As AI applications move into real workflows, the engineering problem changes.

It is no longer only about model quality. Teams also need a clearer way to review AI activity, narrow capability boundaries, and define policies around interactions that touch tools, APIs, and sensitive workflows.

That is the direction ClawVault takes.

According to the current repository README, the project positions itself as an OpenClaw Security Vault for AI agents and AI applications.

• visual monitoring

• atomic control

• generative policies

The README also lists concrete areas such as:

• sensitive data detection

• prompt injection defense

• dangerous command guard

• auto-sanitization

• token budget control

• a real-time dashboard

Architecturally, the repo describes a control path with:

• a transparent proxy gateway

• a detection engine

• a guard / sanitizer layer

• audit + monitoring

• a dashboard

One detail worth noting is that the README separates what is already implemented from what is still in progress, rather than presenting everything as fully finished.

Open source repository: https://github.com/tophant-ai/ClawVault

How is your team approaching this today: interaction visibility, capability boundaries, or policy definition?