Hey everyone,

I’ve been setting up my first AI agent with OpenClaw and I’m really excited, but I’ve hit a snag I’m hoping you can help with. I’m trying to design the audit log system, and I keep worrying about sensitive data like names, emails, or API keys accidentally getting written to the logs and just sitting there in plain text. I want the logs to be useful for figuring out what went wrong if there’s an incident, but I don’t want to create a data leak myself.

From what I’ve read, the logs need tool calls, decisions, and inputs/outputs. But if my agent processes a user support ticket that contains a home address, that address could end up in a “model input” log entry. How do you avoid logging that kind of PII while still keeping the log useful? Do you filter it out before it’s written, or mask it after?

I’m working with Python and Docker, and my current approach is a bit clumsy. I’m trying to write a wrapper function that scrubs known patterns before logging, but I’m sure I’m missing edge cases.

```python
def safe_log(content):
# Very basic example - I know this isn't enough
patterns = [r'bd{3}-d{2}-d{4}b', r'b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Z|a-z]{2,}b']
scrubbed = content
for p in patterns:
scrubbed = re.sub(p, '[REDACTED]', scrubbed)
return scrubbed
```

Is there a better design pattern or common practice for this? Should I be structuring my log data differently from the start? Any pointers or examples from your own setups would be incredibly helpful. Thanks in advance for guiding a newcomer through this!

- Tom