How to investigate cloud credential compromise with Bits AI Security Analyst

Cloud environments produce an overwhelming number of security signals daily, leading security engineers and analysts to spend excessive time triaging rather than addressing genuine threats. Automating the manual aspects of cloud security investigations, such as identifying related signals and building timelines, allows teams to focus on tasks requiring human judgment. The article explores automating such processes with Bits AI Security Analyst, which streamlines behavioral analysis and threat correlation. Using a specific scenario involving an AWS IAM user suddenly generating numerous API calls, it illustrates how Bits AI identifies suspicious activity and assesses potential threats by analyzing signal patterns, API call structures, and threat intelligence data. The scenario highlights the importance of distinguishing between legitimate and suspicious actions, such as phishing campaigns, by examining API call patterns, user agents, and IP addresses. The investigation process involves identifying potential account compromises through unauthorized API calls and determining the actor's intentions, such as phishing, by examining the structure and frequency of these calls. Ultimately, Bits AI Security Analyst aids in accelerating cloud security investigations, enabling analysts to make informed decisions and prioritize containment and remediation efforts based on concrete behavioral and threat intelligence data.