3 Keys to a Modern Security Data Architecture Strategy

Introduction

As companies generate more and more information, the security data architecture strategy needs to change. In the past, all of this information was stored in a central Security Information and Event Management (SIEM) system. However, this is no longer feasible due to the sheer volume of data that companies are generating. Instead, businesses should embrace a data lake vision where they store information in its raw form and use solutions to consume that data. In this ebook, we will explore the three key components of a modern security data architecture strategy and show you how to get started. These key components are: - Transitioning from a central SIEM to a data lake vision - Reassessing the process for identifying risks and potential hacks - Leveraging the power of analytics to detect and prevent threats Putting these key components into action will help your company keep pace with the changing landscape of data security.

Transitioning from a central SIEM to a data lake vision

Modern companies produce huge amounts of data as a result of their digital transformation initiatives. This data comes in many different forms, including logs, packets, flows, and metadata. This data is used by security teams to identify risks, potential hacks, and compliance issues. In the past, it was common for companies to store this data in a centralized SIEM (Security Information and Event Management) solution. A SIEM solution collects and stores data in a central location. This data is then processed and analyzed to identify risks and potential hacks. But storing all this information in a central SIEM doesn't work anymore. The volume of data is too big, the variety is too great, and the velocity is too fast. The reality is that normalizing and centralizing all data doesn't scale and is expensive. Transitioning from a central SIEM to a data lake vision is a more effective way to store your data. Data lake solutions store information in its raw form, making it easier and cheaper to store. Instead of a single central location, data is stored in multiple locations, taking advantage of the benefits of distributed storage. There are a few key benefits to using a data lake solution: - Reduced costs: Storing data in its raw form is cheaper than storing processed data. - Increased flexibility: Data can be stored in any format, making it easier to use different types of data. - Improved performance: Data can be processed in parallel, making it faster to access.

Reassessing the process for identifying risks and potential hacks

At a time when companies are generating more data than ever before, security teams are under pressure to find the needle in the haystack. Instead of putting resources into collecting and storing data that may never be used, security teams should focus on identifying the data that is most likely to be useful and then using it to improve their detection capabilities. Transitioning from a central SIEM to a data lake vision is critical for success in today’s security landscape. A data lake can provide the same benefits as a SIEM, but at a fraction of the cost. By storing information where it is easy and cheap to do so, and using solutions that are designed to consume raw data, security teams can free up resources to focus on more important tasks. Additionally, in order to stay competitive security teams need to be more opportunistic in how they onboard and protect new business areas, which is only possible if they have a modern security data architecture strategy in place. The data lake model allows security teams to simplify the process, allowing customers to store raw data close to the emitting service and investing in the necessary connecting capabilities to access the data from there. Simply put, the data lake model is far more agile and scalable than traditional SIEM solutions, factors that are critical to success in today’s business environment.

Leveraging the power of analytics to detect and prevent threats

In the face of this Cambrian explosion of data, successful security teams will refocus on, and invest in, their analytics skills. Data manipulation, short-lived visualization, and iterative machine learning models will become the norm as analysts work to detect threats in real-time. These analytics-driven security teams will also need to be able to quickly pivot their models and respond to changes in data sets, which is only possible if they have rapid access to raw data and the necessary analytics skills and tools. Security analytics platforms that allow users to access and analyze all data, regardless of its source, are critical in this new era of security data architecture. What’s more, these same platforms make it possible to quickly and easily pivot from one data source to another, making it possible to investigate and remediate threats in near-real-time. These solutions give analysts the ability to detect threats that might otherwise go undetected using traditional SIEM tools. While these tools have become increasingly simple to use, they have yet are not widely adopted among security teams. This is where Security analytics platforms that allow users to access and analyze all data, regardless of its source, are critical in this new era of security data architecture.

Connecting the dots

At a time when an agile approach to data is critical for success, Trout Software’s security analytics platform helps companies take a data-centric approach to security. Through a market-leading Security Hub, customers can creates a "control plane" layer, allowing customers to connect all their security tools and infrastructure. Additionally, the Security Hub enables multiple users to collaborate on a single platform and self-document their code. This flexible and agile approach to collaboration drastically decreases the time to deliver use cases or onboard new team members.