Snyk and Snowflake have allied to make it simpler for cybersecurity teams to analyze the security posture of their IT environments using data hosted in the cloud.

John Bland, head of cybersecurity strategy for Snowflake, said ultimately cybersecurity is a data problem that requires access to a data lake capable of hosting petabytes of data.

Snyk CTO Danny Allan added the integration of Snyk Analytics with a Snowflake cloud platform that is already being used to collect cybersecurity data, making it possible to add the data that Snyk collects from the various processes that application developers use to build applications to the Snowflake cloud platform.

Additionally, Snyk analytics tools will be able to surface threats using data already normalized in the Snowflake cloud, thereby eliminating the need for extract, transform and load (ETL) tools to move data into a central repository, he noted.

That capability will enable cybersecurity teams to eliminate one of the silos that today makes it challenging for organizations to truly assess their overall cybersecurity posture, said Allan.

Snowflake has been making a case for using its data lake to drive cybersecurity analytics for several years now. The alliance with Snyk adds data that software engineering teams typically collect via a DevSecOps workflow as applications are built, deployed and updated. That integration should make it simpler for cybersecurity teams to use that data to identify the various levels of risks that any known vulnerability included in that software might represent to the business.

The Snowflake data lake has in recent times emerged as one of the primary repositories that many organizations are now using to centralize the management of all their business intelligence data. Storing cybersecurity data that might have previously been housed in a separate security information event management (SIEM) platform presents organizations with an opportunity to eliminate another data silo. That centralization effort has also gained additional momentum with the rise of generative artificial intelligence (AI) models that need access to large amounts of training data.

Of course, those data lakes also become primary targets as the amount of data stored in them continues to increase. Cybercriminals have become more adept at, for example, stealing the credentials that end users have been given to either launch a ransomware attack or outright steal data they then sell to any interested party. As such, applying best cybersecurity practices to any centralized data repository is essential.

It’s not clear just how many data lakes any organization might wind up creating, but as they proliferate across the enterprise the volume and variety of data that needs to be protected is also increasing. Not all that data is, of course, of equal value. Cybersecurity teams will need, as always, to prioritize their limited resources to ensure that the organization’s most sensitive data is secured. On the plus side, however, a data lake at the very least makes it a lot easier to determine where the primary copy of that data resides, versus continuing to hope to discover it one day residing in some other repository that is completely unprotected.