Cloud environments create a flood of security signals, often reaching tens of thousands per day depending on the organization’s size. Security engineers and analysts spend a disproportionate share of their time triaging these signals instead of acting on legitimate threats. But the time-intensive parts of that work, such as identifying related signals and building a timeline, can be handled systematically, leaving teams free to focus on what actually requires human judgment.
This post walks through what a cloud security investigation looks like when most of the manual work is automated at the following stages:
Detecting changes in behavioral baselines for a userAnalyzing the API calls involved in the activityIdentifying patterns in the user’s new activityGenerating a final assessment of the activity
We’ll also show how Bits AI Security Analyst manages behavioral analysis and threat correlation at each stage, so analysts can spend less time reconstructing what happened and more time deciding how to respond.
Identify behavioral anomalies in a cloud security investigation
