DocSentinel

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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 + FastAPI service + MCP/A2A agent gateway, DocSentinel integrates into local security review workflows, CI/CD pipelines, AI agents, and multi-agent platforms without giving external agents approval authority.

• 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 coverageMost tools only address testing/deployment. | Full lifecycle agents cover all 6 SSDLC phases with dedicated AI personas. | | Fragmented criteriaPolicies, standards, and precedents are scattered. | Single knowledge base ensures consistent findings and traceability. | | No automated threat modelingThreat models are created ad-hoc. | Design Agent generates STRIDE/DREAD threat models from architecture docs. | | Heavy questionnaire workflowEndless review cycles. | Automated first-pass and gap analysis reduces manual back-and-forth rounds. | | SAST/DAST report overloadToo many findings, too little context. | Testing Agent triages, prioritizes, and maps findings to threat models. | | Pre-release review pressureEverything 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. consistencyManual reviews vary by reviewer. | LangGraph workflows and unified pipeline ensure consistent, auditable assessment across projects. | | SSDLC coverage gapsSecurity 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](./SPEC.md).

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Architecture

DocSentinel is built on a React Console plus FastAPI, MCP, and A2A access layer, with LangGraph for stateful agent orchestration and LangChain for unified LLM access. REST and agent protocols share one task lifecycle; MCP and A2A submissions always enter the human-review workflow.

flowchart TB
    subgraph User["User / Security Staff"]
    end
    subgraph Access["Access Layer"]
        Console["React Console(Vite + Tailwind)"]
        API["REST API(FastAPI)"]
        Gateway["Agent Gateway"]
        MCP["MCP(stdio + HTTP)"]
        A2A["A2A 1.0(JSON-RPC)"]
        Tasks["Shared Assessment Service"]
    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 & A2A
    MCP & A2A --> Gateway
    API & Gateway --> Tasks
    Tasks --> 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](./ARCHITECTURE.md) and [docs/01-architecture-and-tech-stack.md](./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 & A2A Ready

Use the local React console for human review, integrate CI/CD pipelines through the REST API, or expose the same assessment capabilities to AI agents (Claude Desktop, Cursor, OpenClaw) through MCP. A2A-compatible platforms can delegate assessment tasks to DocSentinel as a specialist security agent.

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Agent Integration (MCP + A2A)

Use MCP to expose bounded tools to Claude Desktop, Cursor, coding agents, and workflow platforms. Use A2A to expose DocSentinel as a remote specialist agent. REST, MCP, and A2A submissions share the same task lifecycle and activity log. Agent-created assessments always remain drafts until a human reviewer approves them in the console.

| Interface | Endpoint | Purpose | | :--- | :--- | :--- | | MCP stdio | python app/mcp_server.py | Local desktop and coding-agent integration | | MCP Streamable HTTP | POST /mcp/ | Remote tool discovery and invocation | | A2A Agent Card | GET /.well-known/agent-card.json | Standards-based agent discovery | | A2A JSON-RPC | POST /a2a | Remote task delegation | | Integration status | GET /api/v1/integrations/agents/status | Non-secret protocol and capability status | | Console | /console/integrations | Human-readable integration state |

MCP exposes five governed tools:

• submit_document_assessment
• get_assessment_status
• assess_document (compatibility tool)
• query_knowledge_base
• get_agent_gateway_status

The A2A Agent Card advertises assessment submission, assessment retrieval, and security knowledge query skills.

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",
        "MCP_DOCUMENT_ROOTS": "/absolute/path/to/approved/documents"
      }
    }
  }
}

assess_document only reads files inside MCP_DOCUMENT_ROOTS. Use : to separate multiple roots on macOS/Linux, or ; on Windows. If unset, the server only allows ./examples.

FastAPI also serves Streamable HTTP MCP at /mcp/ and publishes the A2A Agent Card at /.well-known/agent-card.json. Tokenless protocol access is loopback-only. External agents cannot approve, reject, or bypass human review.

For network, Docker, or reverse-proxy access, configure:

AGENT_GATEWAY_TOKEN=generate-a-long-random-value
AGENT_GATEWAY_PUBLIC_URL=https://docsentinel.internal.example
AGENT_GATEWAY_ALLOWED_HOSTS=docsentinel.internal.example
AGENT_GATEWAY_ALLOWED_ORIGINS=https://trusted-agent-console.internal.example

Clients send Authorization: Bearer . Keep MCP_DOCUMENT_ROOTS, allowed hosts, and allowed origins as narrow as possible. Docker bridge traffic is not treated as loopback, so published MCP/A2A ports require a token.

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](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

• React Console: http://localhost:8000/console
• Agent Integrations: http://localhost:8000/console/integrations
• API Docs: http://localhost:8000/api-docs
• A2A Agent Card: http://localhost:8000/.well-known/agent-card.json

To connect an external agent through the published Docker port, set AGENT_GATEWAY_TOKEN in .env before running ./deploy.sh.

Option B: Manual Setup

Prerequisites: Python 3.10+ and Node.js 20+. 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
npm install --prefix frontend
npm run build --prefix frontend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

• API docs: http://localhost:8000/api-docs · Health: http://localhost:8000/health
• React Console: http://localhost:8000/console
• Agent Integrations: http://localhost:8000/console/integrations
• MCP Streamable HTTP: http://localhost:8000/mcp/
• A2A Agent Card: http://localhost:8000/.well-known/agent-card.json
• 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": "Wh

…

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [arthurpanhku](https://github.com/arthurpanhku)
- **Source:** [arthurpanhku/DocSentinel](https://github.com/arthurpanhku/DocSentinel)
- **License:** MIT

Install and usage instructions live in the source repository linked above.