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.
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:
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.
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.
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.
A persistent barrier to sensitive data protection and compliance is the proliferation of unstructured data, which doesn’t adhere to any pre-defined data model or schema. Assets in this class include email, media files, PDFs, Word docs, and more. The inherently untidy nature of unstructured data makes it scattered somewhat chaotically in the network and among cloud systems.
When you don’t know where data assets are, you can’t protect them or apply policy-driven controls that align with any regulations governing sensitive information contained within these assets. Solutions that automatically discover, label, and track these unstructured data assets can dramatically improve your security posture and reduce non-compliance risks.
When employees can’t easily find the data they need to perform specific tasks, workflows aren’t efficient. An hour can easily pass with an employee trawling different cloud services for a presentation or report they were working on. Data classification facilitates workflow efficiency by making information easier to find.
When classifying sensitive data, it’s good practice to reflect the sensitivity of the information in any labeling method you use to tag the data. Combining this with the three-step classification model previously outlined, you end up with three distinct classes of sensitive data:
If there is a degree of subjectivity, for example, a file that’s both low sensitivity and yet it’s internal, it’s best to classify it at the higher of the two levels.
When it comes to deciding how to approach the task of classification, the three commonly used types of data classification are:
The tag or classification level given to data during these approaches influences decisions about protecting different data assets. A solution that automates classification is recommended to improve classification accuracy and speed in modern IT networks.
Suppose it’s clear by now why identifying and classifying sensitive data is so important. In that case, it’s understandable if you remain somewhat perplexed about the best way to achieve the necessary discovery and classification for your company. There’s no getting around the fact that data creation is chaotic and decentralized, and data assets are dispersed throughout the cloud.
Polar's data security posture management solution is the first automated data security and compliance platform to automatically map and follow your data and data flows to provide deep visibility and protection across your cloud-native data assets. Automated data labeling takes any guesswork out of classification and keeps up to speed with the complex data ecosystems in which most DevOps and security teams immerse themselves.
Regardless of how fast developers or other teams create data, you can discover, label, and track it. The platform also detects and mitigates compliance violations before they become costly.