As modern data stacks become more complex, data teams need more efficient ways to manage sensitive data. When more data sources are added or more platforms leveraged for storage and analysis, understanding what kind of data lives in your data ecosystem can become increasingly difficult. This difficulty becomes more of a problem when much of the data involved is sensitive, and the consequences of losing or leaking that data are very high-risk.
Through the implementation of efficient and effective sensitive data discovery (SDD) tools, data teams can ensure that the appropriate tabs are kept on their sensitive information as their organization continues to grow and scale data use.
Sensitive data discovery is the practice of locating and identifying the sensitive information in a data set in order to prevent it from falling victim to unauthorized access. This can include personally identifiable information (PII) such as Social Security numbers, email addresses, passwords, names and addresses, birth dates, and more.
Protected health information (PHI) is another relevant form of sensitive data. This information includes an individual’s health information, such as demographic data, medical histories, test results, insurance information, and other data used to identify a patient or provide healthcare services or coverage. Commercially sensitive data can also be considered sensitive, encompassing information like trade secrets, profit gain or loss, new ideas, and confidentiality agreements.
With new data created and added into data ecosystems constantly, sensitive data discovery is essential for contemporary data teams. It is critical that organizations build a robust and secure foundation to protect this sensitive data and maintain the security of consumer and company-protected information. By identifying this sensitive information, groups can more effectively prevent outcomes like data loss, data breaches, and regulatory violations.
When new data sources are added to an existing platform, SDD tools scan for sensitive information that needs to be protected against exposure. Once this information is identified through SDD, the data is then classified based on a range of prebuilt and/or domain-specific classifiers that note data type, level of sensitivity, and more. Beyond this, data can then be tagged within the platform so that it is recognized system-wide as sensitive and can therefore become subject to specific access policies.
When data teams proactively detect sensitive data as it enters the ecosystem, the correct policies and procedures can be created and applied in order to keep it protected. Sensitive data discovery is therefore not the same as regular “data discovery,” which is simply the process of data consumers finding the data they need. This process is aimed at identifying the information that puts those generating the data most at risk, and subsequently creating the proper policy framework to stop unauthorized access before it can even happen.
These sensitive data discovery tools can be integrated with leading cloud databases such as Snowflake and Databricks to better manage all parts of the data storage and analysis process, including organization, structure, metadata, file size, compression, and statistics. The tool also allows the data to be tagged so that the subject matter expert can verify classifiers and apply appropriate policies.
For a variety of reasons, sensitive data discovery tools are essential to any business that handles sensitive information.
On one hand, these tools dramatically reduce manual operations required of data teams. They can eliminate the need for manual inspection of sensitive information, which is always subject to human error. This also allows organizations to enforce their privacy, data use, and protection policies and procedures more readily once the information is identified.
These tools can limit regulatory and legal exposure by enabling an organization to understand what data they are housing, where it is housed, and who has access. This eases the ability to protect such data and keep practices and operations in compliance with regulations. In turn, this helps groups avoid costly reputation-related or monetary penalties resulting from data breaches or non-compliance. Sensitive data discovery tools also allow companies to keep up with the rapidly changing world of data, giving insight into data that is added or created within the company’s systems in real-time.
When choosing a sensitive data discovery tool, it is essential to look for those that integrate with leading cloud providers. This allows for the seamless management of all your data across a range of platforms, and prevents the need for manual tracking and analysis.
Automation is also a vital characteristic. The fewer manual touches required by your data teams, the better and more scalable your model will be. You want a system that can automatically provide accurate identification, classification, and policy enforcement on any data stored in all systems and networks. Systems that have pre-built criteria, in addition to customizable classifications, are critical timesavers and limit errors.
Additionally, search for tools with the ability to extract metadata from existing catalogs to ease the implementation and enforcement of policies. It’s important to leverage what you have, and finding a tool that can refer to and utilize existing metadata stores makes identification processes much more efficient.
In the rapidly developing field of data storage and analysis, there are a range of platforms that offer sensitive data discovery capabilities for their users. A few of the most relevant options for SDD enablement include:
Immuta’s sensitive data discovery tool leverages pre-built classifiers to automatically scan for the presence of sensitive data as new information is added to a data ecosystem. By creating standardized tagging across platforms, this tool allows multiple team members to easily inspect and manage data through custom workflows. With catalog integration, this tool enables data owners to author policies that reference existing metadata from platforms like Alation and Collibra without needing to manage policy metadata in multiple places.
Spirion systems allow users to find all structured and unstructured data across all company networks, cloud systems, remote file servers, and endpoints. Tools focus on the preservation of intellectual property and customer-sensitive data. It also allows companies to assess their sensitive data footprint by profiling registered tags for elements such as PII, PHI, or other sensitive data.
OneTrust is a unified data discovery tool that allows users to automatically discover data across their entire IT infrastructure with scans of actual data, including cloud, on-premises, and legacy systems. The tool captures and catalogs metadata to enable management of data retention, access, protection, and governance.
Sensitive data discovery tools are integral for any organization that handles data, from financial and healthcare institutions to retail stores and entrepreneurs of all sizes. Keeping sensitive data safe is a must to maintain positive relationships with customers and compliance with regulations.
Utilizing sensitive data discovery tools help reduce risk by classifying data and tracking who has access, allowing companies to better manage data and avoid costly and embarrassing breaches. Companies should be careful in assessing the tool they choose and find one that would fit their application and needs best, being mindful to look for tools that can look at data in real-time, minimize manual intervention, and assign responsibility.
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