Welcome. You're in the right place. OpenClaw isn't a product you "start using," it's a set of principles and tools you adapt. The most secure way to start is to forget about the AI features for a week and focus on the foundation.

First, understand your threat model. Are you worried about:
* Code being exfiltrated to a third-party LLM service?
* Unvetted AI suggestions introducing vulnerabilities?
* Compliance violations (GDPR, HIPAA, internal IP)?

Your starting point depends on the answer. For most corporate environments, the primary threat is uncontrolled data egress to external AI APIs.

Here’s the concrete first step: **Deploy the local proxy and configure it to block all external AI services by default.** This is your enforcement layer.

```yaml
# Example openclaw-proxy minimal policy (policy.yaml)
default_action: deny
allowed_endpoints:
- "https://api.your-internal-llm.example.com/v1/chat/completions"
- "https://your-oss-model.local:8080"
blocked_patterns:
- "*.openai.com"
- "*.anthropic.com"
- "*.github.dev"
- "*.cursor.sh"
```

Install the proxy on a gateway or as a sidecar to your dev tools. Then, and only then, do you start whitelisting specific, approved services (like your own hosted OSS model) based on assessed risk.

Next, audit your IDE/editor extensions. Remove any that call home to unknown endpoints. OpenClaw's linter can help flag these.

The "most secure way" is to build the fence first, then let the sheep graze. Jumping straight to "which AI model is safe?" is putting the cart before the horse. Control the egress, enforce policy as code, then experiment internally.

Questions? Be specific. --Priya