The very nature of data is its infinite capacity for growth.
For security teams at large, highly integrated and complex enterprises like financial services institutions, that growth can quickly become unwieldy when the approach is to store, normalize and prepare all of this data in order to extract value.
The result is a virtually endless cycle of data overload for SOC and NOC engineers and analysts paired with privacy regulations like GDPR and CCPA.
But there is a solution.
In our newest whitepaper, “The Data Overload Problem in Cybersecurity,” we dive into the data overload problem plaguing the cybersecurity industry and uncover how organizations can greatly reduce or even completely eliminate many of these challenges by adopting an AI-driven solution to analyze network behavior in the context of current data while meeting compliance and regulatory requirements.
In this paper, we analyze:
- Why Data Overload Happens and Why It Is a Problem
- How Data Normalization Impacts Regulatory Compliance
- How Data Overload Impacts Security Outcomes
- Why Data Normalization Is Expensive
- MixMode AI First Security Solves the Data Overload Problem
- MixMode Versus Typical Security Solutions