The Reveal Platform
HR notifications of resignations often arrive too late to prevent data theft. Learn how behavioral indicators can identify “flight risk” employees weeks before they submit their notice, preventing exfiltration before the assets leave the building.
In a CISO’s mind, the most dangerous day isn’t the day an employee leaves—it’s the 30 days before they tell you they’re going.
Legacy Insider Risk Management (IRM) relies on a broken trigger: the HR notification. By the time HR marks a user as “departing,” the crown jewels—client lists, source code, and strategic plans—have often already been Bcc’d to a personal Gmail account or moved to a personal cloud.
The problem is contextual blindness. A recruiter browsing LinkedIn is “doing their job.” A financial advisor downloading a client list might be “preparing for a meeting.” In isolation, there is noise. In sequence, they are a breach in progress.
To understand how modern exfiltration occurs, let’s look at a common, high-risk persona: the Financial Advisor.
Meet “Ken Winston” (a fictional character from a real world scenario). Ken has access to sensitive wealth management data. Under a legacy SIEM, Ken is treated as a “trusted user” until his last day. Under Gurucul REVEAL, Ken’s path toward the exit is tracked through subtle Behavioral Identifiers:
The verdict? The file contains the exact contact details Ken was researching. The intent is clear: Ken is about to take his book of business to a competitor.
Gurucul REVEAL dismantles the “Legacy SIEM” failure of reactive monitoring through Model-Driven Security.
By moving the point of detection from the “Notice Period” to the “Intent Phase,” Gurucul reduces MTTR by up to 83% and stops the risk before it becomes a loss.
Security is no longer about watching the perimeter; it’s about understanding the person. If your defense strategy begins only after HR hits “print” on a resignation letter, you aren’t managing risk; you’re documenting a loss. Gurucul REVEAL flips this script by transforming fragmented logs into a clear narrative of intent, allowing you to intercept data exfiltration before the “insider” becomes a “flight risk.”
Stop Guessing Who is Leaving. See how Gurucul AI IRM predicts flight risk and stops data exfiltration in a live sandbox environment.
Taylor Smith
Aparna Sharma
Most data theft occurs weeks before an employee submits a resignation notice. By the time HR alerts security teams, sensitive files such as customer lists, code repositories, or strategy documents may already have been exfiltrated.
The misconception is that risk begins when HR flags someone as a departing employee. In reality, data exfiltration usually begins 30+ days earlier, during the job search and preparation phase, long before official notice.
Warning signs include: uploading resumes, visiting job sites, researching client contact info, accessing atypical systems, and emailing work documents to personal accounts. Individually harmless, together they reveal intent.
Traditional tools rely on static rules and HR triggers, offering no context behind behavior. Without behavioral analytics, they treat employees as trusted users until the day they resign, missing the entire intent timeline.
Gurucul AI IRM uses identity correlation and 4,000+ ML behavioral models to detect abnormal patterns, raise risk scores, and automatically build investigation timelines. This shifts detection from the notice period to the intent phase, stopping exfiltration before it begins.
