Systems and system users from large government agencies produce terabytes of data daily that must be labeled, processed, transferred, stored, and deleted. This data must be protected throughout its lifecycle in accordance with its identified labeling. To enhance data protection, a government agency launched the Enterprise Security Audit Trails (ESAT) program. Led by the Cyber Solutions Development (CSD) team, this program centralizes analytical auditing using Splunk for real-time cybersecurity visibility. The CSD teams handle terabytes of data, including audit, security event, configuration settings, vulnerability, whitelisting, security endpoint, and system access – and each area would benefit from leveraging artificial intelligence (AI) and machine learning (ML). In this paper, we talk about a case study with a proposed set of activities where we have secured citizens’ large volume of data utilizing various cybersecurity solutions. Furthermore, it provides a strategy for using AI/ML technologies to modernize, optimize and automate (MOA) security events handling.