Why Legacy SIEM Fails with Large Data Sets
Security information and event management (SIEM) has taken a leading role in threat detection and response. With potential liabilities resulting from ransomware attacks, loss of customer data, and disruptions to daily operations, organizations are continually seeking ways to improve their threat detection and response program. Using modern approaches offers a significant competitive advantage over legacy solutions that haven’t kept pace with today’s rapidly evolving cybersecurity landscape. Let’s look at the limitations of legacy SIEMs and how cloud SIEMs powered by a security data lake have evolved to meet the demands of the modern organization.
The Evolution of SIEM
Historically, SIEM was primarily used to handle log management details generated by monitored environments. SIEM systems flagged security events on specific machines while aggregating information from across the entire network to create a holistic view across the whole system. But modern iterations play an expanded role, including real-time monitoring and analysis of events and tracking and analyzing security data. Artificial intelligence has automated many previously manual processes, allowing security experts to focus their energies on more-advanced tasks. In addition, machine learning algorithms power advanced user and entity behavior analytics (UEBA) capabilities, helping organizations keep pace with the ever-changing threat environment.
Limitations of Traditional SIEMs
Legacy SIEMs were designed for a different time and place in the history of cybersecurity. These monolithic systems were originally deployed to protect on-premises networks from outside attackers. These SIEM systems were used primarily as log storage, making their continued use today much less effective and more costly than modern equivalents. Although these rules-based systems worked well at detecting threats that had already been identified and cataloged, they did little to proactively identify and engage new ones.
Scalability constraints and expense
As more businesses move to the cloud and engage in digital transformation, the legacy SIEM architecture fails to keep up with the complexity and volume of data storage to achieve complete visibility. As a result, security teams face restrictive limits on which data they can collect from security sources and how long they can keep it available. Additionally, legacy SIEM pricing is based on the volume of data ingested, so teams are forced to ingest only a small subset of their data for threat detection and response. This means data breaches are more likely to occur, and last longer, with more harm to customers and the business.
Legacy SIEMs tend to have slower query performance because they do not separate compute from storage. Today’s organizations need dedicated compute resources for every user and workload in order to avoid bottlenecks. They must be able to provision compute clusters ranging in size from extra small to 6XL to match demand and choose multi-cluster computing resources for near-unlimited concurrency.
Next-Gen SIEMs Embrace Security Data Lakes
Next-gen SIEMs address the limitations above by sitting on top of a security data lake, which eliminates security data silos by removing limits on ingest and retention. With a security data lake, organizations can scale resources up and down automatically and only pay for the resources they use.
Additionally, security data lakes enable analysts to apply complex detection logic and security policies to log data and security tool output. Security analysts can also quickly join security logs with contextual data sets such as asset inventory, user details, configuration details, and other information to eliminate would-be false positives and identify stealthy threats. The full power of data analytics, using universal languages such as SQL and Python, enables automation of otherwise manual security analyst tasks in areas such as evidence gathering and investigations.
Snowflake for Cybersecurity
Snowflake provides a security data lake that enables better security analytics. Snowflake enables near-infinite amounts of data to be stored and leveraged for advanced threat detection, threat investigations, and reporting and compliance purposes. Organizations choose Snowflake for cybersecurity because implementing a security data lake powered by a cloud data platform will allow teams to index and query all data types while experiencing fast compute speeds at near-unlimited scale. Snowflake supports connected applications, where leading security vendors provide powerful analytics and custom detection rules to enable better, faster, more cost-effective security analytics.
See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.