Zero to SIEM in Seconds Part 1: Operationalize in Seconds

Operationalize in Seconds is the first in a series of blog posts focused on what is required to gain the maximum efficiency from your SIEM for the purposes of Threat Detection, Investigation and Response (TDIR). The Security Operations Center (SOC) has come to rely on the SIEM as a core part of their monitoring and it is always a race against time to prevent threat actors from accomplishing their ultimate goal. At Gurucul we believe that the right platform can compress the time required by security teams across every part of the SOC lifecycle so they can get ahead of the attack.

Limitations in current SIEM architectures have led to challenges for Security Operations Teams in operationalizing their SIEM as they race to prevent a successful breach. This includes the inability to support hybrid cloud and multi-cloud architectures, being forced to limit the volume of data ingested, and difficult customization in accepting new data sources. Let’s address each one in deploying and operationalizing your SIEM.

Inability to Support Hybrid Cloud and Multi-Cloud Architectures

Most traditional SIEMs were architected for on-premises networks. They have of course been upgraded to handle cloud environments or virtualized to more easily support cloud deployments but were never architected to be cloud-native. Even cloud-native platforms struggle in hybrid situations or were not always designed to handle multi-cloud and regional cloud centers with applications and data spread throughout. This leads to a lack of consistent visibility and often produces visibility gaps for SIEMs leaving them unable to correlate or analyze event data across these environments.

Threat actors are very aware of these limitations and often spread attacks across multiple cloud providers, or on-premises and cloud infrastructure as a way to hide from SIEM and even XDR solutions.

Therefore, it is critical to pick and validate cloud-native SIEM solutions fully capable of not only collecting data across hybrid-cloud architectures but can also correlate and analyze threats hidden across multi-cloud deployments.

Being Forced to Limit Volume of Data Ingested

The traditional school of thought has been that SOC teams should not try to feed the SIEM every log and data source from all the business infrastructure. There have been two reasons for this:

  1. The more data you feed into your SIEM, the more alerts you create leading to an increased number of false positives.
  2. The cost of your SIEM dramatically increases over time, often unpredictably. This is because most SIEMs charge based on the amount of data ingested and collected. This equates to customers getting penalized the more they want to protect their organization. Adding additional security analytics such as User and Entity Behavior Analytics (UEBA) or Network Traffic Analysis (NTA) simply exacerbates the problem with more alerts.

The result is that security teams suffer serious burnout, not to mention the burying of a real attack campaign potentially getting missed altogether.

Customers need to look at next generation SIEM solutions with trained machine learning analytical capabilities to collect and analyze data from as many sources as possible without penalizing organizations with higher licensing costs.

Difficult Customization in Accepting New Data Sources

Related to the previous challenge is that most SIEMs require a great deal of customization for pulling in data from new sources, custom applications, and even other security tools. The goal is for the SIEM to interpret these events and use this data with its correlation rules. This requires the security team to manually build data “parsers” – if they have the skills or time – or outsource them to the vendor. Large organizations will often demand a new parser from the SIEM vendor, but that gets put on a wait list of months or the SIEM vendor passes the buck to whatever solution is sending data to the SIEM. This can also take a long time to test and develop if not already built.

This leaves the SIEM with incomplete visibility that slows down attack detection, requires more manual investigation and context gathering efforts, which also leads slowing down response.

Organizations need a SIEM that can readily interpret any data source, extra the security-relevant event and metadata, and automatically adapt that to out-of-the-box threat models to improve time-to-value for the SOC team, especially as new or even unknown devices or applications are added to the organization.

Operationalize in Seconds with Gurucul Next-Gen SIEM

Gurucul Next Generation SIEM overcomes these operational issues with advanced architecture and deployment options. Gurucul Next-Gen SIEM works in complex hybrid-cloud and geographically dispersed locations, automatically ingests and interprets any data source, scales predictably, and lowers your overall licensing, operational and storage costs.

To learn more about Gurucul Next-Gen SIEM, please contact us for a discovery call and demo.


About The Author

Sanjay RajaSanjay Raja, VP Product Marketing and Solutions, Gurucul

Sanjay brings over 20 years of experience in building, marketing and selling cyber security and networking solutions to enterprises, medium-to-small business, and managed service providers. Previously, Sanjay was VP of Marketing at Prevailion, a cyber intelligence startup. Sanjay has also several successful leadership roles in Marketing, Product Strategy, Alliances and Engineering at Digital Defense (acquired by Help Systems), Lumeta (acquired by Firemon), RSA (Netwitness), Cisco Systems, HP Enterprise Security, Crossbeam Systems, Arbor Networks, Top Layer Networks, Caw Networks (acquired by Spirent Communications), Nexsi Systems, 3Com, and Cabletron Systems. Sanjay holds a B.S.EE and an MBA from Worcester Polytechnic Institute. Sanjay is also a CISSP as well as Pragmatic Marketing certified.