How Texas A&M System Cuts Incident Resolution by 99% with Elastic Security

Protecting a public higher education system at the scale of the Texas A&M University System (TAMUS) is fundamentally different from protecting a typical enterprise. The cybersecurity team must defend not only 11 universities with tens of thousands of students, but also eight state agencies including the Texas Division of Emergency Management and the Texas A&M Forest Service. Research institutions within the system attract state-sponsored threat actors who target intellectual property, making the security posture critical not just to the university but to federal research partners and public safety infrastructure.

Before Elastic, the A&M System’s security analysts spent their time bouncing between multiple security products built on incompatible query languages. Gathering information required manual effort across siloed platforms, and when incidents occurred, the recovery process was slow and opaque. Long hours spent on documentation and correlation created analyst burnout and delayed response times. The security team had 30 days of telemetry from 25,000 endpoints, but no unified way to query it efficiently.

The team deployed Elastic Security for Endpoint across all devices in its universities, agencies, emergency response teams, and research organizations. A single interface now surfaces data from all sources—phishing feeds, device telemetry, and threat intelligence—queryable in a common language with a unified schema. Elastic’s machine learning capabilities run continuously in the background, flagging unusual patterns including previously unseen attack vectors related to fraud, data breaches, and denial-of-service campaigns. The automation layer handles documentation for security workflows, eliminating the manual write-up burden that had previously consumed significant analyst time.

The operational improvement was decisive: where a comparable security incident previously took months to resolve, the same scenario now takes approximately two hours—a 99% reduction in mean time to resolve. Automated documentation alone saves the team more than 100 analyst hours per month. The single-pane-of-glass approach also transformed analyst focus: where analysts once spent the majority of their time on reactive first-line triage, they can now focus on proactive threat hunting and detection engineering.

Looking ahead, the A&M System sees Elastic as a platform that grows with its security needs. The ability to integrate new data sources at any time—already spanning data from 25,000 endpoints—means the team can absorb new threats and data streams without re-architecting its security stack. For a public institution that must do more with constrained budgets, Elastic’s combination of automation, ML-driven detection, and operational efficiency represents a model for modern university cybersecurity.