The Forrester Sensitive Data Discovery and Classification report discusses the market maturity of sensitive data discovery and classification technologies. While the technology to classify data has existed for several years, the space is undergoing an explosion in interest and technology advances recently — due to privacy regulations like GDPR and the increasing use of cloud-based services.
The market now includes large vendors with standalone offerings, many security and privacy technology offerings that incorporate sensitive data discovery and classification as a feature, and startups focusing on discovery and classification innovation.
The focus of the report surrounds the importance of sensitive data discovery and classification in securing confidential data.
The report highlights the importance of several key benefits of solutions in this space:
Forrester emphasizes the need for organizations to understand their data to protect it effectively, especially in a business environment in which data volumes grow exponentially, regulatory requirements evolve, and the scope of what constitutes sensitive data expands.
Sensitive data discovery and classification provide visibility into where sensitive data is located, identify what the sensitive data is and why it’s considered sensitive (context), and tag or label data based on its level of sensitivity. This is becoming a crucial business process, as it identifies what must be protected and facilitates the next step of enabling a data security control. Essentially, if you don’t know where your data is or whether it should be protected, how can that data be secured?
Organizations use this visibility and understanding of data to optimize data use and handling policies and identify appropriate security, privacy, and data governance controls. They may automate remediation capabilities to protect the data and surface insights that inform policy, data handling, and data lifecycle decisions.
The report underscores the significant business value of sensitive data discovery and classification solutions. These technologies introduce automation and consistency into the process of discovering and identifying sensitive data, which reduces our reliance on more error-prone approaches like employee surveys or regular expressions (regex) to find data. The level of classification helps determine the types of controls and handling policies necessary for the data. Classification labels, and in some cases employee involvement in the labeling process, also help people who use and handle data to better comprehend its value.
Sensitive data discovery and classification solutions also support compliance, privacy, and ethical data use. Meeting compliance requirements, third-party partner requirements, and internal privacy and ethical standards for data use require an understanding of what data your organization collects, processes, stores, and shares. Plus, the need to protect data goes beyond the business need, as data privacy and protection have become much more important to customers.
Finally, the report advises security and risk professionals to understand the value they can expect from a sensitive data discovery and classification vendor, learn how vendors differ, and select one based on size and market focus. It also provides a list of notable vendors based on their market size, geographic and industry focus, and type of offering.
It’s crucial to note that while most classification methods are better than having none at all, most paths to classification — like end-user, centralized and metadata-driven — can be time-consuming and ineffective.
Therefore, for best results, you should seek out solutions like Concentric that leverage sophisticated machine learning technologies to autonomously scan and categorize data — from financial data to PII/PHI/PCI to intellectual property to confidential business information – wherever it is stored. In the cloud, on-premises, structured or unstructured stores… data lives everywhere.
Concentric can autonomously identify data, learn how it’s used, and determine whether it’s at risk. Our solution empowers you to know where your data is across unstructured or structured data repositories, email/ messaging applications, cloud or on-premises – all with semantic context.
Developed over many years with a key focus on data protection, our large language models have developed a semantic understanding of data and provides a thematic category-oriented view into all sensitive data – from financial to intellectual property to business confidential to PII/PCI/PHI. Concentric allows security teams to classify data without any rules, regex patterns or end user-involvement.
Concentric’s Risk Distance analysis compares each data element to baseline security practices exhibited by semantically similar data. Risk is autonomously identified from inappropriate permissioning, risky sharing, unauthorized access, wrong location etc.
In the Forrester report, Concentric was compared with 30 other vendors in the category. Only Concentric and Proofpoint checked all five boxes for the 5 key use cases: cloud migration, government-related compliance, insider risk detection and response, post-breach investigation, and data loss prevention.
Want to see firsthand — with your own data — how you can quickly and easily deploy Concentric AI to classify your data without rules, regex, or end-user involvement? Book a demo today.
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