The 3 Layers of Healthcare Security Intelligence in Cynerio NDR-H

Healthcare networks are buzzing with thousands of device-to-device communication sessions every minute. But beneath that raw traffic lies critical context: What devices are talking? How do they behave? And most importantly: what happens when something doesn’t look right?

Each device in a hospital, from infusion pumps to PACS servers, has a unique communication profile, protocol set, and operational pattern. Distinguishing benign behavior from malicious activity takes serious intelligence. That’s where Cynerio NDR-H comes in.

Why Layered Intelligence Matters

Cynerio’s Network Detection and Response (NDR-H) solution sifts through complex, noisy traffic to spotlight only the most critical security incidents on healthcare networks. Our three-layered approach to intelligence cuts through the chaos and delivers clarity fast, giving hospitals a clearer picture of what really matters.

Each layer brings its own distinctive value to our solution, working together with and strengthening the others to create a robust security framework based on defense-in-depth.

Layer 1: Human Intelligence from the CynerioLive Team

Our in-house CynerioLive team of security analysts forms the foundation. They: 

• Research the latest attack vectors
• Define behavioral markers for IT, OT, IoT and IoMT devices 
• Flag anomalies in network behavior 
• Provide expert guidance to help users separate real threats from false alarms

This human-led layer ensures we’re always tuned in to the most pressing threats in healthcare environments.

Layer 2: Machine Learning for Contextual Insight

Next comes ML-powered behavior profiling.

Using deep domain knowledge, we model “normal” communication patterns for every type of hospital device. This lets us automate questions like:

• Is this protocol unusual for this device?
• Should this server be talking to that IP address?
• Does this series of network events correlated together resemble previously observed attack patterns?

These profiles provide the individual context to automate the evaluation of any suspicious sessions we see, and bring further analysis and precision to our network threat detection.

Layer 3: Generative AI for Narrative Clarity

The final layer brings everything together using GenAI.

Rather than flooding dashboards with fields and flags, our GenAI engine:

• Summarizes incidents across multiple signals (such as failed logins, port scans, config changes, and more)
• Connects events into logical, human-readable stories 
• Prioritizes potential incidents based on risk, device role, and historical patterns

By showing how all these individual events may be part of the same attack, it helps security teams quickly determine whether the pattern reflects harmless background activity or a real threat. It’s not just an alert; it’s an explanation and a recipe for further action.

‍Real-World Example: PACS Server Threat Detection

PACS servers are high-value assets on medical networks, serving as storage systems accessed by multiple users, including some outside the hospital network. But they also store sensitive ePHI and maintain active connections with critical radiology equipment, making them especially attractive targets for attackers.

At a U.S. hospital, we detected multiple failed SMB login attempts to a PACS server, followed by a successful one from an external IP. Was it normal user activity, or a brute-force attack?

Here’s how Cyniero’s three layers of intelligence worked together to unmask the threat:

• Layer 1 (CynerioLive Team): Analyst rules flagged the suspicious SMB login patterns. 
• Layer 2 (ML): Identified inconsistencies like an odd hostname and unfamiliar ASN, despite other signals seeming normal.
• Layer 3 (GenAI): Fused it all into a concise story, assessing the threat’s medical impact and urgency based on the device type, model, similar past incidents, and other relevant context. 

The outcome: a prioritized alert with actionable context ensuring a swift response.

Smarter Together: How Cynerio’s Three Intelligence Layers Reinforce Each Other

Each intelligence layer informs and sharpens the others:

• Analyst rules refine ML triggers and GenAI summaries 
• ML context gives GenAI the precision needed for an effective threat response
• GenAI feedback enhances future analyst playbooks 

It’s a feedback loop that grows more powerful over time, but without losing the human touch of our healthcare security expertise.

From Complexity to Clarity with Cynerio

In healthcare, where every device could impact patient care, security can’t be superficial. Cynerio NDR-H transforms chaotic network data into focused, actionable intelligence using:

• Analyst-driven insights
• Machine learning-based profiles
• Generative AI narrative synthesis

The result is faster threat detection, smarter and more sensitive remediation, and safer hospitals. 

Want to see how layered intelligence can elevate your hospital’s security posture? Contact us today to schedule a personalized demo or learn more about how Cynerio NDR-H delivers faster detection, smarter response, and safer care environments.