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Building an OpenClaw Security Vault for AI Agents

#ai #opensource #python #security

Once AI agents start using tools and external APIs, the engineering problem changes.

It is no longer only about output quality. It becomes a runtime problem: how do you inspect traffic, detect risky behavior, limit what the system can do, and keep token spend under control?

That is the angle ClawVault takes.

According to the current repository README, ClawVault is an open-source OpenClaw Security Vault for AI agents and AI applications, centered on three ideas:

  1. Visual Monitoring
    Monitoring AI agents and model invocations.

  2. Atomic Control
    Applying finer-grained control over agent capabilities and permissions.

  3. Generative Policies
    Using natural language to define policy logic.

What makes the repo more concrete is that it also lists the operational features around those ideas:

  • sensitive data detection
  • prompt injection defense
  • dangerous command guard
  • auto-sanitization
  • token budget control
  • real-time dashboard

The architecture shown in the README is also useful because it makes the control path explicit:

  • a transparent proxy gateway for AI traffic
  • a detection engine for sensitive data, injection patterns, and dangerous commands
  • a guard / sanitizer that can allow, block, or sanitize
  • audit + monitoring with token budget tracking
  • a web dashboard for configuration and review

That is a stronger pattern than scattering checks across application code, because it gives you a central place to observe and govern runtime behavior.

The quick-start shown in the repo today is:

pip install -e .
clawvault start
clawvault scan "password=MySecret key=sk-proj-abc123"
clawvault demo
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And the sample config looks like this:

proxy:
  port: 8765
  intercept_hosts: ["api.openai.com", "api.anthropic.com"]

guard:
  mode: "interactive"  # interactive | strict | permissive

monitor:
  daily_token_budget: 50000
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One detail worth noting: the README also separates what is already implemented from what is still expanding. API gateway monitoring and interception are marked as implemented, while file-side monitoring, broader agent-level atomic control, and generative policy orchestration are still in progress.

That makes ClawVault interesting not because it claims to solve everything, but because it defines a clear control-layer shape for production AI systems.

Repo:
https://github.com/tophant-ai/ClawVault

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