A 5 Step Guide to Data Protection and Risk Reduction for Financial Services

November 25, 2020

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Financial services organizations face specific challenges when it comes to securing their sensitive, personal, and regulated data: from addressing layered compliance requirements for various regulations to being able to proactively manage their data to mitigate risk and boost business outcomes.

Financial services organizations face specific challenges when it comes to securing their sensitive, personal, and regulated data: from addressing layered compliance requirements for various regulations to being able to proactively manage their data to mitigate risk and boost business outcomes.  

Financial institutions need to be able to find, classify, inventory, and manage all of their sensitive data, regardless of where it is, whose it is, or what it is. It’s a momentous task — and critical to address common challenges like siloed data, lack of visibility and accurate insight, and balancing legacy systems with cloud data.

Start with a strong data-centric approach with these five key steps to protect data, achieve compliance, and reduce risk.

1.) Know Your Data

The first step in protecting enterprise data is to know your data: you can only protect what you can see.

Automatically discover, identify, and map sensitive data across the organization  regardless of where it’s stored: from on-prem to cloud to mainframe, from structured to unstructured, and get visibility on the data you know - and the data you don’t.

 

2.) Catalog and Inventory Sensitive Data

Catalog and inventory sensitive, personal, regulated, and critical data for a single source of data truth. Incorporate active metadata for added business context and data-driven insights — and get full visibility and insight into your data.

 

3.) Classify Your Data

Classify your data to effectively drive policy and enforcement. Unstructured, semi-structured, and structured data should be classified by sensitivity, type, and regulation for better data management, protection, and processing.

Automatically classify all types of sensitive information based on the content and  structure of the data – including personal information (PI), personally identifiable  information (PII), MNPI/NPI, customer data, identity data, and sensitive data – without being limited to a specific classifier.

 

4.) Identify and Manage Risk

In order to adequately manage and reduce risk, organizations need to take a data-centric approach.  

Follow the principles of privacy-by-design, establish 360º visibility of data at risk, prioritize over-privileged and vulnerable data, and enforce policy based on sensitivity and regulation to manage that risk.

 

5.) Take Action

Once you have a single source of data truth, take action to protect, minimize, remediate, and reduce risk.

Establish workflows for data retention, remediation, and risk reduction, and maintain an end-to-end approach to take action on your sensitive and regulated data.  

 

How to Get Started  

Leverage machine learning and automation to get to value faster, classify data more accurately, correlate related data, discover dark data, and drive deep data insight for successful security, privacy, and governance initiatives.  

By taking a discovery-first approach to data and risk, organizations can address the challenges of continuous compliance, minimize security risks, proactively address data privacy programs, and strengthen data management initiatives.

 

Learn more at www.bigid.com