Managed Detection and Response (MDR) services play a critical role in cybersecurity. These technologies remotely monitor, detect, and respond to threats, blending threat intelligence with human expertise to hunt down and neutralize potential risks. However, one of the biggest challenges MDRs face is managing the sheer volume and variety of threat intelligence data they receive. This data comes from internal resources and the numerous security technologies their customers use, making it difficult to create a cohesive picture of the threat landscape.
To effectively reduce cyber risks, improve the number of threats detected, and optimize Mean Time to Respond (MTTR), MDRs must first consolidate and analyze this data. The key to achieving this is establishing a single source of truth. By creating a unified data repository such as a data lake, MDRs can enhance their threat detection capabilities and provide more accurate, timely responses to emerging threats.
In this blog post, we'll explore how effective threat hunting is an MDRs’ unique differentiator. We’ll also examine how MDR teams can optimize their threat intelligence by combining various data sources into a security data lake — and analyzing that data at scale.
MDRs vs. EDRs vs. XDRs: What’s the Difference?
Today’s threat actors are increasingly sophisticated. They often use legitimate IT tools to blend into corporate networks — making detection, investigation, and response difficult. These attackers gain access to administrative credentials, allowing them to move laterally within systems, and evading traditional security measures. This technique, known as lateral movement, poses a significant challenge to conventional Endpoint Detection and Response (EDR) systems.
Traditional EDRs focus primarily on endpoint-level data, which might not be sufficient to detect these advanced threats. They are designed to respond to known threats but often fall short when dealing with attackers who use novel techniques or hide within legitimate system processes. This is where Managed Detection and Response (MDR) services come into play. Unlike EDRs, the benefit of MDRs is to look for patterns in telemetry data—such as logs and events—across a wide range of security tools. This broader view allows MDRs to detect anomalies and patterns that might indicate an ongoing attack.
Extended Detection and Response (XDR) takes this a step further by integrating data from across an organization's entire environment, including endpoints, networks, and cloud systems. However, the key difference between XDR and MDR lies in the latter’s focus on proactive threat hunting. MDR services not only monitor and respond to threats but also actively seek out potential risks before they materialize into full-blown incidents.
Key Threat Hunting Challenges for MDRs: It’s All About Data Quality
Threat hunting is crucial for maintaining a proactive security posture. It allows security teams to identify and mitigate threats before they cause significant damage. Proactive threat hunting can also enable security experts to track down advanced persistent threats (APTs) that linger in a network over time, by looking for patterns in typical IT telemetry data like logs and events.
Without the ability to analyze data feeds at scale, an MDR's customer might fail to detect these stealthy attacks until it's too late. The importance of having a robust, scalable data management strategy becomes clear when we examine real-world examples, such as that of Blackpoint Cyber.
Blackpoint Cyber, a leading MDR provider, faced significant challenges in managing and analyzing their threat intelligence data. As their customer base grew, so did the volume of data they needed to process. They were receiving data in a variety of formats from numerous sources, which made it difficult to integrate and analyze this information effectively. The cost of integrating and analyzing this data at scale was also a major concern.
Blackpoint Cyber needed a solution that could unify their data, making it easier to manage and analyze. They turned to ChaosSearch, a log analytics platform that enables users to search and analyze data directly from Amazon S3, without the need for complex data transformations or costly infrastructure.
ChaosSearch helped Blackpoint Cyber create a more efficient, cost-effective data model. By leveraging ChaosSearch, Blackpoint was able to normalize their data into a standard format, which made it easier to analyze at scale. This gave them a significant competitive advantage, allowing them to offer their customers faster, more accurate threat detection and incident response services.
The Advantages of Using a Data Lake for Threat Intelligence
Some of the key challenges MDRs face in managing threat intelligence data are:
- Data in a Variety of Formats: MDRs receive data from a wide range of sources, including logs, events, and telemetry data from various security technologies. This data is often in different formats, making it difficult to integrate and analyze.
- Cost of Integrating and Analyzing Data at Scale: As the volume of data grows, so does the cost of storing, processing, and analyzing it. Traditional data management solutions can be expensive and complex, especially when dealing with large-scale data sets.
One effective approach to overcoming these challenges is to use a security data lake. A security data lake is a centralized repository that allows MDRs to store, manage, and analyze their threat intelligence data in one place. This approach offers several key advantages:
- Data Normalization: A security data lake can normalize data into a standard format, making it easier to integrate and analyze. For example, Amazon Security Lake is a popular solution that automatically normalizes security data into the Open Cybersecurity Schema Framework (OCSF), a standardized data format that makes it easier to analyze data across different tools.
- Scalability: Security data lakes are designed to handle large-scale data sets, making them ideal for MDRs that need to analyze massive amounts of data. This scalability allows MDRs to detect and respond to threats more quickly and efficiently.
- Cost-Efficiency: By storing data in a centralized data lake, MDRs can reduce the costs associated with data management. Security data lakes allow organizations to store data in a cost-effective manner, especially when used alongside complementary tools like ChaosSearch for advanced data analysis and threat hunting capabilities.
For many MDRs, building a data lake using Amazon S3 and tools like ChaosSearch may be the most cost-effective and convenient option. Unlike traditional data lakes that require data to be transformed into a specific format, ChaosSearch allows MDRs to analyze data in any format. This flexibility makes it easier to integrate data from a variety of sources, without the need for complex data transformations. In addition, leveraging the scalability of Amazon S3, allows MDRs to store and analyze large amounts of data at a lower cost. This makes it easier for MDRs to scale their operations as their data needs grow.
Building an Elite Data Model is a Competitive Advantage for MDRs
The ability to analyze data at scale and at speed can mean the difference between detecting a threat and having it go unnoticed. As cyber threats become more advanced, MDRs must be able to quickly identify and respond to these risks in order to protect their customers. That’s why it’s important to create a more efficient and cost-effective data model. Doing so enables MDRs to offer their customers faster, more accurate threat detection and response services, giving them a significant competitive advantage in the market.
The modern threat environment demands a solution that can handle large volumes of data from a variety of sources, without sacrificing performance or increasing costs. A security data lake combined with a log analytics solution like ChaosSearch can help MDRs overcome these challenges. By creating a unified data repository and leveraging advanced analytics tools, MDRs can enhance their threat detection capabilities, reduce costs, and gain a competitive edge. This approach not only improves the efficiency and effectiveness of MDR services but also helps ensure that their customers are protected against the latest cyber threats.
