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repository arthurpanhku/DocSentinel;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
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Anime cyber kitsune sentinel for the DocSentinel console and documentation.
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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Product Tour
The local React console brings the main workflow into one operational surface:
- **Command Center**: live API and LLM status, assessment throughput, review
demand, remediation queues, and recent activity.
- **Assessment Workbench**: upload project documents, choose SSDLC phase/skill,
inspect AI-generated risks, and complete human review.
- **Governance Portal**: create projects, apply public framework overlays,
generate controls, submit evidence, and track Pallas Lens readiness.
- **Knowledge Base**: ingest policies and standards for RAG-backed review.
- **Agent Integrations**: expose governed MCP and A2A tools to coding agents and
multi-agent platforms without granting approval authority.
- **Settings**: switch providers such as DeepSeek, OpenAI, Anthropic, Qwen, or
Ollama; API keys are accepted locally and only shown as masked previews.
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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. |