This Proof of Concept explores how Microsoft Copilot on Windows can assist in interpreting and summarizing application logs directly from the desktop.
The goal is to test whether AI can accelerate bottom-up troubleshooting by providing instant context, explanations, and possible resolutions for recurring log patterns — without specialized tools or external data connections.
In traditional troubleshooting, log analysis can be time-consuming, requiring pattern recognition, documentation lookup, and manual correlation with known issues.
This PoC approaches the problem from the desktop upward: using an AI assistant (Copilot) to read, summarize, and suggest meaning behind anonymized logs.
It complements the earlier tested https://github.com/A1cX/AI_Log_PoC that used sample data and Jupyter Noytebook on Hadoop logs to detect anomalies by focusing now on local intelligence and assistive reasoning rather than system-level data aggregation.
- Collect anonymized logs from the local environment.
- Manually anonymize and sanitize the data - this step is most challenging but required due to on prem application characteristics
- Use Copilot to:
- Summarize errors or warnings.
- Explain likely causes.
- Suggest next troubleshooting steps.
- Compare the AI's reasoning with known resolutions or admin notes.
All log samples are stripped of:
- Hostnames or system identifiers
- IP addresses or usernames
- Product or environment names
Check the possiblity os using PowerShell or Python script to perform anonymization before testing.
Each Copilot test is documented in results/copilot_responses.md with:
- Log snippet tested
- Prompt used
- AI output summary
- Human validation comment
- Compare Copilot interpretations vs human admin notes
- Test with multiple log types (application, system, network)
- Measure time savings or accuracy improvement