Ryan Bellows is a Senior Information Security Analyst who leads insider risk operations for a Fortune 500 biotechnology company. A U.S. Army veteran with experience spanning the Department of Defense, incident response, detection engineering, and enterprise insider risk, Ryan began working with insider threat cases as part of a broader Cyber Operations function. As the scale and complexity of that risk increased, he recognized that a reactive, case-by-case approach was neither efficient nor sustainable. Using the Insider Threat Matrix as the foundation for a structured operational model, Ryan helped transform insider risk management from an ad hoc responsibility within incident response into a formalized service delivery function with dedicated strategy, detections, and response processes. His perspective in this article is grounded in that practical experience of applying the ITM to program design, operational delivery, and enterprise risk reduction.
Introduction
Insider threat is one of the most difficult risk domains to manage effectively — not because the signals are absent, but because no single signal tells the full story. Traditional investigation approaches tend to match a subject's behavior to a known use case, which leaves critical context on the table and forces analysts to hunt for supporting signals after the fact. This article describes how aligning your detection engineering strategy to the Insider Threat Matrix™ (ITM) transforms insider risk operations into a measurable, enterprise risk reduction capability. In our program, this approach yielded an ~95% improvement in detection accuracy and an ~80% reduction in analysis time (measured against a pre-ITM baseline).
The ITM is a community-maintained framework that maps insider threat behaviors to structured articles. By chaining these articles together in a sequence, analysts can see how a series of individually ambiguous events builds into a recognizable threat pattern. The ITM gave us a way to capture that context by design, rather than reconstructing it retrospectively.
Adopting the ITM overhauled our approach. Instead of chasing individual signals, we shifted focus to tracking a subject's rising risk level based on sequenced behavioral elements. The improvement in speed and accuracy quickly earned stakeholder and leadership support, and within a year we had grown from an ad-hoc incident response capability into a dedicated insider risk service delivery function.
The Approach
The ITM's structure maps directly to detection engineering needs, enabling the following capabilities:
Identify risk levels based on a sequence of behavioral signals
Alerting on risk level
Risk level monitoring
Escalation prioritization
Threat landscape identification
Detection validation over hunting (analysts confirm pre-surfaced signals rather than searching for them from scratch)
Detection Engineering
Insider events span many different categories such as behavioral, messaging, endpoint, network, cloud, and physical risk signals. Because of this broad range, attempting to build individual targeted detections for every possible insider event can be inefficient or incomplete. Instead, the capture of a relevant ITM article for each risk signal to facilitate development of an ITM chain provides a means to address this issue. This sequence of events tells a complete story, as the examples below illustrate:
ITM Article | Risk Signal Description | Risk Signal Source |
|---|---|---|
MT003 – Leaver | Submitting resignation / offboarding / behavioral indicating a flight risk. | Behavioral / Messaging / Endpoint / Cloud |
ME024 - Access | Access to critical or sensitive assets | Endpoint / Network / Cloud / Physical |
PR025 – File Download | Downloading a file (storage location will impact severity) | Endpoint / Network / Cloud |
IF025 – Internal Credential Sharing | Disclosure of credentials to another employee | Messaging / Endpoint / Physical |
AF024 – Account Misuse | Leveraging an account in a manner that's unexpected or unauthorized | Behavioral / Endpoint / Cloud |
This example showcases the range of activity we can correlate from various sources. Additional articles can be detected to further enhance the story to denote severity of an insider event. Using the examples above, we'll explore a hypothetical scenario:
The subject begins an offboarding process under unamicable conditions (Monday). They collude with another employee to receive elevated privileges (Tuesday). With elevated permissions, the subject downloads critical information (Wednesday). The subject performs data egress activity of the downloaded information (Friday).
By themselves these risk signals are either not concerning or depending on organization culture, may find themselves in the gray area. As we aggregate these and form a chain of events the full story can surface. Leading us to understand what's happening without searching down many possible rabbit holes.
Risk Levels
Through this example we can see how a subject's behavior creates new risk signals that show an escalating insider event. As each event is performed throughout the week, our subject's risk level increases based upon ITM alignment. With this methodology we can track risk level and severity through the chain of events, allowing analysts to appropriately prioritize their efforts.
Alerting on Risk Level
When a subject has exceeded a predetermined threshold of acceptable risk, we can further leverage this methodology to generate an alert containing this timeline of events. The alert received will showcase the detected risk signals and when each signal surfaced. With this information, there's a timeline of an insider event allowing immediate understanding of scope for investigations. Without this information, analysis efforts can be too narrow resulting in missing critical risk signals, or too broad hindering efforts.
Threat Landscape Identification
As the incident management system builds a repository of ITM chains, two categories of intelligence emerge. First, patterns across true positive chains reveal common insider “attack paths” — sequences of ITM articles that precede confirmed adverse events. These high-likelihood chains inform where prevention and response investments should be focused. Second, chains that surface as false positives can be tuned or weighted down, improving the signal-to-noise ratio over time. This feedback loop means the detection capability improves continuously as the program matures.
Response Efforts
This detection engineering strategy directly shapes how the operations team responds to an insider event. By this point we have an alert containing a timeline of risk signals and through maturity of this methodology an identified / understanding of chains denoting severity of the timeline. As analysts engage the alert, they're spending less time hunting for additional / supporting risk signals, and the focus is shifted to validating these risk signals. This change accelerates response actions and renders a more comprehensive report that mitigates surprises during interviews.
Going back to our example: Prior to the ITM strategy an alert may flag a subject on a leaver list performing data egress activity. While this meets coverage for a leavers use-case and detects an adverse event, all other supporting risk signals are missed and require hunting. The difference is right at the start, an analyst would be unaware of “where did the data reside”, “what was the data timeline”, “how was the data accessed?”, “what was the leading indicators that resulted in this adverse event?” and “why should the subject in this leaver use-case be prioritized”. The answers this strategy provides is the realized impact to efficient insider risk management. At the start we either know or have a path forward to answering these questions.
Conclusion
The ~95% improvement in detection accuracy and ~80% reduction in analysis time described in this article are not the product of better tooling. They reflect a structural change in how insider events are understood: not as isolated signals matched to a use case, but as correlated chains of behavior that reveal intent and severity over time. That shift, enabled by alignment to the ITM, is what moves a program from reactive incident handling to proactive risk management.
The ITM's value lies not only in its framework for detection engineering, but in the shared intelligence it enables across the community of contributors. As the threat landscape evolves, so too does the ITM, ensuring that detection chains remain relevant against emerging insider tactics. This dynamic quality is critical for organizations that cannot afford to operate against a static picture of insider risk.
Ultimately, this approach transforms insider threat from a reactive discipline into a proactive risk management capability. Analysts enter each investigation with a timeline already in hand; leadership gains confidence from measurable outcomes, and the organization as a whole benefits from a sustained reduction in enterprise risk. For any organization looking to mature capabilities of their insider risk program, alignment to the Insider Threat Matrix™ offers a proven scalable path forward.
