Extended detection and response (XDR) telemetry refers to the collection of data from security layers and solutions, including those for email, endpoints, servers, cloud workloads, and networks. An XDR platform consolidates and checks activity insights to detect and hunt for threats, known and unknown, and assist in root cause analysis.
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Understanding XDR telemetry
XDR telemetry empowers security operations center (SOC) teams with actionable insights, consolidated from real-time data across your digital environment. It’s a mission-critical component of attack surface reduction and proactive risk management, helping SOCs aggregate and correlate data to visualize, prioritize, and mitigate threats. This also enables you to break down data silos, ensuring faster, more informed risk identification and response measures.
Over time, XDR telemetry has evolved from simple event logging to sophisticated, real-time analytics. Early approaches gathered basic alert insights, but this is not enough to stay ahead of threat actors and truly take control of risk. Telemetry has since expanded to factor in behavioral data, traffic patterns, changes to configurations, and even encrypted fingerprinting for Transport Layer Security (TLS) clients via JA3.
Artificial intelligence (AI), machine learning (ML), and automation play essential roles in any proactive security strategy leveraging XDR telemetry, equipping SOC teams with ample context for faster response.
Telemetry types by security layer
Cybersecurity platforms, solutions, and capabilities collect data on a variety of events. These vary in quantity and complexity depending on the organization in question, so having a personalized, centralized platform helps to parse and prioritize them quickly. By consolidating XDR telemetry and pulling it into a secure “hub” for streamlined access, SOC teams don’t have to put up with siloed point solutions. This enables them to organize and address security events in order of urgency, mitigating alert overload and reducing resource strain.
Event data comprises everything from user-accessed file information to registry modifications on devices. Some categorized examples include:
Network events
- Traffic flow patterns
- Perimeter and lateral connections made
- Suspicious traffic behavior
- Fingerprinting for TLS, formerly Secure Sockets Layer (SSL), via JA3
Cloud workloads
- Configuration changes
- New or changed instances
- User account activity
- Processes
- Executed commands
- Network connections
- Files created or accessed
- Registry modifications
- Message metadata for external and internal email
- Attachment metadata
- External links
- User activity, such as logins
Endpoints
- Processes
- Executed commands
- Network connections
- Files created or accessed
- Registry modifications
The role of telemetry in XDR performance
What differentiates XDR platforms is the type of data collected and how it is used.
An XDR platform built primarily on its own native security stack has the advantage of a deeper understanding of the data. This enables the platform to collect precisely what is needed to optimize analytical models for correlated detection, in-depth investigation, and threat hunting.
Vendors who primarily pull data from third-party products tend to lack sufficient context, getting only a surface-level glimpse. They may be missing the type and depth of telemetry needed to fully understand the risks and threats organizations face. While telemetry, metadata, and NetFlow are commonly used, alert data alone can lack the activity details needed for effective analytics and insights. Understanding the structure and storage of telemetry is crucial, as it influences how data is captured, queried, and used for activity insights.
Using network data as an example, a graph database can be highly efficient, while for endpoint data, an open search and analytics engine like Elasticsearch is well suited. Having various data lake structures in place can help with detection, correlation, and search -- but only if they can “speak” to one another through a centralized XDR platform.
Key features and benefits of effective XDR telemetry
Comprehensive data insights from your entire environment
XDR platforms gather telemetry from across your full digital environment, providing SOC teams with a clear, holistic, and complete view of security events. As touched on earlier, modern XDR telemetry extends beyond traditional logs and alerts to include activity data, network flows, process executions, and behavioral indicators.
Automated data correlation and analytics
Advanced detection models analyze and correlate disparate data sources in real time. This helps you spot attack patterns, suspicious behavior, and other risk elements across all security layers. By leveraging these insights, you can have confidence in your response actions, prioritized and informed by the latest data analytics.
Interoperability and flexible architecture
The most effective and proactive approaches to XDR telemetry seamlessly integrate with established databases, data lakes, and analytics engines. They also support efficient and streamlined storage, querying, and analyses tailored to different types of activity data.
AI and ML integration
Modern XDR leverages the power of AI and ML to analyze telemetry streams in real time. Models continually adapt to the latest threats, attack methods, and risk types to help you not just keep pace with adversaries but see their next moves. Predictive analytics help with detecting suspicious behaviors, and addressing them, before breaches occur. In other words, nothing is left to chance. These powerful integrated technologies are always learning from and adapting.
XDR telemetry vs. SIEM alerts
Traditional security information and event management (SIEM) is effective at aggregating logs and alerts, but it isn’t as efficient when connecting multiple alerts tied to the same incident. This requires an analysis at the root telemetry level across security layers.
Leveraging telemetry, XDR alerts consider alert information as well as other critical activities designed to identify suspicious or malicious activity. For example, Microsoft PowerShell activity on its own may not result in a SIEM alert, but XDR is able to assess and correlate activities across several security layers, including the endpoint.
By running detection models on the collected telemetry, an XDR platform can identify and send fewer, higher-confidence alerts to the SIEM, reducing the amount of triage required by security analysts.
How does XDR telemetry work with AI and ML?
AI-powered XDR platforms can analyze vast streams of telemetry data in real time, discerning subtle patterns and anomalies that traditional methods might overlook. This capacity for rapid pattern recognition enables more accurate detection of risks and sophisticated threats, including zero-day vulnerabilities and lateral movement within networks.
Meanwhile, ML continually learns from the latest threat insights and adapts to evolving attack tactics. This makes threat identification more precise, helping SOC teams reduce false positives and focus on what needs their most urgent attention, without being overloaded or overwhelmed. By automating the correlation of data across endpoints, networks, and cloud environments, this combination of AI and ML is the proactive “one-two punch” needed to eliminate bottlenecks, streamline security operations, and accelerate incident response.
What are the challenges of XDR telemetry?
If not consolidated and prioritized correctly, the sheer volume and diversity of telemetry data—collected from endpoints, networks, cloud environments, and applications—can lead to information overload. Security teams may struggle to parse and prioritize this constant influx, creating additional risks such as missed threats, resource strain, or alert fatigue. Integrating telemetry across disparate systems also demands robust interoperability, yet technical silos and proprietary formats often hamper seamless aggregation.
Getting consistent access to accurate, real-time, relevant data is another challenge. Incomplete or inconsistent telemetry can undermine detection models, yielding false positives or causing your SOC team to overlook hidden or subtle attack patterns. There’s also a need to maintain privacy and security compliance, particularly in environments such as government, finance, and healthcare.
Additionally, the effective deployment of AI-driven analytics within XDR platforms relies on well-trained models, which can be stymied by evolving threats and adversarial techniques. Lastly, the operational burden of maintaining, tuning, and scaling XDR solutions can strain resources, particularly for organizations with limited expertise or budget.
What is the Trend Micro approach to XDR telemetry?
The Trend approach to XDR telemetry is built for the next generation of SOC. By integrating telemetry from endpoints, networks, emails, and cloud workloads, it correlates data across disparate security layers, turning your data silos into actionable insights while accelerating response. Powerful agentic AI, ML, and automation technologies enable you to detect risks in real time, including zero-day vulnerabilities and lateral movements that traditional tools might miss. Automated correlation and contextual risk assessment prioritize the most critical threats, empowering security teams to respond faster and more accurately. This approach also helps you prioritize interoperability, ensuring smooth integration with diverse environments and third-party solutions.
Joe Lee is Vice President of Product Management at Trend Micro, where he leads global strategy and product development for enterprise email and network security solutions.
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