Introduction
An estimated 82% of the data breaches that occurred in 2022 involved human error or intentional misconduct. That’s why organizations need to be diligent in protecting their data from both internal and external threats. One way to do so is by implementing a strong information governance (IG) program that will keep an organization’s data organized, secure, and accessible.
But to implement such a program, information governance managers must first determine which data needs what level of security and accessibility. That’s where data classification comes in. By assigning risk categories to their institutional data, organizations—from corporations and government agencies to healthcare organizations and entities in other industries—can improve their information governance programs and adequately protect their data.
In this post, we’ll give a complete overview of data classification, including its definition and a review of the main types of data classifications. We’ll then explore how data classification can support an organization’s information governance efforts, how organizations can implement best practices, and how modern technology can help.
Contents
What is a data classification policy?
Types of data classifications
When and why organizations need data classification policies
How data classification helps with information governance
3 best practices for integrating data classification policy with information governance
IPRO helps organizations effortlessly manage information governance
What is a data classification policy?
A data classification policy is a set of rules and procedures that an organization implements to classify its information based on its degree of sensitivity and then organize it accordingly. One of the key tenets of information security is that different types of data require different levels of security controls. That’s because some data sets contain sensitive, e.g. personally identifiable information, while others must be widely and easily accessible. Data classification allows an organization to group its data by category and dictate the security controls that are necessary for each category.
Let’s turn to the most common types of data classifications.
Types of data classification
Organizations generally classify data based on risk sensitivity, or the degree of financial or reputational harm the organization would suffer if the information were compromised. Here are the four most common data classifications:
· Restricted: Restricted data is the most sensitive information an organization has. It is only used for specific purposes and should only be accessible to a select few within the organization. Restricted data includes trade secrets, customers’ financial information, and regulated data, such as protected health information (PHI) under the Health Insurance Portability and Accountability Act (HIPAA).
· Confidential: Confidential data can be used by the data owners or certain people within an organization but must be protected from widespread access by each data owner. Examples include marketing data, customers’ contact information, and details of business strategy.
· Internal: Internal data is less protected and can be used and shared widely throughout the organization. It should not, however, be exposed to the general public. Internal data includes employee handbooks and company policies and communications.
· Public: Public data is information that can be shared with anyone at any time; it requires no security controls. Public data can include product ingredients, promotional materials, and information about an organization, such as its structure and the names of its executives.
Do all organizations need to separate their data into these or similar classifications? Let’s look at when an organization might need to establish a data classification policy.
When and why organizations need data classification policies
Data classification can be helpful for any organization, but it’s especially important for organizations that deal with sensitive data, including proprietary business information and individuals’ personal data. Data classification policies help organizations of all kinds:
· protect their data;
· comply with data privacy laws and other legal and regulatory requirements so they can avoid fines, lawsuits, and reputational damage;
· save money on security controls by separating sensitive and non-sensitive data; and
· understand what information they have and where to find it.
You may be thinking that data classification sounds a lot like information governance—and you’re right. Let’s look at how these concepts are related.
A data classification policy isn’t the optional cherry on top of a robust information governance program—it’s an essential component of that program. A data classification policy provides a solid foundation for effective information governance by allowing an organization to understand its data and the types of risk that data poses.
Once they understand the various categories of data they have, information governance managers can efficiently address the needs associated with each classification, from heightened information security to easy accessibility, instead of taking a piecemeal approach to individual pieces of data. This makes it easier for an organization to adequately manage and protect its data throughout its life cycle and demonstrate that it has taken steps to comply with relevant data privacy laws and regulations, all of which are part of a healthy information governance strategy.
Here are three ways to efficiently adopt a new data classification policy in a way that complements an existing information governance strategy.
1. Think like a lawyer.
As we’ve explained previously, improperly using or storing data can be a huge legal liability. When adopting a data classification policy, organizations must consider more than just potential business risks; they must also be mindful of the laws they need to comply with, from HIPAA to the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). By taking this holistic approach, organizations can accurately categorize their data and set their information governance programs up for success.
2. Continually monitor data.
Although the types of data classifications are relatively fixed, the needs of the data within those classifications can change. For example, if a law or regulation is amended or an organization adds new information to an existing data set, data may need to be re-classified to a more or less protected category. By continually monitoring their data, organizations can adapt to these changes and identify inaccuracies.
3. Leverage technology.
Technology can help organizations automatically search and classify their data. Modern platforms can sift through data much faster and more thoroughly than humans can, allowing organizations to cover their bases and shift their attention to information governance sooner, avoiding potential business risks and legal liability.
