Data Discovery and Classification are Essential
What are data discovery and classification?
Data discovery and classification are two steps towards data security and privacy. Organizations must know where their data is located so that they can secure it. More than that, they need to know the particulars of the data in order to classify it properly.
Data discovery is the process of determining where data (structured and unstructured) exist - on a local database, a server, or in the cloud. Once data has been located it is classified according to type and sensitivity level.
When data has been classified as sensitive, it must be tagged carefully so that access to it is restricted for protection, but also so it can be accessed easily to comply with a consumer’s ‘’right to know’’.
Why have data discovery and classification become so important?
Data discovery has at least two important purposes: it leads to the discovery of business insights and the location of sensitive data that falls under the protection of privacy laws. An organization must know where what data is located to be able to protect it.
Among other requirements, privacy laws require organizations to disclose their data collection and the purpose for data processing. It is impossible to provide this information without being extremely well organized in terms of data collection and data processing.
Data discovery enables organizations to locate data that is sensitive and at risk. Data classification enables organizations to properly tag sensitive data in order to protect it.
Apart from being a prerequisite to data classification, data discovery holds the following benefits for organizations: it improves operations and efficiency, helps with regulatory compliance, reduces risk, and increases revenue through meaningful business insights.
Organizations are realizing the importance and need for data discovery. According to a recent report, the global data discovery market size will double from $7.0 billion in 2020 to $14.4 billion in 2025.
Various factors are driving the growth of this market: the increasing need to discover sensitive structured and unstructured data, the need to ensure data privacy and protection, and the adoption of cloud-based data discovery solutions.
Data discovery is difficult and it’s getting increasingly challenging. Organizations are generating vast amounts of data, much of it sensitive. The discovery of sensitive data is the basis of a data security plan, however, as the report points out, due to the rapid adoption of cloud solutions and the rise of remote workers, organizations are now faced with sensitive data originating from sources outside the enterprise.
Another challenge is so-called ‘’dark data’’. This is data that is unused and unknown. If you keep in mind that an estimated 55% of an organization’s data is dark data, the scale of the problem becomes clear. Most companies (85%) indicated that they are not using their dark data, because they do not have the tools to find, capture, or analyze it.
Dark data constitutes data that companies have captured but don’t know how to use, and data that they are not sure that they in fact have.
Dark data could hide sensitive customer data that was not captured or saved correctly. If data like this is not discovered and classified, an organization can’t protect it properly. This scenario has two negative consequences: unprotected data is vulnerable to attack plus the business can’t be sure that it’s compliant because it doesn’t know where all its sensitive data is stored.
Data discovery automation
The sheer amount of data produced and collected by organizations make manual data discovery virtually impossible. Instead, companies must look to automating their data discovery efforts.
Data discovery automation can reveal exactly what data a company has and where it is located.
The right tool can help data teams to find personal data and other sensitive or at-risk data across a business, and help teams to understand which privacy regulations apply.
These tools can scour all digital environments to find where data is stored, such as local or cloud servers, and locate all possible data sources.
Leveraging automated data discovery will ensure that sensitive data doesn’t go undiscovered. This is important, because a company can’t protect sensitive data that it isn’t aware of.
Techopedia defines data classification as ‘’the process of sorting and categorizing data into various types, forms or any other distinct class.’’
Data is classified according to different sensitivity levels.
High sensitivity data. This data must be protected at all costs. Examples are personal information that can identify an individual, financial records or intellectual property. When this type of data is stolen or destroyed, it can have destructive consequences for an individual or an organization.
Medium sensitivity data. This data originates from communications within an organization and doesn’t contain any confidential information. Should this information be leaked, it won’t destroy the organization or an individual in the organization.
Low sensitivity data. This is data that is not regarded as confidential for example, website and social media content.
Data classification types
Data classification can be performed based on content, context, or user decisions.
Content-based classification inspects files to determine if they contain sensitive information.
Context-based classification looks at the application used to create the file, the person who created the file, and where the files were created or worked on.
User-based classification happens when an individual who works with a document determines the sensitivity level of the document.
Data classification is an ongoing process. From time to time, it’s necessary to reevaluate the classification of data to ensure that the designated classification still applies. Over time, the classification might have become inappropriate for several reasons.
The evaluation should be done by a data steward who determines whether the existing classification is still valid and if security protocols could stay the same or should be changed.
Data classification benefits
Data classification is immensely beneficial for organizations. Data classification promotes
effective and accurate management of data
fast and secure access and sharing of information
compliance to GDPR, HIPAA, FERPA, and other data protection regulations
accurate identification of sensitive data, correct labeling of this data, and safe access to it
cybersecurity through proper management of sensitive data
The data classification market
forecasts that the global data classification market size will grow from $ 536 million in 2018 to $ 1,661 million by 2023. The market growth is due to several factors, including the need for regulatory compliance, growth in uncontrolled data volumes, and increased security risks.
The data classification market is serviced by integrated and standalone solutions. Data solution providers automate the data classification process, which helps organizations to manage their data better.
Automating data classification
Automating data classification avoids the limitations of manual data classification. Manual classification typically suffers from human error (failing to tag or using the wrong tag), inconsistency (different people using different classifying standards, and neglect (failing to reclassify regularly).
A data classification platform can quickly find personally identifiable information (PII) when needed. Also, these systems take care of reclassification of data as the circumstances around data change.
Automation tools can automatically locate and identify sensitive data, allowing companies to know where their data exists and how sensitive it is so they can effectively protect their most sensitive data.
Insight into why so many companies struggle with data discovery and classification
Despite the obvious need for data discovery and classification, many companies are struggling with it or not doing it at all. Digital Guardian spoke to a panel of experts on the issue, and we note Steve Dickson of answer here.
Dickson highlights three main challenges related to data discovery and classification.
Data classification policies are complex to understand and implement.
Data doesn’t remain stagnant – it changes in volume and sensitivity levels.
Once data discovery and classification have been completed, it can create a false sense of security that data is now fully protected.
To avoid these pitfalls, Dickson recommends that organizations:
Create an uncomplicated data classification policy. It should be short and to the point and set out objectives, workflows, and data owners. All employees who work with sensitive data should read and understand the policy.
Remain aware that data changes over time and make allowance for that by having procedures and controls in place for when there’s a change in data volumes or sensitivity.
Invest in doing data discovery and classification regularly.
Invest in data discovery and classification training to ensure that data remains secure and the company is compliant.
Do data discovery and classification in tandem with other security practices like risk assessment.
Data is a valuable asset but only if it is secure and complies with data privacy regulations. Data discovery and classification are two steps towards data security and privacy.
However, data doesn’t allow itself to be easily discovered. That is because data is disparate and not easily unified across different platforms and formats. It’s a difficult and time-consuming process.
On top of that, data privacy regulations are manifold, complicated and change constantly. It’s difficult to be sure that you are compliant in every respect.
Another factor that complicates the issue is the sheer volume of data that organizations are collecting and generating. There is also a lack of data scientists and data analysts to help companies with data discovery and classification.
Organizations will have no choice but to employ and trust automated data discovery and classification tools like Datahunter in order to secure their data and be compliant.