Personal AI assistants are no longer toys.
Runtimes like OpenClaw (formerly Clawdbot / Moltbot) let agents:
- Execute shell commands
- Browse the web
- Read and write local files
- Send messages across Telegram, Discord, WhatsApp, and more
That power comes with a new class of risk — and most users have zero visibility into what their agents can actually do.
Today, we're introducing OG Personal, the personal edition of OpenGuardrails: the first guard agent designed specifically for personal AI assistants.
The Problem: Powerful Agents, Invisible Risk
Modern personal agents operate with real system access. But security today is implicit, fragile, and opaque:
- Users don't know what assets are exposed
- Prompt injection and tool abuse go unnoticed
- Credential leakage happens silently
- There's no audit trail when things go wrong
Security tooling has existed for enterprise AI — but not for individuals.
OG Personal changes that.
What Is OG Personal?
OG Personal runs as a local daemon alongside your assistant runtime.
It doesn't replace your agent. It watches, analyzes, and protects it in real time.
Core capabilities:
Observability
- Agent inventory and session tracking
- Asset discovery (files, credentials, tools, permissions)
- Entry point mapping and blast radius analysis
Threat Detection
- LLM-powered detection across 19 threat types
- Prompt injection, system override, credential exposure
- Malicious code, destructive file operations, data exfiltration
Controls
- Safety rules with confirmation gates
- Isolation and auto-blocking policies
- Emergency pause and kill switches
Governance
- Execution timeline replay
- Structured audit logs
- Risk scoring and historical trends
All designed for non-technical users, not security teams.
How It Works (High Level)
OG Personal integrates at the most critical layer: LLM input, output, and tool execution.
Using OpenClaw's plugin system, it intercepts:
- User messages
- System prompts
- LLM responses
- Tool calls (shell, file, network, etc.)
Each step is scanned using a two-tier model:
- Fast local checks for obvious risks
- Deep LLM analysis via OpenGuardrails for ambiguous cases
Every decision is logged. Every action is attributable.
Open Source, User-Owned
OG Personal is fully open source and runs locally.
- No hidden enforcement logic
- No opaque cloud control plane
- Users own their data, logs, and decisions
You can inspect the entire codebase here: https://github.com/openguardrails/og-personal
Why This Matters
Personal AI is becoming long-running infrastructure, not a chat session.
As agents gain autonomy, security can't be an afterthought — and it can't require a security team either.
OG Personal is our answer to that gap:
“Security that's visible, controllable, and understandable — for individuals.”
This is just the beginning. We're excited to build this together with the community.
Questions or want to contribute? Reach out to thomas@openguardrails.com or visit the OG Personal GitHub repository.