Data Classification in Finance

Why should the financial services sector adopt a data classification strategy?  

Because more than nine out of ten (95%) of data breaches are financially motivated, according to this year’s Verizon Data Breach Investigations Report. Within the finance and insurance sector specifically, the rates were as high as 97% — no surprise there.  

However, what may be surprising is that most of these attacks were easy to do.  

One of the top two attack patterns was mis-delivery, or in other words, sending the wrong document to the wrong person. This can result in de facto data breach, or simply supply a threat actor with just enough information to leverage another (bigger) breach down the road.  

In either event, protecting data starts with sorting it properly. That’s where data classification comes in. You can’t secure what you don’t understand, and last week’s webinar deserves a different level of security than snippets of the company’s source code.  

The finance sector is losing too much ($5.9 million this year) to data breaches when data classification helps get to the root of the issue. Here’s why, how, and what financial firms have to gain. 

Why Financial Data Protection Is Critical 

Every year, thousands of pilfered records hit the dark web. Stolen data can go for up to $1,000 a piece, and there were no less than 153.3 million records negatively impacted by financial service data breaches between 2018 and 2022. At least 79 U.S. financial firms reported data breaches affecting upwards of 1,000 people in 2022. 

While the impact to consumers is paramount, we can’t ignore the effect on the financial services industry. Finance firms lose approximately 28% more than the global average per data breach, according to the IBM Cost of a Data Breach Report 2023. And since most estimates place the financial sector at roughly a quarter (20-25%) of the global economy, it isn’t hard to see that what happens here can inflict the status quo at large. 

Financial data is at a premium, and if there is any time for solutions that work, it is now. Data protection in the financial services industry requires not only next-generation solutions, but primarily, a foundation of accountability for all digital assets.  

What Is Data Classification? 

As this year’s DBIR notes, it’s the little things that impact financial breaches the most. That’s why, when considering cybersecurity solutions for financial services, a data classification tool should be at the top of the list. 

Data classification is the process of using predefined criteria to organize and label assets by type, business value, and sensitivity. The four common levels of data classification are: 

1. Public 
   No restrictions on access or usage; press releases, brochures, public research 

2. Internal 
   For internal employees who are granted access only; memos, internal emails, marketing research 

3. Confidential 
   Access by permission only and contained within the business or third parties: personally identifiable information (PII), personal health information (PHI) 

4. Restricted 
   Need-to-know basis: trade secrets, intellectual property, federally protected data 

Classifying your data in this way is foundational to creating policies that will then protect that data and protect it accurately. Each classification warrants its own level of security and makes policy creation methodical and more effective. This, along with other ways of streamlining financial data security, can help companies in the industry protect against threats at scale and with intention.  

Challenges of Financial Data Protection 

With the acceleration of hybrid models, cloud-based networks, increasing regulation, and advanced threats, the industry has faced some significant obstacles to smoothly meshing finance and cybersecurity. Data classification solutions help to alleviate these challenges in the following ways: 

• Data Visibility 
  Know which data is sensitive customer banking information and which is publicly available policy information — and know where all that sensitive information resides. If data gets lost in the network, it is both unprotected and likely uncompliant, landing you on the wrong side of data protection requirements.  

• Addressing Workforce Gaps 
  The cyber talent shortage is expected to grow in the banking industry, and SOCs need a way to do more with less. Data classification lifts the burden of overwhelm as data is neatly arranged and easier to create policies and protections around.  

• Data Privacy Regulations 
  Stay compliant with financial data privacy regulations like SOX, PCI DSS, GDPR, and more. You can’t securely maintain data you are unaware of, and data classification makes data easy to pull in an audit.  

• Emerging Threats 
  Classifying data helps defend finance firms against this year’s emerging threats — from RaaS to supply-chain exploitation — by providing context to partner tools like endpoint detection and response (EDR), user/entity behavior analytics (UEBA), and more. It not only helps determine where the breach occurred and how severe it is, but what response action should be prioritized first.  

Benefits of Data Classification in Finance 

Besides identifying and protecting data wherever it is located within the enterprise, benefits of finance data classification include: 

• Preventing missends. One of the top two most prominent sources of financial data loss, missent information can be prevented with data classification tools. Email classifiers sort information within an email client to prevent sensitive data from being sent to the wrong person.  

• Global data protection regulations like GDPR, CCPA, HIPAA, CMMC, ITAR, and CUI to help you stay compliant. Categorizing information gives organizations more control and granularity, making data easier to retrieve when needed for risk management and compliance purposes.  

• Metadata that presents context to otherwise general alerts. Data classification augments downstream data security solutions like encryption, data loss prevention (DLP), and digital rights management (DRM). It does this, in part, by reducing the number of false positives stirred up by the high-level scans of DLP tools, improving responses with better, more accurate information. 

Knowing not only where, but what data is lets companies make better choices about how to protect, manage, and share it both inside and outside the organization.  

Advancing Data Classification with Fortra 

When it comes to financial data protection, Fortra's Data Classification Suite offers financial institutions around the world a way to structure data, secure it in place, improve DLP via automation, and make users more aware of the data they use. Talk to a Fortra expert today and start the conversation.