The following is an excerpt from our recently published whitepaper, “The Data Overload Problem in Cybersecurity.” In this whitepaper, 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.
Data Overload Impacts Security Outcomes
Although these compliance standards are strict, they are completely necessary which leads to a major problem for security teams working in finance security teams. The same compliance and regulatory requirements that force these institutions to store massive amounts of data are also contributing to data capture and loss issues, as well.
It becomes costly and time-consuming to maintain a robust network to handle the massive amount of required data captured from countless financial instruments and other documents. Still, financial institutions must be able to reach back in time, sometimes as long ago as a decade or more, to retrieve data for various reasons.
One goal for data normalization is improving network security through a data tagging process so security platforms can find and analyze related network traffic. However, many enterprises store data in a way that prevents true real-time analysis, limiting the effectiveness of expensive, sprawling security “solutions.”
Despite the availability of new technology, companies still have to depend on extracted, aggregated, and normalized historical data to operate. The inherent architecture of legacy solutions diverts focus away from fundamental business problems companies need to address.
Ultimately, large enterprises tend to store far more data than is actually needed. All data and every alert, regardless of severity, is saved on the network, bogging down security platforms and creating a situation where SecOps professionals are spending at least a quarter of their workday hunting false positives.