Creating Human-Readable Activity Summary from Millions of Logs Using LLMs

Log data of all kinds store a wealth of information about benign and malicious identity and system behavior that are difficult for humans to understand. With examples drawn from AWS CloudTrail data, we will show practitioners how to leverage Large Language Models to mine log data and provide easily understandable, human-readable summaries of normal user session behavior as well as explain malicious activities.