In today’s dynamic world of distributed computing and cloud-scale systems, traditional security data platforms and tools such as SIEM typically fall short of actually delivering the intelligence needed to better adapt to the rapidly changing threat landscape. This is primarily due to a lack of core data lifecycle management, analytics, and integration capabilities. In addition to closing these functional gaps, security organizations could benefit by making AI/ML-driven advanced analytics a core component of their security intelligence capabilities. While there is admittedly a lot of hype around the concept of a “security data lake” in the industry, most approaches to date have not really delivered the type of usable intelligence needed to be as nimble as we must be in today’s cybersecurity world.

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