Advancing Cybersecurity with Data Lakes
06.08.2021
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As companies generate an ever-increasing amount of data, security information and event management (SIEM) becomes increasingly challenging. Cybersecurity professionals have more to manage, and as cybercrime rises, there is less time to do so. While big data poses challenges for security teams, it also presents an opportunity.
As of 2019, 52.5% of organizations worldwide have been using big data, with another 38% planning to do so in the future. That means companies have a rapidly growing store of information at their disposal: information that can help improve incident response. You can capitalize on this wealth of information with a cybersecurity data lake.
Data lakes developed from lessons learned from dealing with an influx of data. Warehouses, which require preprocessing before storage, create bottlenecks and require considerable computing power when working with big data. Consequently, 60% to 80% of data scientists’ time went to managing infrastructure frictions.
Unlike data warehouses, data lakes can store unstructured and semi-structured data, supporting faster processing times. Similarly, they don’t apply a schema to the data until you pull it, enabling multiple use cases for the same data. These benefits, while initially aimed at data scientists, are relevant to security teams, too.
A security data lake provides a centralized location for all relevant network and endpoint logs. It also parses this data for relevant information and enriches it with additional contextual information. Many include search and analytics tools as well, letting you find and capitalize on relevant security data even faster.
Cybersecurity data lakes provide the scale and storage enterprise security teams need for advanced SIEM. Here’s how they drive security improvements.
Cyber data lakes let you capitalize on big data to manage company networks better and prevent data breaches. As of 2018, 81% of surveyed organizations have managed at least 1 billion files. All of this data can be a valuable asset in understanding past and future security threats, and security data lakes provide the means to harness it.
With access to such vast amounts of data, you could identify threat patterns over extended periods. These insights would then help you develop more effective SIEM strategies to defend against similar events in the future. You may also discover long-standing vulnerabilities that previously passed under the radar.
Since cybersecurity data lakes enrich data with other relevant information, they make even unstructured data actionable. This enrichment lets you better understand past or current alerts in context, informing more appropriate responses.
Without a data lake, SIEM is a challenging undertaking primarily because big data can slow incident response times. Collecting logs from multiple systems before combing through them takes time in which a cybercriminal could compromise valuable data. Security data lakes streamline this process by consolidating all of the relevant data.
Many organizations have billions of security-related logs each day, which would quickly create bottlenecks in a data warehouse. A data lake is a better solution since it doesn’t apply any schema to the data until reading it. A dedicated cybersecurity data lake is even better, as it provides a single location for all relevant security data, free of unrelated information that could muddy the waters.
Creating a cyber data lake is an important step in unleashing the full potential of your SIEM. When the time comes to build your data lake, there are a few considerations to keep in mind.
First, your cybersecurity data lake should have an automated collection process. Often, that involves using an API call or protocols like Syslog. Remember to include a parser library, too, to enable automatic data parsing.
It’s also crucial to include contextual information in your data lake, as this facilitates a more accurate incident response. For example, users’ roles are a critical piece of context, as some employees accessing some systems may not be suspicious while others are. Time series data alone is insufficient for effective SIEM.
Search and reporting features are another critical feature for security data lakes. These will help improve response times, so the easier it is to search for relevant information, the better.
Finally, security data lakes must be scalable. You must be able to store vast amounts of newly generated data while retaining older data to analyze historical trends. Local regulations may also require you to store information for a given period, and you want to leave yourself room to grow.
Security data lakes are helpful resources for improving enterprise security, but building one is only half the equation. Once you’ve established your data lake, an advanced SIEM tool can help you make the most of it. With this service at your side, you can use your data lake to its fullest potential.
Instead of responding to individual alerts manually, by using Logsign SIEM, you can reduce the incident response efforts and, by doing so, you will ease the burden on your incident response team.
You can start data ingestion as soon as you deploy your product. It is possible to create your own data lake that classifies, normalizes and enriches the data for effective use. Besides, the Logsign SIEM solution provides limitless data collection from every source and environment and performs real-time data enrichment with real-time Threat-Intelligence.
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