How Data Normalization in Cybersecurity Impacts Regulatory Compliance

Christian Wiens Director of Marketing

Christian Wiens is Director of Marketing at MixMode. He has 10+ years of experience as a cybersecurity professional. He has his BA from The University of California, Berkeley and resides in Austin, TX.

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 Normalization Impacts Regulatory Compliance

A major factor impacting the data overload problem in organizations like financial enterprises is the need for regulatory compliance.

Complying with privacy regulations requires all organizations to have access to data on demand, wherever it lives on a network. With the unfathomable amount of data managed by most organizations operating in the finance space today, it can become a significant challenge to locate specific data across legacy systems and networks with countless connections online and off.

Often, companies are simply consolidating and storing data solely for compliance reasons as part of their Cybersecurity processes and associated budgetary spend. Costs to manage and store this data keeps rising while the value of the data remains stable or even depreciates.

This poses a major problem for enterprises as data storage requirements continue to grow. Compliance data storage requirements need not be an obligatory component of a security solution.

By separating compliance data storage requirements from real time threat prevention, companies can be more effective in improving their security posture, and save millions in unnecessary, duplicative data aggregation and storage costs.

In financial institutions, enterprises often face even more headaches and complexities when it comes to data overload. For example, the actual nature of the way core ERP systems operate for banking creates additional compliance challenges for these organizations. Financial institutions must meet a higher compliance threshold to stay on the right side of the regulatory fence, especially as it relates to payment card industry (PCI) data.

Private information must be accessed quickly to keep up with regulations around loans, PCI, assets, and various contractual obligations. ATM data, remote banking, and banking across multiple branches add to the significant compliance burden. Financial institutions need to store data longer and aggregate and report on it more frequently.