Three Steps to Identify and Protect Consumer Financial Information


As data volumes rise, data protection regulations grow in strength, and bad actors continue their cyber attacks on the industry, financial institutions need to remain on high alert when it comes to securing their customers’ sensitive information. This blog outlines three steps organizations can take to minimize their risk exposure and protect consumer financial information.

Three Steps to Identify and Protect Consumer Financial Information
1. Discover and Classify Unstructured Banking Data
2. Automate Data Governance to Secure Consumer Financial Information
3. Enable On-Going Financial Data Risk Management

For as long as banks have been operating, they’ve been placed in positions of great trust. This trust comes not only in the form of finances but also in the vast amount of personally identifiable information (PII) and payment card industry (PCI) data they hold about their clients and partners. In addition to protecting money, they’re entrusted to protect consumer financial information.

Any failure to responsibly manage and secure this trove of data can incur hefty penalties from industry regulators – Citi was hit with a $400 million fine for this in 2020 – and will also undermine public confidence in the organization. In a sector where trust is paramount, this can be devastating.

Of course, this is nothing new. Most banks already know the need to secure their data and have tried to protect sensitive information in well-structured and well-secured databases. They believe that because these databases are secure, their data must be as safe as possible.

However, this is not necessarily accurate. Research has found that as much as 80% of all banking data is unstructured, falling outside these highly managed environments. This so-called unstructured data exists as text files, emails, voice notes, and any one of the thousands of other scraps of data scattered through any large organization’s systems.

While much of this data is stored in benign documents relating to the organization’s day-to-day operations, there is always a risk that sensitive consumer financial information is buried somewhere in the mix. For example, a credit card number might lurk in PDFs of contracts attached to emails or spreadsheets used to share customer details throughout the support team quickly.

Regardless of its origins, with cyber-attacks on the banking sector increasing yearly, organizations holding this valuable and sensitive data must take a proactive approach to manage their unstructured data.

Discover & Classify Banking Data

One of the eternal truths of data security is that you can’t manage what you can’t see. Therefore, the first step banks need to take is to interrogate their unstructured data and learn what it holds.

However, this process is somewhat more complex than it may first seem. If nothing else, it’s hard to overstate the sheer quantity of unstructured data that modern organizations manage. Studies indicate that the average enterprise stores over 300TB of data within its various systems and generates more daily.

Beyond this, unstructured data is – by its nature – not particularly well organized. It isn’t typically sorted into consistent formats or file types, making even the seemingly straightforward processes of accurately identifying this virtually impossible for human workers to manage.

Fortunately, tools and technologies exist to help with this challenge. Among the most useful of these are classification engines that can rapidly process vast numbers of files and documents and seek out potential risk.  The most advanced classification tools include artificial intelligence (AI) to augment humans and increase accuracy.

Performing this unstructured data analysis and classification is integral to identifying and protecting consumer financial information.

Automate Data Governance to Secure Consumer Financial Information

Knowing what data exists is only half the battle. It’s also crucial that banks are proactive in securing their data once they’ve uncovered the risks.

Again, this is an area where AI-driven software can be invaluable. The banks can configure the same tools that identify the risky data to automatically apply governance rules. For example, defined rules could instruct the AI to flag any potential PCI data so that a human administrator can investigate, or it could automatically redact the information and quarantine the file in a secure system area.

Even simple rules, such as limiting access to those with the correct permissions, can deliver potent results. For example, research has found that 84% of businesses say their unstructured data is accessible by people with no business need for access. Making potentially risky data inaccessible to users who don’t need to view it can produce massive benefits.

The financial industry is heavily regulated, and these regulations are often changing. With automation in place, banking institutions can ensure that unstructured data across the organization is secured in compliance with the wide array of data protection regulations that they’re responsible for adhering to.

Ongoing Financial Data Risk Management

There is a regrettable tendency among all businesses – and banks are not immune – to view both physical and cyber security as a project to tick off every few years. However, proper data governance shouldn’t be seen as a one-and-done operation.

Instead, it’s vital to view data risk management as an ongoing concern. Banks generate unstructured data every single day. Therefore, any data governance must also be continuous if a bank wants to be confident that they are minimizing the risk associated with its data.

For many organizations, this means working with governance software to monitor unstructured data 24/7. For example, software can run in the background on a bank’s systems and automatically flag potentially risky data almost as soon as it’s generated. This proactive response can dramatically cut down on the financial and reputational risk the bank takes on in its daily operations.

Related video: Data Regulations are Constantly Changing—Your Data Management Should Too

Proactive Protection of Consumer Financial Information Builds Trust

While it may not hold any tangible value, trust is one of a bank’s most important assets. Research has shown that it even outstrips price when influencing customers’ choice of banks. Unfortunately, while this trust comes in many forms – we generally expect a bank to manage our money carefully — everything is undermined with poor data security and governance.

Proper management of unstructured data forms a crucial part of any security plan, and AI-driven data discovery and governance software can form the core of this management. It allows banks to ensure that they aren’t exposing themselves to any more risk than they must and can illuminate any dangers lurking within their unstructured data.

A holistic financial data risk management practice should include the discovery and classification of unstructured data, automated data governance to protect consumer financial information, and continuous data risk assessment to identify and manage potential regulatory violations before they occur.

Customers are unlikely to trust a bank that leaves their finances unguarded and unobserved. So why would they trust a bank that did the same to their data?

Request a demo to see how DryvIQ can help financial institutions uncover and manage the risk within their unstructured data.

Learn more about DryvIQ unstructured data discovery and governance solutions.

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