love to hear from you (e.g. via GitHub Discussions or Issues).