C-Suite Leaders: Unveiling the Power of XDR

Understanding Extended Detection and Response (XDR)

Extended Detection and Response (XDR) is a term coined by Nir Zuk at Palo Alto Networks in 2018 to address the challenges posed by siloed approaches to data analysis for security. Traditional security approaches focused on specific areas such as endpoints, networks, or user behavior, often missing crucial context and indicators from other areas that could have identified risks. XDR, on the other hand, analyzes all these focus areas and brings them together into a holistic platform that can understand and analyze all the data involved in a security event.

The Need for XDR in Enterprises

Enterprises frequently face challenges regarding visibility and understanding the significance of security events in their environments. Vendors have historically released focused, siloed products that do not provide the broad coverage necessary for a unified platform that enterprises require. XDR was designed to bridge this gap by connecting information from all sides of an enterprise IT infrastructure. This comprehensive approach allows for a deeper understanding of the entire security operations and IT landscape.

An important aspect of XDR is the incorporation of a machine learning engine to analyze the massive increase in raw data. Machine learning helps verify that only significant events are brought to an analyst’s attention, preventing them from being overwhelmed by unactionable or irrelevant alerts. The “X” in XDR signifies the philosophy of extending detection and response to any and every IT operation, emphasizing the inclusion of diverse data sources in the analysis.

The Importance of XDR in Cybersecurity

The shift from segregated datasets for endpoints, networks, and threats to a single platform that aggregates these and other areas creates a fundamental change in how enterprises can understand their security operations and overall IT landscape. Unifying these datasets into a single view reduces the chances of missing significant events, false positives, false negatives, skill barriers, and manual aggregation and reporting. By leveraging machine learning to analyze these combined datasets, businesses can better handle the evolving landscape of cybercrime, from individual “hacktivists” to cybercrime organizations to nation-state level operators. XDR enables organizations to defend against increasingly complex attacks.

The Market Response to XDR

While XDR has gained recognition and adoption, many vendors are reluctant to fully embrace the concept. Instead, they attempt to pass off their endpoint detection and response (EDR), network detection and response (NDR), or network traffic analysis (NTA) products as XDR. These vendors often redesign their user interfaces to present the information as a “unified single source” without modifying the underlying application to ingest data from all sources. This approach merely displays the siloed data streams in one view, lacking the true integrated functionality of XDR.

There has also been an increase in the emergence of new players focused on achieving in-depth visibility but without comprehensive coverage across all elements of an IT infrastructure. This results in information gaps that limit their ability to present a complete picture.

Furthermore, some vendors release products without automation through machine learning, inundating businesses with alerts that cannot be effectively addressed or providing incomplete data that hinders analysts’ understanding of the entire incident chain. This lack of automation and integration undermines the efficacy of XDR solutions.

Adopting XDR: What to Look For

When adopting XDR, two key aspects must be prioritized and integrated:

Integration of All Data Streams

A successful XDR solution must bring together and correlate all data streams into a single understanding of an event. Siloed data is inadequate for achieving comprehensive security. By aggregating and analyzing data from various sources, including endpoints, networks, and user behavior, organizations gain a unified and more accurate view of potential risks.

Automated Event Severity Determination

An effective XDR solution should automatically determine the severity of an event and whether further investigation is required by an analyst. Machine learning capabilities play a critical role in accurately assessing the significance of security events, reducing the burden on analysts and allowing them to prioritize their attention and resources on the most critical incidents.

Both of these aspects are essential and must work together for organizations to achieve success in their cybersecurity defense programs. By adopting an XDR solution that integrates diverse data streams and incorporates automated event severity determination, businesses can improve their security posture and effectively respond to evolving threats.

About the Author

Zachary Malone is a Systems Engineering manager at Palo Alto Networks’ SE Academy. With over a decade of experience, Zachary is a seasoned security engineer specializing in cyber security, compliance, networking, firewalls, IoT, NGFW, system deployment, and orchestration.