What is Data Classification Policy?
The main goal of a data classification policy is to standardize how a company manages its data assets. A data classification policy ensures that sensitive information is properly handled throughout its entire lifecycle by all relevant stakeholders. It can significantly reduce risks associated with data security, privacy, and compliance.
A data classification policy is unique to each organization and is strongly dependent on industry standards and regulations that affect the organization. It takes into account how data is collected and structured by the organization, as well as the authorized parties allowed to access and use the information.
Data classification policies can help ensure that authorized stakeholders have access to the data while preventing unauthorized access and abuse of privileges. By classifying the data stored in databases, organizations can ensure that only those who are authorized can view, modify, delete, or add sensitive information.
A data classification policy is based on the separation of data into several classification levels, according to the sensitivity of the data. Learn more in our guide to data classification levels (coming soon).
In this article:
What is the Difference Between Data Classification Policies, Security Policies, and Risk Assessments?
It is important to understand the difference between data classification policies, security policies, and risk assessments:
- Data classification policy—a plan that helps an organization determine risk tolerance across all its data assets.
- Security policy—a plan designed according to the overall security needs of the organization. It includes security controls determined according to predefined risk tolerance. Data security policies are dependent on the organization’s data classification policy.
- Risk assessment—a technique used to assess the impact of threats on each asset. It helps in understanding the level of security each asset requires, what safeguards to put in place, and what countermeasures are required to mitigate risks. Risk assessments can complement data classification policies, by determining what concrete threats affect each category of the data asset.
What are the Benefits of a Data Classification Policy?
A data classification policy can help you achieve the following:
- Know how much data you are required to protect—and then easily implement security-related resource allocation.
- Gain a better understanding of data across the organization—learn what types of data are located in each location and determine the security requirements of each data type. Additionally, you can learn whether your current data protection situation is acceptable, from either a compliance regulation or company standpoint.
- Understand compliance requirements—by defining what types of data require certain levels of protection.
- Improve data visibility and control—properly categorized data can help gain accurate visibility into data protection, which can help improve protection controls. You can learn if data is well protected, identify weaknesses, and mitigate existing data security issues.
Examples of Data Classification Policy
Here are two examples of how data classification policies are used in practice by organizations.
Example #1: Healthcare
Healthcare technology companies that store sensitive patient information are required to comply with the Health Insurance Portability and Accountability Act of 1996 (HIPAA), which defines special requirements for the protection of protected health information (PHI).
A data classification policy can help organizations quickly provide proof that all personal healthcare information is properly classified and protected. It details the measures the organization takes and what security safeguards are applied to healthcare information. It ensures evidence is properly filed and remains accessible for auditors.
Example #2: Acquisitions
When companies are in the process of being acquired by other entities, they enter into a short window of due diligence. During this time, the company needs to demonstrate value and viability. This requires compiling a list of all assets and liabilities. Additionally, the company is assessed for how well it manages risks.
A data classification policy enables companies undergoing due diligence processes to accurately and swiftly provide all necessary information. It helps the company show that data protection is treated seriously and efficiently, and informs relevant stakeholders exactly how data is classified and protected. An efficient classification system can significantly reduce data risks, minimize liability, and increase the perceived value of the company—all of which can contribute to a successful acquisition.
Data Classification Policy Techniques
Here are two alternative techniques commonly used to classify data and define an appropriate data classification policy. In many cases, organizations combine these two methods.
Automated Classification Policy
In this technique, classification is performed by software solutions. The classification relies on algorithms that analyze phrases or keywords in the content in order to classify it. This approach is useful when specific types of data are generated without user involvement—for instance, reports created by ERP systems, or information featuring specific personal details which can be easily identified (such as credit card details or social security numbers).
Automated solutions are useful for many use cases, but because they cannot appreciate context, they often result in false positives—data wrongly classified as sensitive, resulting in unnecessary security measures that can hinder business processes and annoy users. They may also give false negative errors that make organizations vulnerable to the loss of sensitive information and may result in compliance violations.
User-Driven Classification Policy
Data classification is more efficient when the user, responsible for the data in their day-to-day role, is in charge. The user-driven classification approach gives employees themselves the responsibility to decide which classification label fits the information they manage, applying a label while data is being edited, created, saved, or sent.
User-driven classification has several benefits:
- Taps into the user’s knowledge of business value, context, and sensitivity of specific data, making data classification much more accurate
- Improves security by eliminating false negative classifications
- Promotes a culture of data security, and makes it easier to keep track of user behavior
- Makes it possible to isolate potential insider threats, and identify policy violations by specific users or departments, which can be addressed by policy changes
Related content: Read our guide to data classification best practices