XDR gathers signals from across the whole of today’s cloud-native and hybrid architectures. It normalizes, enriches and contextualizes this data, initiating automated responses in seconds for high fidelity security detections. Powerful machine learning (ML) models are applied to XDR platforms to provide human experts with the right information at the right time. SOC analysts and threat hunters are empowered to hunt, contain and respond to attacks exponentially faster – with less fatigue and frustration, when an automated response is not possible. XDR also delivers reliable insights to accelerate investigation analysis and streamline risk reporting.
Here are 6 ways XDR’s is more secure than MDR:
1. XDR ingests multiple signal sources.
What makes XDR powerful is that it’s able to gather and normalize data from across the enitre environment. This enables high-fidelity detection because it gives security teams true and comprehensive visibility from endpoint to cloud and beyond. Ideally, there should be no limits on what the security team can see or how much information can be incorporated into analyses. This means included technologies shouldn’t be limited to a single vendor’s product portfolio or solution suite.
2. Intelligent analytics eliminate noise & greatly reduce false positive rates
In traditional SIEM-centric security architectures, high false positive rates are a perennial problem, as well as the primary contributor to burnout among security analysts. Excessive noise can also lead to alert fatigue, which can ultimately result in failures to detect if analysts end up dismissing alerts because they simply don’t have enough time to investigate all events. In XDR, machine learning (ML) models and artificial intelligence (AI) algorithms aid analysts in recognizing patterns. The technology does so by automatically bringing in contextual data and taking investigative steps that a human would otherwise have to take. The end result is time savings and far fewer false positives.
3. Enriched data and contextual information enables threat hunting
Because multiple different types of signals are ingested by the XDR platform, it’s possible to see relationships within this rich data when it’s the object of human investigation in threat hunting. If there’s evidence of attack techniques that were used in the past, of relationships between the various parts of an attack sequence, or of activity patterns that are clearly malicious, this becomes readily apparent to security researchers. When the models have high confidence, automated response actions can be initiated.
4. Automated response capabilities dramatically accelerate threat containment
When an XDR platform incorporates automated response capabilities, it’s possible to initiate containment activities in mere seconds if there’s a high degree of confidence that an observed activity is risky or malicious. A top-performing XDR platform that leverages proprietary decision-making technology to facilitate automated disruptions can execute effective, safe and appropriate containment protocols whenever there’s clear evidence that they’re warranted, reducing threat actor dwell time.
5. XDR platforms can learn from current threat intelligence, observed investigations and response actions taken across the platform.
Top-performing XDR platforms can make use of large volumes of data on current and emerging threats to improve detection accuracy. In particular, an XDR platform that sees detections, investigations and response actions across a large number of customer environments will be able to learn from
that information. It can generalize from those learnings to the benefit of all customers. The investigation steps learned in one customer’s environment can be automated in another’s, and response and containment activities that were successful in one environment can be extended to all customers. It’s a rapid feedback cycle that’s constantly improving and hardening the security postures of the provider’s global customer base.
6. XDR supplies proactive security that scales.
In traditional security architectures built around the capabilities of a SIEM, each additional signal source that the security team adds has the potential to increase the false positive rate and contribute to security analyst overload. Not so with XDR: increasing the number of signals ingested actually enhances detection fidelity. What’s more, because ingesting more data leads to better-quality investigations and responses, this is an effect that’s amplified when more customers leverage the platform. This network effect is the reason that expanding the size of an MDR provider’s global customer should only improve its capabilities.
