Connecting the dots: Revealing access insights with context-aware AI

At 7:42 a.m., a nurse badges into a shared workstation and opens an adult patient's record. The login succeeds. The badge is valid. The nurse is authenticated. Nothing about the event looks obviously wrong.

But the context tells a different story.

The nurse is from pediatrics and rarely accesses adult patient records. The workstation is in a low-traffic area outside her normal work pattern. Finally, the patient is an immediate family member of one of her coworkers and is not assigned to her unit or care team.

Viewed alone, the access looks routine. Viewed together, the details point to something different: a legitimate user in an unusual location, looking at a record she has no reason to see. No one notices at the time. But months later, after the patient files a complaint and a time-consuming manual investigation begins, investigators finally flag the inappropriate access.

This is a challenge healthcare organizations face every day. Access, activity, and systems can hide meaningful risks, anomalies, utilization gaps, or operational issues in plain sight.

Most organizations already have access to the data that could provide needed context. The hard part is efficiently analyzing that data to uncover insights before issues escalate. This is where AI can make a significant difference, so long as it's equipped to connect the dots.

Making sense of context with AI

Healthcare access is tricky to analyze because “normal” depends on the situation.

Privacy analysts, IT admins, and clinicians across various departments will all use systems differently. A workstation or workflow that is routine for one team may be unusual for another. A login that looks harmless by itself may look different when viewed alongside peer behavior, historical patterns, and surrounding activity.

That is why healthcare organizations are increasingly turning to AI enriched with contextual data and purpose-built analytics to evaluate identity and access activity. By analyzing large volumes of activity across users, devices, systems, and workflows, AI can help surface insights that would be difficult to identify manually.

But AI needs the right tools to connect related activity, compare behavior against meaningful baselines, and distinguish routine events from those that deserve closer attention. Behavioral communities, probabilistic record linkage, workstation clustering, anomaly detection, and Identity Threat Detection and Response (ITDR) all contribute to that broader picture, providing unique forms of context, analysis, and response.

Together, these capabilities help healthcare teams move from isolated events to a clearer understanding of what activity means and whether it requires action. We’ll explore each of them below.

Building context through behavioral communities

The same action can mean different things depending on who performs it. Even users with similar job titles may have different patterns depending on department, shift, location, and workflow.

Behavioral communities help add that peer context.

Instead of comparing every user and entity to one broad baseline, Imprivata groups them with peers that have similar responsibilities and access patterns. This helps answer a more useful question: is this activity unusual for someone like this user, doing this kind of work?

In the opening example, a nurse accessing an adult patient record may not be meaningful on its own. But if similar pediatric nurses rarely access adult patient records, that peer context matters. It helps separate behavior that is merely different from behavior that may require review. This context improves sensitivity and specificity, while minimizing false positives.

Connecting activity through probabilistic record linkage

The clues that explain an event are often spread across multiple systems and sessions.

A user may badge into a workstation, move through an application, then switch to a mobile device as they continue their work, generating activity across multiple systems or sessions in a short time span. If those events remain disconnected, investigators only see fragments.

Probabilistic record linkage helps connect those fragments into a single, more coherent workflow.

In simple terms, it links related activity even when traditional identifiers do not line up perfectly. Instead of relying on one exact match, it weighs multiple signals to determine which events likely belong together.

That matters because sequence provides context. Linking related activity helps teams understand what happened before and after an event, revealing a fuller picture of the workflow and enabling more accurate behavioral baselines.

Adding workstation context through clustering

Shared workstations are a fact of life in healthcare. They also create a unique challenge for access security.

In many clinical environments, users move between workstations throughout the day. This makes risk analysis more challenging, as most generalized risk tools assume users access a relatively small set of familiar, user-specific devices. Shared workstations do not fit that model, but they still generate meaningful patterns. Certain shared workstations may be commonly used by specific departments, care areas, and users while being unusual for others, even when the login and authentication are valid.

Workstation clustering helps identify those patterns. It groups workstations, automated dispensing cabinets (ADCs), and other shared access points based on historical usage patterns, including overlapping user activity, department patterns, location data, and other environmental signals. It then evaluates how likely a particular user is to use a particular workstation. This adds device-context affinity to access decisions, making it easier to understand whether a user-workstation pairing is expected or unusual.

When applied to login and access analysis, workstation clustering creates practical value for healthcare organizations. It gives security and IT teams another signal to understand usage patterns, support stronger authentication, and enhance visibility into unusual access events.

Turning context into anomaly detection

Peer patterns, connected workflows, and workstation context all help answer the same question: does this activity fit?

Effective AI-powered anomaly detection builds on this context by evaluating a broader set of behavioral and contextual factors tied to access activity, including frequency metrics, patient interaction patterns, search behavior, workflow variance, patient location variance, time since encounter, and other indicators. It then correlates all the data to identify activity that meaningfully deviates from expected behavior and could require review.

But the end result is more than a list of unusual events. By weighing those factors together, anomaly detection produces an overall risk score that helps teams prioritize review and focus attention on activity most likely to require follow-up.

Just as important, the score is explainable. Imprivata anomaly detection surfaces the behavioral factors that influenced the score, helping investigators understand why an event was flagged and giving them a clearer starting point for review.

From detection to response with ITDR

Prioritizing unusual activity helps teams focus their investigations. But some identity risks require real-time evaluation and response. That's where Identity Threat Detection and Response (ITDR) comes in.

ITDR detects identity attacks in real time to help prevent account compromise and remediate threats. It combines threat detection with adaptive authentication and response, helping organizations protect identities while preserving seamless access for trusted users.

In practice, ITDR helps evaluate whether an access attempt or identity-related event should continue as normal, require additional authentication, or trigger a stronger response. The decision can be informed by signals such as suspicious IP activity, impossible travel, unusual device use, session sharing, breached credentials, workstation affinity, or other indicators of identity risk.

That makes context actionable closer to the point of access. Trusted activity can remain low-friction. Higher-risk activity can receive additional scrutiny and safeguards.

From context to confidence

In healthcare, access activity is rarely simple. A valid login, familiar workflow, or active workstation may require a closer look when viewed in context.

AI-powered contextual analysis helps teams connect signals across users, devices, systems, and workflows. By bringing together the various technologies discussed above, AI can help analyze vast amounts of access and activity data at a scale that would be difficult to achieve manually. That can support faster investigations, smarter prioritization, stronger identity protection, and better visibility into activity or device patterns that may otherwise go unnoticed.

The result is a clearer way to understand what happened, why it matters, and whether action is needed, giving healthcare teams greater confidence in where to focus and what to do next.

Learn more about Imprivata’s approach to AI in our Artificial Intelligence at Imprivata whitepaper.