The OpenAI Successful Login Base Rule serves as a foundational detection mechanism for monitoring successful login events in the OpenAI environment. Designed primarily for correlation with other rules, this rule does not generate alerts independently. Instead, it logs successful login events based on OpenAI audit logs, enabling security analysts to track and investigate authentication activities effectively. The rule is configured as a non-alerting mechanism to reduce noise while allowing for comprehensive visibility into login successes, which can be instrumental in identifying patterns of legitimate user behavior versus potential malicious activities. The rule's testing includes verifying a successful login against an expected true result and ensuring that failed logins do not erroneously match against the success criteria. This dual testing approach is critical for maintaining the accuracy of the security monitoring framework. With a defined threshold of one million for events within a sixty-minute deduplication period, this rule can efficiently handle a significant volume of authentication events without flooding the alerting system. The baselining of successful logins can be pivotal for building more complex correlation detections in the future.