At a glance
A master data classification policy defines the rules for how data should be categorized, as well as who has access to sensitive or confidential data.
There are four major data classification levels: public, internal, confidential, and restricted.
Data classification standards like GDPR, NIST 800-53, and ISO 27001 help businesses maintain data integrity and remain compliant with relevant industry regulations.
Table of contents
What is a master data classification policy?
A master data classification policy is a key element of any effective privacy or security program. It defines the rules for how data is categorized and stored, while identifying which departments and personnel have access to sensitive or confidential data.
This policy also sets different security levels for each type of sensitive information, ensuring that only authorized users can access it. Implementing a well-defined master data classification policy is essential for protecting an organization's critical data.
Data classification levels
Data classification levels are the different categories that data is placed into—depending on its risk and value. In a typical commercial setting, data will be classified as public, internal, confidential, or restricted.
Public data
Public data is freely available and does not require any special security measures. As its name implies, public information can be openly shared with anyone without the need for additional precautions.
Internal data
Internal data is only intended for use within an organization, and can include things like the employee handbook, company policies, and certain company-wide communications. Though it should remain private, if this type of information were to be made public, the repercussions would be minimal.
Confidential data
Confidential data must be kept within the organization and should only be accessed by authorized personnel. It can include information like pricing details, promotional materials, or contact information. If this type of data were to be disclosed, it could damage the company or brand.
Restricted data
Restricted data requires the highest level of protection and access must be limited to necessary personnel. Often protected by a Non-Disclosure Agreement (NDA), restricted data can include trade secrets, credit card details, medical records, and personally identifiable information (PII)—which is especially important in the context of privacy.
Data classification standards
Data classification standards are the discrete rulesets that govern how data should be governed across different industries, including:
Guidelines about what data types belong in each classification level
Who can access the data
Necessary security measures like encryption and/or authentication, and
Procedural information about how and when data is accessed
In the simplest terms, data classification standards exist to help businesses maintain data integrity and remain compliant with relevant industry rules. Though there are several important data classification standards, below we’ll explore how data is classified under the General Data Protection Regulation (GDPR), NIST 800-53, and ISO 27001.
Data classification under GDPR
Classifying data, specifically PII and sensitive information, is essential to GDPR compliance. As a refresher, GDPR is the landmark privacy law in the EU, offering consumer data rights to EU citizens and regulating businesses who process consumer data in the EU.
GDPR Article 4 offers a clear, but broad definition of personal data:
“‘'personal data' means any information relating to an identified or identifiable natural person...”
Under this definition, personal data includes:
Phone number
Physical address
Driver’s license number
License plate number
Social security number
Credit card information
IP address
Bank account
Location data
Utility records (sewer, gas, water, electric)
Work hours or performance
Biometric data like weight, height, hair color, or fingerprints
Personal data can include information like someone’s name (a direct identifier) or physical characteristics (an indirect identifier). Ultimately, personal data is any information that can identify an individual—whether it’s used independently or in tandem with other data.
Data Protection Impact Assessments (DPIAs) play an important role in classifying PII data under GDPR. Completing a DPIA means analyzing all data processing workflows involved in the collection, use, storage, and deletion of personal data.
It also entails assessing the value or confidentiality of the information, as well as potential risks that could occur in the event of a security breach.
These assessments help organizations understand what, when, and where personal data is being processed in order to better respect individual privacy rights. They also help organizations develop and implement measures that protect collected data from accidental or unlawful alteration, destruction, loss or disclosure.
NIST 800-53 data classification
NIST 800-53 is a data classification system created by the National Institute of Standards and Technology (NIST). It helps organizations identify which information they are collecting and whether or not it should be protected.
This system is based on three main types of data: public, sensitive, and confidential. Public data is accessible to anyone, while sensitive data requires additional security measures due to its importance. Confidential data requires the highest level of protection as it carries the most risk if it were released or misused.
The NIST 800-53 classification system can help organizations better understand the risks associated with their different data types and ensure that appropriate security measures are put in place.
ISO 27001 data classification
ISO 27001 is an information security management system set forth by the International Organization for Standardization (ISO). It focuses on data classification and provides organizations with a framework for categorizing sensitive information.
Data categorized under the ISO 27001 standard can include intellectual property, customer data, financial records, employee records, personal information, and any other type of confidential or sensitive data.
This data classification system helps organizations choose appropriate security measures according to the value and sensitivity of the information they are collecting. It also ensures that companies adhere to both local and international regulations related to protecting personal data and privacy rights.
Why is data classification important
Data classification is important because it helps keep sensitive data secure, decreases the chance of data breaches or misuse, and supports compliance with relevant data protection laws.
A comprehensive data classification policy can increase visibility into how data is being collected and processed, reallocate resources to other strategic projects, and minimize operational risk by ensuring regulatory compliance.
Increase data visibility
Having a clear data classification system is the first step towards understanding how, when, and why your company is processing personally identifiable information (PII) or sensitive data.
Classification makes it easier to understand what kind of information is being collected, where it's stored, and who has access to it. It also lays a strong foundation for implementing a data privacy compliance program.
Support robust compliance
Creating a data classification policy is essential to promoting a culture of compliance throughout your organization. By clearly defining the sensitivity levels of different kinds of data, you can ensure that confidential and classified information remain secure and protected.
Putting a clear classification policy in place will help your organization comply with any regulatory obligations, avoid penalties, and reduce the chances of costly errors.
Resource savings
Socializing a clear data classification system makes it easier for employees and technologies to recognize sensitive information quickly. This helps to ensure appropriate security controls are applied, reducing costs in the long run.
5 data classification best practices
For organizations who collect and process personal data, implementing a data classification policy is critical for effective data protection and compliance. To ensure the success of your data classification framework, there's a few best practices to follow.
Manage expectations
When introducing a data classification framework, don’t expect to go from 0-100 overnight—data classification is an iterative process and it’s alright to start small.
Consider the industry you work in and prioritize the data types most likely to be scrutinized by regulators. Build relationships with the team most involved with that data and start applying your framework there first.
Consider your audience
Don’t assume that everyone reading your data classification documentation is a cybersecurity or privacy professional. In fact, it’s safer to assume the opposite. Data classification frameworks should be written with a wide audience in mind—using clear, concise language that marketing, sales, legal, IT, and leadership can understand.
Write straightforward definitions for your data classification levels and be sure to provide real-world examples whenever possible. Avoid jargon, industry-specific acronyms, and overly technical terminology.
If it’s impossible to skip the acronym or industry-term—be sure to include a definition, so it’s easy for everyone to get on the same page.
Avoid unnecessary granularity
Though we outlined only four data classification levels above, some systems include additional levels like classified and top secret. Our advice to you? Only include as many data classification levels as is truly necessary!
The more complex your data classification system is, the harder it will be to implement across your org and the more mistakes there’s likely to be. When deciding how many data classification levels you need, consider:
Your industry—heavily regulated industries tend to need more data classification
The effort involved in managing a complex framework
How an increase in complexity will affect employees at your company—at organizations well-versed with strict security, increased complexity will have a less pronounced effect
Overall user experience when trying to manually classify various data types
Provide training and clear information
Training is key to successfully implementing a master data classification policy. Try to provide as much information as possible about how to classify data, handle different types of information, and what folks should do if they come across an unfamiliar use case.
Develop a few modules to teach existing employees about classification, and make sure new hires are aware of those resources.
Pull in the right people
For a data classification framework to work, you need to make sure you’ve built up enough cross-functional support. IT teams may lead it, but they should also involve privacy and legal stakeholders such as the Chief Privacy Officer and the Office of General Counsel.
Additionally, input from the compliance department, information governance professionals, and communications team can be valuable when rolling out the framework internally.
Use cases for data classification
Data mapping
Data classification is an important part of data mapping because it helps categorize data sets according to their sensitivity and importance. Organizations can more easily identify which parts of the data sets need to be protected and managed, while those with less security requirements can take lower priority.
Data classification provides a basis for organizations to efficiently map out their data landscape, ensuring that all pieces of information remain safe and secure.
Privacy compliance
Data classification also allows organizations to comply with local and international data privacy regulations. This is especially important in today's digital world as many businesses collect and store large quantities of customer information.
By classifying this data according to the level of sensitivity, companies can build robust compliance programs and better avoid expensive non-compliance fines.
Risk management
Data classification is also important when it comes to risk management. By properly assessing the risks associated with different types of data, organizations can ensure they are taking the necessary steps to protect their information from potential threats.
This helps reduce reputation damage and financial or legal repercussions in the event of a security breach or unauthorized access to confidential information.
About Transcend
Transcend is the platform that helps companies put privacy on autopilot by making it easy to encode privacy across an entire tech stack.
Transcend Data Mapping is the only solution that goes beyond observability to power your privacy program with smart governance suggestions. Get unified data management through automated scanning, data silo discovery and advanced data classification, all in a collaborative platform.
Ensure nothing is tracked without user consent using Transcend Consent, automate data subject request workflows with Privacy Requests, and mitigate risk with smarter privacy Assessments.
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