Organizations can inventory their data by identity, content, sort, and sensitivity using an ML-based discovery-in-depth approach, gaining full visibility into all controlled and sensitive data of all types—structured and unstructured.
FREMONT, CA : Finance data is private, sensitive, and heavily supervised. When it comes to protecting consumer data, ensuring compliance with different legislation, and proactively managing their data to reduce risk and improve business outcomes, financial services companies face unique challenges. Furthermore, financial institutions have a bias for storing large amounts of data, which exposes them to greater risk. It is challenging to keep track of the data they know they have, let alone the dark and potentially dangerous data buried (or forgotten) in the organization.
Financial institutions must be able to locate, identify, inventory, and handle all of their sensitive data, regardless of its location or type. Doing so is a monumental task that necessitates resolving common issues such as siloed data, a lack of visibility and reliable insight, and integrating legacy systems with cloud data. All while meeting an invocation of compliance requirements.
How to Use Machine Learning (ML) to Reduce Risk
Regulated data can be found in various places, including silos, shadow servers, and data streams, as well as legacy systems and modern cloud storage. Traditional technologies that dominated the discovery landscape in the past almost guarantee that one will miss dark and sensitive data hiding in the company. These tools can only see one type of data or only find data that one already knows about, leaving many sensitive data vulnerable to attack.
A machine-learning approach to data discovery overturns this confusion, allowing financial institutions to find, clean up, and handle decades of legacy data they may or may not be aware of. Organizations can inventory their data by identity, content, sort, and sensitivity using an ML-based discovery-in-depth approach, gaining full visibility into all controlled and sensitive data of all types—structured and unstructured. Financial institutions may find and classify data associations, match inferred data, and recognize related data down to the identity level by using machine learning for correlation.
Discover Data to Protect Data
When it comes to adequately protecting their data, managing regulatory compliance, modernizing their processes, and extracting knowledge from data, financial institutions face specific challenges. To automatically discover, map, and inventory critical, controlled, and personal data across all data sources and types of data, start with a single source of data reality. From on-premises to cloud, mainframe to data lakes, organized to unstructured, financial institutions must be vigilant in understanding their data, regardless of where it resides.
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