OpenClaw Security Vault — Atomic "claw" control: every AI reach, within your sight.

Overview

Image: ClawVault Logo (./doc/images/logo1.png)
OpenClaw Security Vault — Atomic "claw" control: every AI reach, within your sight. This project positions itself as a guardian for AI interactions, offering a multi-layered approach to protect sensitive data, regulate agent behavior, and monitor how AI systems touch confidential assets.
Quick identifiers: MIT License badge, Python 3.10+ compatibility badge, and a Stars badge signaling community interest. These visual markers indicate openness, modern runtime requirements, and a healthy community footprint.
Language and accessibility: English primary documentation with a Chinese version available. This ensures wider adoption while preserving precise guidance for diverse users.
Image: ClawVault Cartoon (./doc/images/cartoon_en.png)
Core promise: a secure, auditable vault where private files, API keys, credentials, and other sensitive materials are guarded from unauthorized AI access, while still enabling productive, policy-driven interactions with AI agents.

Visual Identity and Tone

Image: Visual cartoon representation of ClawVault (./doc/images/cartoon_en.png)
Aims of the visuals: convey the sense of a protective, precise, and approachable security system for AI tooling. The cartoon emphasizes the balance between rigorous security and accessible usability, signaling to developers and organizations that safety can coexist with productive AI workflows.
Logo usage: the logo marker anchors the brand in all integrations, while supporting graphics (like the cartoon) help communicate concepts such as interception, policy enforcement, and secure data handling in a human-friendly way.

Who This Is For

Target concerns:
You are worried about leaking personal private data when AI agents are in play.
You want to prevent AI agents from accessing API keys, private files, and credentials.
You need to stop mishandling of sensitive or confidential files by agents.
You require logging of AI interactions with private data for auditing and compliance.
You want to detect AI injection attacks and other dangerous commands.
Activation flow in brief:
Load private files into a secure vault.
Set up and customize secure storage and access controls.
Create remote management skills to govern AI behavior and interactions.

Core Visual Effect and Demonstrations

Visual indicators of interception and protection:
Interception visuals show how traffic or actions are observed and potentially blocked.
Interception Record captures the details of access events and policy decisions.
Images for visual reference:
Blocked interactions (examples): block-tui.png and block-web.png depict how the system interrupts unsafe or policy-violating activity.
Accessibility and transparency: the visuals communicate that every touchpoint with protected assets is monitored and governed.
Important note: the tool emphasizes real-time awareness and historical insight, making it possible to audit who touched what in the vault on any given day.

Core Capabilities (High-Level)

The architecture is designed around three intertwined layers, each contributing a pillar of security and control: 1) Visual Monitoring 2) Atomic Control 3) Generative Capabilities
Visual Monitoring
Users configure a personal vault, selecting the Agents, Skills, credentials, and files that deserve protection.
When any authorized or unauthorized touch occurs, the Security Lobster notifies the operator with details about who touched what and when.
Technical backbone includes event collection through an API gateway and file-side monitoring, covering invocation records, file accesses, and change tracking.
Supports both periodic and real-time alerting to keep operators informed without requiring constant manual checks.
Atomic Control
Fine-grained governance at the Agent level through composable atomic capabilities.
Elements include: Agent interaction and invocation policies, model routing, whitelists, quota controls, and security detections (sensitive information, credential exposure, prompt injection protection, etc.).
Users can mix and match atomic capabilities like building blocks to assemble reusable policy configurations tailored to specific environments or workflows.
Generative Capabilities
Each “storage chamber” in the vault ships with built-in security scenarios and allows additional detection scenarios and Skills to be added using natural language—by mobilizing atomic capabilities.
Example: a user can instruct the system through a chat interface to enforce sensitive data desensitization for PDFs containing the word “contract” and restrict to a specific model (e.g., GPT-4o-mini) with a token usage cap.
The system translates natural language prompts into concrete policy rules and enforcement actions, enabling rapid, policy-driven responses to evolving threats and needs.

✨ Features (Key Functionalities)

Sensitive Data Detection: Recognizes API keys, passwords, PII, credit cards, and more than 15 pattern types, providing early visibility into potential data leaks.
Prompt Injection Defense: Guards against role hijacking, instruction override, and data exfiltration, preserving the integrity of agent prompts and outputs.
Dangerous Command Guard: Intercepts dangerous shell commands, such as rm -rf, curl|bash sequences, and attempts at privilege escalation.
Auto-Sanitization: Replaces secrets with placeholders in transit and in responses, with the ability to restore original content where appropriate under controlled conditions.
Token Budget Control: Enforces daily or monthly token usage budgets with alerts, helping manage costs and detect anomalous usage patterns.
Real-Time Dashboard: A web-based UI that shows per-agent configuration, detection results, and quick test outcomes for rapid validation and tuning.
Transparent Proxy Gateway Module: A crucial component that sits between your AI tooling and external APIs (e.g., OpenAI, Anthropic), enabling safe, auditable interception of traffic to enforce policies without sacrificing performance.

Quick Start and Deployment (Guided Pathways)

Available routes:
Option 1: Install as OpenClaw Skill (Recommended)
- Command flow (illustrative):
- Install from ClawHub: openclaw skills install tophant-clawvault
- Or install via clawhub CLI: clawhub install tophant-clawvault
- ClawHub bridge: The skill guides installation and ongoing management with convenient commands such as:
- /clawvault install --mode quick (Quick setup)
- /clawvault health (Check status)
- /clawvault generate-rule "Block AWS credentials" (Create security rules)
- /clawvault test --category all (Run detection tests)
- Documentation: Skills directory at skills/tophant-clawvault/ contains detailed guidance.
Option 2: Install as Python Package
- Commands:
- pip install -e .
- clawvault start (proxy + dashboard)
- clawvault scan "password=MySecret key=sk-proj-abc123" (Scan text for sensitive data)
- clawvault demo (Interactive demo)
Deploying to a server
- Commands:
- One-command deployment: ./scripts/deploy.sh root
- On the server: setup integration + start: ./scripts/setup.sh and ./scripts/start.sh
Scripted tooling and usage
deploy.sh: Deploy to cloud server
start.sh: Start ClawVault and optionally the OpenClaw if requested
stop.sh: Stop all services
test.sh: Run CLI and API tests
setup.sh: Prepare OpenClaw proxy integration
uninstall.sh: Remove and restore original system state
Quick deployment philosophy: the workflow emphasizes a loop of install, configure, test, and monitor, ensuring that policy intent is preserved from the moment of deployment into routine operation.

Architecture in Focus

OpenClaw integration architecture
ClawVault acts as the Security Vault portion, with a gateway module that sits alongside an overarching OpenClaw system.
Core modules:
- Gateway Module: Transparent proxy on port 8765, responsible for traffic interception and routing to detection and guard components.
- Detection Engine: Performs sensitive data detection, injection pattern recognition, and dangerous command detection.
- Guard / Sanitizer: Decides whether to Allow, Block, or Sanitize requests and responses based on policy.
- Audit + Monitor: Uses a lightweight storage layer (SQLite) for token budgets and event history.
- Dashboard: Web UI running on port 8766, showing agent configurations, detections, and test outcomes.
Data flow simplification
When a request or data interaction occurs, it passes through the gateway.
The detection engine analyzes the content for sensitive material or policy violations.
The guard decides action (allow, block, or sanitize).
The audit log keeps a record for review and compliance.
The dashboard provides real-time visibility and management.
Important configuration anchors
proxy: port 8765
intercept_hosts: a list such as api.openai.com and api.anthropic.com
guard mode: interactive, strict, or permissive
monitor: dailytokenbudget
These components together create a feedback loop that supports continuous improvement and policy refinement.

Configuration Essentials

Primary configuration areas (illustrative YAML snippet):
proxy: port: 8765
intercept_hosts: ["api.openai.com", "api.anthropic.com"]
guard: mode: "interactive" (choices: interactive | strict | permissive)
monitor: dailytokenbudget: 50000
Practical implications
The proxy placement ensures centralized control of AI traffic.
Intercepted hosts specify where policy checks are enforced.
Guard mode determines how aggressively the system enforces policies.
Token budget helps balance performance, cost, and safety goals.
Purpose of the configuration is to provide a reproducible and auditable foundation for secure AI interactions across environments.

Development and Documentation Ecosystem

Development progress highlights (Capability Modules)
API Gateway Monitoring & Interception: Implemented (V1 core capability)
File-side Monitoring: In progress (gradual integration)
Agent-level Atomic Control: In progress (gateway-side available; expansion underway)
Generative Policy Orchestration: In progress (gradual integration)
Documentation suite
Development Setup: doc/INSTALL_DEV.md (local development environment)
Production Deployment: doc/INSTALL_PRODUCTION.md (server deployment)
OpenClaw Integration: doc/OPENCLAW_INTEGRATION.md (connect with OpenClaw)
Architecture: doc/architecture.md
Guard Modes: doc/GUARD_MODE.md (strict / interactive / permissive)
Scenarios: doc/scenes.md (use cases and roadmap)
Development workflow
Typical steps include cloning the repository, setting up a virtual environment, installing dev dependencies, and running tests with pytest.
The emphasis is on an end-to-end development experience that supports both code contributions and policy modeling enhancements.

Development Environment and Execution

Local development steps (illustrative)
git clone https://github.com/tophant-ai/ClawVault.git
cd ClawVault
python3 -m venv venv
source venv/bin/activate
pip install -e ".[dev]"
pytest
Environment goals
A reproducible dev environment that mirrors production constraints, enabling meaningful testing of detection, policy enforcement, and audit capabilities.
A design that helps developers verify integration with OpenClaw and external AI toolchains.

Licensing, Ownership, and Community

Licensing
MIT license governs the project, promoting openness and collaboration.
Ownership
© 2026 Tophant (https://www.tophant.com/)
Community and collaboration
GitHub Issues: for bug reports and feature requests
Security Issues: designated channel for vulnerability reports
Purpose-driven message
“Built for people who want to secure AI, not babysit agents.” The project emphasizes enabling secure AI-enabled workflows without micromanaging or stifling innovation.

Practical Takeaways: How ClawVault Helps You

Security and privacy enablement
You can prevent AI agents from accessing sensitive assets or credentials.
You gain end-to-end visibility into who touched what and when, across the entire vault.
Flexible governance and policy design
Atomic capabilities let you construct modular, reusable security policies tailored to teams and use cases.
Generative capabilities facilitate rapid policy generation through natural language prompts, bridging human intent with automated enforcement.
Operational efficiency
Real-time interception and automatic sanitization reduce risk while keeping AI workflows productive.
Token budget control helps manage operating costs and prevent unexpected expenditures from AI usage.
Observability and compliance
The Audit + Monitor and web-based dashboard deliver auditable trails and verification capabilities for governance and compliance reporting.

Images and Visual References Embedded in the Experience

Image: ClawVault Logo (./doc/images/logo1.png) anchors branding and identity, representing the core idea of a protective vault for AI interactions.
Image: ClawVault Cartoon (./doc/images/cartoon_en.png) reinforces the approachable security posture and explains complex ideas in a human-friendly way.
Image: Blocked Interception (./doc/images/block-tui.png) illustrates how the system intercepts risky interactions in a user interface context.
Image: Blocked Web (./doc/images/block-web.png) demonstrates interception of web-based interactions and API calls.
These visual references support a narrative where security, visibility, and policy enforcement are tangible and operable.

Final Notes and Vision

Summary
ClawVault (OpenClaw Security Vault) provides a comprehensive, adaptable, and auditable framework to secure AI interactions.
The combination of visual monitoring, atomic control, and generative capabilities enables precise governance without sacrificing usability.
The architecture supports both quick-start deployment and long-term, policy-driven security management across diverse AI workflows.
Long-term aspiration
The project aspires to empower organizations to deploy AI in ways that are observable, controllable, and compliant—without slowing innovation.
By integrating proxy-based interception, robust detection, and modular policy construction, ClawVault aims to become a trusted backbone for responsible AI in production environments.

For further exploration, see:
Documentation index at doc/
Production and development guides in doc/INSTALLPRODUCTION.md and doc/INSTALLDEV.md
Architecture and guard-mode specifics in the corresponding doc pages
Skills and integration guidance in skills/tophant-clawvault/ on ClawHub
Final invitation
For teams building AI-assisted tools who want to maintain strict data boundaries, ClawVault offers a path forward that respects privacy, security, and operational realities.

Appendices and Quick References (Operational Snippets)

Quick commands (reference only, inline)
Install as OpenClaw Skill: openclaw skills install tophant-clawvault
Start as Python package: clawvault start
Run a basic scan: clawvault scan "password=MySecret key=sk-proj-abc123"
Deploy to server: ./scripts/deploy.sh root
Supplemental notes
The system is designed to be incrementally adoptable—start with a basic vault and simple rules, then progressively layer in atomic capabilities and more sophisticated detection scenarios as needed.

Closing Reflection

The ClawVault project embodies a commitment to secure AI workflows that do not demand compromising security for productivity. By presenting a structured, modular, and auditable approach, it offers organizations a practical toolkit to prevent data leaks, enforce policy, and maintain visibility across AI interactions. The combination of a robust architectural core, friendly visual identity, and pragmatic deployment pathways makes ClawVault a compelling option for teams seeking to secure AI without constraint-laden babysitting.
Image references recap:
ClawVault Logo: ./doc/images/logo1.png
ClawVault Cartoon: ./doc/images/cartoon_en.png
Interception visuals: ./doc/images/block-tui.png, ./doc/images/block-web.png
Endnote: This document presents a detailed, structured description suitable for readers who want a thorough understanding of OpenClaw Security Vault and its ClawVault component, its capabilities, deployment paths, and operational philosophy.

OpenClaw Security Vault — Atomic "claw" control: every AI reach, within your sight.

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GitHub - tophant-ai/ClawVault: OpenClaw Security Vault — Atomic "claw" control: every AI reach, within your sight.

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