Data classification is an important part of any cybersecurity program. It helps organizations protect their sensitive data and ensures that data is handled in a secure manner. But what’s the difference between a data classification policy and a data classification framework?
A data classification policy outlines the rules for how to classify, store, use, and share information within an organization. It defines who has access to which types of information and sets out procedures for handling confidential or sensitive material. A good policy should also include guidelines on how to respond if there is a breach or unauthorized access attempt.
On the other hand, a data classification framework provides more detailed guidance on how to implement the policies outlined in your organization’s security plan. This includes specific steps for classifying different types of information based on its sensitivity or risk level (e.g., public, internal-only, confidential), as well as processes for securely storing and sharing this information with authorized personnel.
The framework should also provide guidance on responding to potential threats such as malware attacks or insider threats by outlining appropriate measures that can be taken in each case.
A data classification policy facilitates the creation of a data classification framework — with data parameters defined in in the policy, you’ll be able to better identify and outline appropriate actions and responses in the framework.
Understanding data classification categories
Data classification software should be tailored to your organization, but classification types can’t be arbitrary. Use the following four classification categories as a guide for your classification mapping exercise:
Public Data
Publicly available information is classified as “public” and includes anything that can be accessed without restriction or authentication requirements. This could include publicly available documents such as published press releases or company websites. Publicly accessible information should not contain any sensitive or confidential material; if it does, then it should be reclassified as either internal use only or confidential depending on its sensitivity level.
Internal Use Only Data
Information marked for “internal use only” is not intended for sharing with external parties unless necessary. This type of information may include employee records, financial reports or other proprietary business materials that are intended for internal consumption only. Organizations must ensure that all employees who have access to this type of material understand their responsibility to keep it secure from unauthorized and external individuals.
Confidential Data
Confidential information requires additional levels of security due to its highly sensitive nature; this includes customer records, trade secrets and intellectual property rights (IPR). Organizations must take extra precautions when handling confidential material by implementing strong encryption protocols and limiting access privileges based on need-to-know criteria established by management personnel responsible for overseeing IPR compliance efforts within their respective departments, divisions or business units (DBUs). Additionally, organizations must ensure they have adequate measures in place to detect potential breaches before they occur so they can respond quickly if one does happen – otherwise serious legal repercussions could result from mishandling protected personal identifiable information (PII) stored within their systems, networks and cloud environments.
Restricted Data
Restricted data refers to extremely sensitive material which requires special authorization before being accessed. Examples include government intelligence files or medical records containing patient health histories. Accessing restricted materials typically involves multiple layers of authentication including biometric scans like fingerprints & retinal scans along with two-factor authentication methods like passwords or PIN codes. Organizations must implement strict policies governing who has permission to view these files and under what circumstances. Failure to comply with these regulations could lead to consequences through regulatory bodies such like HIPAA or the SEC.
Aligning data classification categories to your data classification policy
Identifying appropriate data classifiers is the process of categorizing data based on its sensitivity, risk, and value. These classifications ensure your policy will align to the unique types of information your company has. It can also help you accurately identify and flag which types of data need to be protected more rigorously than others.
Here are five steps to guide your organization’s data classification policy:
1. Identify the types of data your organization handles.
This includes both structured and unstructured data, such as customer records, financial information, intellectual property, and more.
2. Assign a classification level to each type of data based on its sensitivity, risk, or value to the organization.
These classifications include public, internal, confidential, and restricted.
3. Establish rules for how data should be handled based on its classification level.
This includes who can access the data, where it can be stored, and how it should be shared or transmitted.
4. Develop a process for regularly reviewing and updating the data classification policy.
This should include a review of any new types of data that may have been added to the organization’s systems, as well as changes in existing classifications.
5. Train employees on the data classification policy and ensure they understand their responsibilities for protecting the organization’s data.
This should include regular updates on any changes to the policy as part of your security awareness training cadence.
Once you’ve created or updated your data classification policy you can create a data classification framework that will map and detail data handling across your entire IT infrastructure, from physical storage devices through to cloud applications.
In practice, you can test and measure the efficacy of your data classification policy by output and outcomes like:
- Having an accurate and up-to-date inventory of all assets across the business.
- Maintaining a customized asset catalog with current classification types that align to your business and the nature of the data it handles.
- Establishing a risk benchmark that’s based on the sensitivity of data types that live throughout your environment.
- Demonstrating data integrity through data access controls.
Find and classify all your organization’s data
Understanding the types of information your business has, and how that data aligns to data classifiers is essential for creating an effective data classification policy that balances appropriate data access with robust protection.
Manual classification methods and traditional data management methods like unique databases and meta data tagging are prone to error, can’t scale, and lack diligence. The resulting blind spots make auditing next to impossible.
Automated data discovery and classification solutions can consolidate data across all sources through a single inventory and simplify the process of classifying and tracking data types over time.
Applying an automated solution will make the task of implementing and enforcing your data classification policy and framework simpler and more efficient. Book a demo with our team to learn more.
