For any and all organizations, the management and protection of data is a significant concern that should be treated as a top priority. Vast amounts of sensitive business data and personally identifiable employee and customer information are often handled by organizations and stored in their systems, and it is essential to ensure that this data is not stolen, tampered with, or abused. In order to effectively prevent both malicious attacks and accidental breaches, the organization must first have visibility into and understanding of what data is available and where it is found. This is where data discovery comes into play.
Defining Data Discovery
Data discovery is “the process of finding and classifying data, making it useful for some purpose.” This includes locating sensitive or regulated data and identifying it for the purpose of either protecting or deleting it securely. Essentially, there is no real way to protect data without data discovery, as undiscovered or unidentified data will always have the potential to cause a security incident, whether it be an intentional or unintentional breach. Data discovery is also necessary for less security-based functions, such as to analyze it for patterns and apply knowledge of the data to inform business and strategy decisions.
There are a number of varying approaches to data discovery that may be more or less effective, depending on the specific use case that the organization needs it for. Organizations looking to use data discovery for business intelligence purposes may leave the tools in the hands of a data science expert, while those attempting to use it to achieve and maintain compliance might take a different approach. In any case, it is important that the professionals in charge of handling the data discovery process are familiar with what the organization needs and hopes to gain from the use of data discovery.
Why Does It Matter?
Data discovery is vital for a slew of reasons, chief among them being the protection of data. The growth in popularity of remote and hybrid working has created an environment where a good portion of business is conducted through the cloud, including file storage and sharing. The sprawl of data across many different systems, applications, and databases can make it nigh impossible for an organization to authenticate and protect its sensitive data. The process of data discovery is necessary to identify and classify data in order to determine how it is at risk and how best to protect against attacks.
One major use case for data discovery is to achieve and maintain compliance with regulations such as the EU’s GPDR, as well as industry standards and general privacy and data protection needs. Privacy regulations, as they are designed for the protection of data, require heavy documentation in order to verify compliance. Data discovery is an extremely useful tool for locating, identifying, and classifying data in a manner that aligns with the requirements of regulatory entities. Additionally, it is important to understand what data is at risk and where the organization stores and sends it for the purposes of defending against attacks.
Tips for Effective Implementation
Implementing an effective strategy for the discovery and protection of data requires an organization to approach the issue with its own particular needs in mind. It is vital to conduct sufficient research into the options available in order to determine the data discovery program that will work best for the business in question. This requires defining the types of data that the organization wishes to locate and identify and finding a program that is capable of detecting all of the relevant data types. The program must be able to scan the organization’s systems and services comprehensively for sensitive data.
It is just as important to implement a solid security strategy to protect the data once it has been discovered and classified. An effective data loss prevention (DLP) tool will allow visibility into several areas that traditional solutions leave vulnerable. Real-time detection and remediation of potentially risky and suspicious behaviors enables users to see the actions that may lead to a data breach and avoid them before they occur. Combined with the information provided by a good data discovery program, a DLP tool can detect sensitive data being misused or abused and prevent users from intentionally or unintentionally causing breaches.
The task of implementing a robust strategy for data discovery and protection can be a daunting one, but an organization can manage it with the proper tools and practices. Preventing both malicious cyberattacks and unintentional data breaches is a matter of the utmost importance that all organizations should prioritize in order to protect their assets. An effective data discovery program should be able to scan the entire company for any types of sensitive data that the organization deems important, and a DLP solution should be able to prevent users from carrying out risky actions that may endanger the company’s data.
PJ Bradley is a writer on a wide variety of topics, passionate about learning and helping people above all else. Holding a bachelor’s degree from Oakland University, PJ enjoys using a lifelong desire to understand how things work to write about subjects that inspire interest. Most of PJ’s free time is spent reading and writing. PJ is also a regular writer at Bora.