In today’s enterprise, the biggest threats are often the hardest to see. As businesses scale digital access across users, services, and non-human identities, attackers are evolving to target trust itself. Credentials, entitlements, and automation open the door for speed and productivity, but they also create opportunity for those who know how to hide in plain sight.

OpenText recently partnered with Ponemon Institute to survey nearly 2,000 CISOs, CIOs, and security leaders. The results are sobering: Over half experienced a breach in the past two years, averaging three incidents per organization. The top targets? Customer data, financial records, and source code—information that reveals not just what a company does, but how it competes.

Even with encryption, DLP, and identity governance in place, too many threats continue to move laterally and silently. Just 41% of leaders said they feel confident in their ability to reduce insider data loss while maintaining operational trust. The message is clear: Rule-based detection is no longer enough.

Insider threats don’t set off sirens

SIEMs do a great job with what they’re built for—spotting known threats, matching signatures, and triggering alerts. But the most dangerous actors aren’t necessarily noisy. They know the environment. They use valid credentials. They stay within policy and hide in the noise until it’s too late.

That’s why modern detection strategies must go beyond identity and access. It's not just about who someone is, it's about what they do. OpenText™ Core Threat Detection and Response was built for exactly this challenge. It applies adaptive behavioral analytics to surface the kinds of risk that don’t trip traditional alarms. By building a profile of how each identity behaves over time, the platform can spot early signs of impersonation, privilege misuse, or coordinated access abuse.

Rather than looking for a needle in a haystack, it watches how the haystack behaves.

Through integrations with Microsoft Defender for Endpoint and Entra ID, the platform gathers telemetry from real-world activity including logins, access patterns, and endpoint behavior, correlating it with advanced models to spot subtle, emergent threats. With the OpenText Cybersecurity Aviator summaries, threat narratives are rendered in plain language so SOC analysts and CISOs alike can quickly understand what happened, why it matters, and what to do next.

Behavior is the new perimeter

Modern threats don't exploit technical vulnerabilities; they exploit human ones. Credential abuse, lateral movement, and data staging all happen under the guise of routine activity. That’s why OpenText Core Threat Detection and Response focuses on behavior, not just events.

By continuously modeling what "normal" looks like across your environment, it surfaces deviations that actually matter without overwhelming analysts with false positives. Whether it’s an imposter using legitimate access or a malicious insider slowly exfiltrating files, the platform helps you catch them earlier.

What makes OpenText’s approach different is the science behind it. At its core is a mature, field-tested anomaly detection engine that utilizes a range of statistical techniques from Bayesian inference to clustering to dimensionality reduction to understand entity behavior in context. These methods don’t just flag rare events, they identify when behaviors are statistically rare for a particular user, peer group, or system role.

That was a lot of words to say that this precision matters. Security teams are already overwhelmed. False positives burn time, introduce doubt, and ultimately reduce response effectiveness. The models in Core Threat Detection and Response are designed to detect, prioritize, explain, and ultimately accelerate response to the most advanced threats.

This is what modern defense looks like: less noise, more signal. Less alert fatigue, more action. And a SOC that’s not just responding but outsmarting the adversary. Learn more about what OpenText™ Core Threat Detection and Response can do for your organization.

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