Security teams are constantly challenged to identify security incidents that truly matter and respond to them before they become a serious threat to business operations. The proliferation of data only complicates matters.
Many security operations centers (SOCs) are finding themselves overwhelmed by telemetry data to correlate, a proliferation of tools, expanding attack surfaces that are challenging to monitor and secure, and data silos across security and IT products, security information and event management (SIEM) systems, enterprise data, and threat intelligence.
The complexity and cost of SIEM solutions and the number of resources that security consumes can easily swallow a large portion of an enterprise’s budget, causing many organizations to fall behind in the security data race. Traditional SIEMs were not designed to handle the explosive growth of data, and cannot provide the insights and automation that modern security teams need.
Security data lakes can reduce organizations’ reliance on SIEM solutions. They support structured, semi-structured and unstructured security data, and can consolidate an organization’s entire security telemetry into a single source of truth. You can also include expanded data, providing a richer security correlation without SIEM costs overruns.
Why should an organization use a security data lake?
- Security data is a big deal—and a big challenge. In addition to wreaking havoc on budgets, it is becoming a large-scale data analytics issue. Log data volumes are growing exponentially, along with the complexity of threat detection. Meanwhile, the cost, complexity and resources that SIEMs consume have gotten out of hand. Enterprises need a solution that can scale in the data cloud to enable more secure data collection and the fastest possible searches to identify threats more quickly. Security data lakes, leveraging the power of cloud computing and scale, help organizations move beyond SIEMs by enabling them to scale compute resources up or down as requirements change.
- Traditional SIEMs only support limited query languages, preventing teams from joining various data sources or using commonly known languages to investigate logs. Security data lakes allow teams to access all log sources—including server and event logs, firewalls, SaaS applications, network traffic and more—through an ETL process. This reduces data silos and helps cover the entire attack surface. It also eliminates the need to manually collect and parse logs, enabling SOCs to better respond to incidents and hunt for threats.
- Many security data lake platforms enable users to perform advanced analytics, joining security logs and enterprise data to help teams detect and respond to sophisticated attacks. Users can also automate triage, eliminating hundreds or even thousands of security incident alerts daily. Leveraging this automation, an organization can detect and respond to real threats faster, reduce complexity, and accelerate how it detects, investigates and responds to threats.
Some organizations may never cross the finish line to securing all data, but using security data lakes as lighter-weight “running shoes” can help them move ahead of the pack, mitigate real threats faster, and reduce overall security risk and spend.
Sirius, a CDW company, offers a security data lake solution powered by the Hunters extended detection and response (XDR) platform and Snowflake’s platform. By adding Hunters XDR capabilities to a Snowflake-powered security data lake, you can consolidate all security data sources regardless of vendor, leveraging out-of-the-box, intelligent analytics and automated threat detection that deliver important insights for your SOC. This approach allows our clients to correlate large data volumes in Snowflake and overcome issues with complexity and false positives—something that a traditional SIEM approach may not have allowed due to cost or performance constraints.
To learn more about the Hunters + Snowflake security data lake solution, speak to your Sirius representative or contact us today.