Data discovery and data classification go hand in hand when creating your data security plan. Each function serves an essential purpose and you cannot have one without the other in order to effectively safeguard your customers’ sensitive data. The issue that many companies face—especially those on an enterprise scale—is that manual data discovery and classification wastes time, often your team’s most valuable resource. And the larger the organization, the more likely that this nuisance becomes an impossible task. This is why companies should look to intelligent SaaS tools that offer automated data discovery and classification.
Why organizations need data discovery and data classification
Before we dive into the benefits of automation, it’s essential to understand the important roles that data discovery and data classification play in your overall data security strategy.
First, there’s data discovery, which should always be an organization’s initial step in properly safeguarding sensitive data. After all, how you can protect data that you don’t know exists?
Once you find sensitive data, your team should be able to tag it properly to ensure authorized access and use. Additionally, proper classification means that your team can better find the right pieces of data to respond to a consumer’s “right to know,” or “right to be forgotten” data subject access rights (DSAR) request, which is common among data privacy regulations like the CCPA.
According to IDC’s State of Data Discovery and Cataloging report, the ability to locate, understand, access, and trust data is a key business enabler in the era of digital transformation. The report reads, “Data discovery is important for business. Period.”
Organizations surveyed said that data discovery supports these five business drivers:
- 82% Operations and efficiency
- 81% Policy compliance
- 80% Risk reduction
- 78% Regulatory compliance
- 77% Increasing revenue
The report also found that 30% to 50% of organizations are not where they want to be when it comes to data discovery. As a result, data professionals say they are wasting, on average, 30% of their time because they cannot find, protect or prepare data.
What makes data discovery and classification so challenging?
According to an IDC report on the ever-growing datasphere, it’s predicted that businesses will generate 175 zettabytes of data by 2025, at a compounded annual growth rate of 61%. A zettabyte is a trillion gigabytes—so, multiply that 175 times to gauge the scope of the challenge.
This unprecedented data sprawl means that data is stored in every nook and cranny of enterprises’ IT infrastructure. As data volume and variety continue to grow, resources become more ineffective because data and information assets are harder to find. No one knows which files contain personally identifiable information (PII), information about particular projects, intellectual property (IP), or other valuable or regulated content. As a result, organizations struggle to protect information adequately, comply with legal mandates, weed out duplicate and redundant data, and empower employees to find the content they need to do their jobs.
Based on this prediction, the report urges CEOs to act now to ensure their data strategy is focused on storing data in small sets (categories) according to business impact, rather than trying to analyze anything and everything. In other words, the more data organizations have to manage, the more data classification becomes a critical necessity. However, companies must first know what data they have and where it is stored before being able to classify and protect it.
How the issue of dark data affects data discovery
Among the mountains of data already stored and arriving daily in today’s businesses, is a secret hiding in plain sight: dark data. This is unknown and unused data, and it makes up more than half the data collected by companies, creating a considerable issue.
According to Splunk’s State of Dark Data report, 55% of all data collected by companies is dark data. Within this category lies two subcategories — data that they know has been captured but don’t know how to use, and data that they are not even certain that they have. Further, 85% of companies say they are not using dark data, because they do not have the tools to find it, capture it and analyze it.
Among the dark data could be needed customer information. Important data, such as a customer’s transaction information, could be missing location or other important metadata because that information sits somewhere else or was never captured in a useable format.
When companies do not know where all the sensitive data is stored, they cannot be confident they are complying with consumer data protection measures. At the same time, data that is misused or improperly protected makes businesses vulnerable to legal action or theft from hackers.
Due to the large amount of data that companies collect and create on a given day, manually discovering data throughout their enterprise is an insurmountable task. Instead, companies must look to automating their data discovery efforts.
Automated data discovery: how it works and how to leverage it
Today’s organizations do not have to be in the dark about their data. Intelligent automated data discovery can tell them exactly what data they have and where it resides.
Automated data discovery and inventory tools work by scanning endpoints or corporate network assets to identify resources that could contain sensitive information, such as hosts, database columns and rows, web applications, storage networks and file shares. Sophisticated systems can find data located in all file types, including .doc, .xls, .pdf, .txt, .ppt, .zip, csv, and .xml, among many others.
These tools should be able to search unstructured data, cloud repositories, endpoints and on-premise servers to find all of the locations where data can be stored. If not, companies risk sensitive data going undiscovered and unprotected.
Automated data classification: a new schema for new privacy laws
Many companies face significant challenges when it comes to data classification. A Gartner report on overcoming the pitfalls stated, “Most data classification implementations continue to be unexpectedly complex and fail to produce practical results. CISOs and information security leaders should simplify schemas, leverage tools and allow for implementation flexibility to make classification valuable for the entire organization.”
Similar to data discovery, companies face hurdles when it comes to classifying the sensitive data they are able to find. Data is inextricably linked to other pieces of data (such as a person’s name tied to an address and a purchase order) and companies need to process data for different purposes (such as marketing and shipping) in different capacities. Companies must also follow a consumer’s request to opt out of certain data processing measures but still retain that same data for legal reasons or other legitimate purposes. With large data sets, classifying data manually for any of the above scenarios would be near impossible for almost any organization.
The solution is an intelligent automated data classification platform that replaces manual processes, or even an outdated, ineffective automated process. The right automation system can aid in streamlining data classification and automatically analyzing and categorizing data in real-time, based on pre-determined parameters.
Organizations often struggle with their data classification programs because they approach them mostly as manual processes. But classifying data manually is simply too labor-intensive, time-consuming and error-prone to be a practical solution for all but the smallest companies. In particular, manual data classification suffers from the following issues:
- Inaccuracy: Busy employees often fail to classify data at all or simply pick the first tag in the list in an attempt to expedite the process. Cutting corners leads to inaccuracies and security vulnerabilities.
- Inconsistency: Different people within a team may classify similar documents in different ways.
- Inflexibility: As a company’s sensitive data requirements and policies change, team members don’t have the time or inclination to update the tags on terabytes of existing data.
- Failure: As users realize that data is not classified correctly, they will quit trusting the process and the whole project fails.
Automated data classification overcomes these limitations by making the process reliable, accurate and continuous. A sophisticated platform can, for example, spot personally identifiable information (PII) by looking for data patterns, such as names, dates of birth, addresses, phone numbers, financial information, health information and social security numbers. Automated systems can also re-classify data as needed, such as for updates and changes within the business or from changes in compliance regulations.
What to expect from a data classification automation tool
There are three general ways that companies can approach data classification: fully manual, fully automated and a hybrid of the two. However, even small organizations that haven’t considered full automation realize the potential after seeing data classification automation in action. They see that intelligent automation can eliminate the work hours and human errors inherent in manual processes.
Today’s automated data classification software and tools vary in terms of usage, access and enforcement capabilities. Common features do include:
- Pull-down menus of available data classification selections for a user or file type
- Content-aware capabilities to suggest a classification for review and change, or confirmation by the user
- Automatic selection of the appropriate classification levels based on content analysis engines
- Classification lifecycle policy enforcement, such as preventing a user action unless the file is classified, or preventing an unauthorized classification change
Some limited integrated data loss prevention (DLP) functionality, often around specific use cases, such as email
Every organization should assess automated data classification options available in the marketplace and determine which solution can provide them with the capabilities they need to take their data privacy protection to the next level. Ideally, organizations should choose a platform that is purpose-built to deliver the key functions of deploying robust data privacy programs.
How automated data discovery and classification supports cyber resilience
A study by Ponemon Institute made a strong case for the power of automation within organizations’ security programs. Yet, many organizations still have not made the leap. The study found that only 23% of organizations use automation extensively versus 77% of respondents who use automation moderately.
The report cited six benefits from using automated security tools, including these two related to data privacy and cybersecurity:
- High-automation organizations recognize the value of the privacy function in achieving cyber resilience. Moreover, high automation organizations are more likely than the overall sample to recognize the importance of aligning the privacy and cybersecurity roles in their organizations (71% vs. 62%). Most recognize that the privacy role is becoming increasingly important, especially due to the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
- High-automation organizations are more likely to say their organizations have the right number of security solutions and technologies. This can be accomplished by aligning in-house expertise to tools so that investments are leveraged properly.
Automated data discovery and classification for data privacy readiness
Data discovery and classification form the bedrock of any organization’s data privacy program because you must first know where your sensitive data resides and what levels of protection it needs. Without those two fundamental steps, your company will not be able to get a full picture of its data for proactive privacy compliance.
Data discovery and classification are so critical, they’ve made their way into the first two steps of Spirion’s data privacy management framework. This framework breaks down effective data privacy management into five steps. By using this framework, businesses can take a strategic approach to data management that not only protects consumer privacy but also increases business benefits:
When your organization handles mountains of data, trying to manually discover and classify data is impossible. Instead, teams should look to automated data discovery and classification tools for improved accuracy and a streamlined workflow that mitigates cross-department confusion and wasted hours. To see how Spirion’s Sensitive Data Platform leads in accurate, automated data discovery and classification, you can watch a free demo here.