Automated security assessment for documents and questionnaires
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DocSentinel Automated security assessment for documents and questionnaires
What is DocSentinel?
DocSentinel is an AI-powered assistant for security teams. It automates the review of security-related documents, forms, and reports (e.g. Security Questionnaires, design docs, compliance evidence), compares them against your policy and knowledge base, and produces structured assessment reports with risks, compliance gaps, and remediation suggestions.
🚀 Agent Ready: Supports Model Context Protocol (MCP) to be used as a "skill" by OpenClaw, Claude Desktop, and other autonomous agents.
- 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 without proportionally scaling headcount.
Why DocSentinel?
| Pain Point | DocSentinel Solution |
|---|---|
| Fragmented criteriaPolicies, standards, and precedents are scattered. | Single knowledge base ensures consistent findings and traceability. |
| Heavy questionnaire workflowBusiness fills form → Security reviews → Business adds evidence → Security reviews again. | Automated first-pass and gap analysis reduces manual back-and-forth rounds. |
| Pre-release review pressureSecurity needs to review and sign off on technical docs before launch. | Structured reports help reviewers focus on decision-making, not line-by-line reading. |
| Scale vs. consistencyMany projects and standards lead to inconsistent or delayed manual reviews. | Unified pipeline with configurable scenarios keeps assessments consistent and auditable. |
See the full problem statement and product goals in SPEC.md.
Architecture
DocSentinel is built around an orchestrator that coordinates parsing, 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"]
API["REST API / MCP"]
end
subgraph Core["DocSentinel Core"]
Orch["Orchestrator"]
Mem["Memory"]
Skill["Skills"]
KB["Knowledge Base (RAG)"]
Parser["Parser"]
end
subgraph LLM["LLM Layer"]
Abst["LLM Abstraction"]
end
subgraph Backends["LLM Backends"]
Cloud["OpenAI / Claude / Qwen"]
Local["Ollama / vLLM"]
end
User --> API
API --> Orch
Orch <--> Mem
Orch --> Skill
Orch --> KB
Orch -->
Tools (2)
assess_documentAnalyzes a security document or questionnaire against the knowledge base to identify risks and compliance gaps.query_knowledge_baseRetrieves information from the uploaded policy and compliance documents.Environment Variables
OPENAI_API_KEYAPI key for OpenAI LLM accessANTHROPIC_API_KEYAPI key for Claude LLM accessConfiguration
{"mcpServers": {"docsentinel": {"command": "python", "args": ["-m", "docsentinel.mcp"], "env": {"OPENAI_API_KEY": "your-key-here"}}}}