The high-risk cyber threat landscape shows no signs of slowing down, with serious data breaches and new regulatory requirements governing sensitive data protection. Two notable examples are the upcoming CPRA amendment to California’s CCPA personal data regulation in January 2023 and an amended GLBA Safeguards Rule that sets out stricter cybersecurity procedures for financial entities.
The sheer volume of data collected and leveraged for business purposes complicates compliance and effective data protection. As organizations increasingly use a multi-cloud strategy, sensitive data often ends up across disparate cloud services with little visibility into its level of protection. Today, 92 percent of organizations have a multi-cloud strategy in place or underway.
Keeping track of sensitive data flows in this multi-cloud world makes it challenging to comply with regulations and protect valuable information from prying eyes.
To minimize sensitive data exposure, businesses must go back to basics with effective sensitive data identification and classification.
What is Data Classification?
Data classification analyzes, labels, and organizes data into relevant categories based on shared characteristics. The purpose of classification is to facilitate more efficient retrieval, use, and protection of data assets. Ease of access is a compelling reason to classify data, but it’s arguably not as important as the potential compliance benefits from accurately classifying sensitive data.
When you know where your data is, what it is, and who has access to it, you’re far better placed to avoid the hefty costs of non-compliance. While achieving compliance can be cumbersome, costly, and cause headaches, it’s far less expensive than non-compliance. The average cost of non-compliance costs is $14.82 million, and there are usually significant extra reputational impacts.
For several regulations, data classification is not something seen as merely helpful for compliance; it’s a mandatory element of being compliant. The HIPAA Privacy Rule requires organizations to group electronic protected healthcare data (ePHI) according to its sensitivity using a simple three-level data classification. PCI DSS for cardholder data has a rule requiring businesses to “classify media so that sensitivity of the data can be determined.”
A similar classification system to HIPAA’s recommendation is a good starting point for any data classification effort, so it’s worth highlighting:
- Restricted/confidential: The most sensitive data assets for which disclosure, destruction, or modification carries significant business consequences, including non-compliance.
- Private: Data that should be kept private and internal to the business because it’s prudent to do so. Examples include internal memos, business plans, budget spreadsheets, and instant messenger communications.
- Public: Data that can be freely disclosed without risk, including press releases or job descriptions.
What is Sensitive Data?
Sensitive data is information with a high level of confidentiality that requires robust protection against unauthorized access. Sensitive data sometimes gets conflated with personal data because of all the different regulations focusing on this sub-category of sensitive information.
The actual scope of sensitive data is more encompassing than just personal data. Other types of sensitive data include trade secrets, intellectual property (which includes code), acquisition plans, privileged credentials, and even marketing metrics.
However, sensitive data protection measures often focus more on sensitive personal data because unauthorized access to this kind of information negatively affects customers and regularly results in non-compliance fines. Cardholder details, biometric data, and healthcare data are examples of information that requires stringent protection to achieve regulatory compliance.
Improper access controls, shadow IT assets, misconfigurations, and a lack of encryption are all potential security risks amplified in today’s complex IT environments. With hybrid work environments remaining the norm, cloud computing infrastructure provides the backbone for remote and on-premise collaboration across every department, from DevOps to marketing teams. But poor data discovery and classification can result in sensitive data assets easily escaping a company’s oversight and ultimately being left without sufficient protection.
Why is Identifying and Classifying Sensitive Data important?
Improved Risk Management
At a high level, identifying and classifying sensitive data is imperative for effective risk management. When you accomplish both of these tasks as part of data management, you have insight into the value of different data assets to your organization.
Just as you wouldn’t want to leave sensitive data unencrypted, it would be equally unnecessary (and costly) to encrypt information for which unauthorized access or unintentional disclosure carries no consequences. You can effectively prioritize security controls based on knowing where your data is and what its value is rather than playing a guessing game.
Better Compliance
Data classification does not need to be mandated by a regulation to provide compliance benefits. When you can find sensitive data, label it, and track it as it gets dispersed throughout your data ecosystem, your company will find it far easier to maintain compliance with any regulation.
Consistent compliance preserves your brand’s reputation among existing and prospective customers. Younger demographics are particularly discerning about businesses demonstrating lax data protection. One survey found that 63 percent of 18-24-year-olds permanently stopped using a firm’s services following a breach.
