Organizations create and collect more sensitive information than ever before—stored on and flowing between local machines, networks, mobile devices, and the cloud. With new security regulations emerging regularly, identifying and securing data at rest has become a critical concern for organizations. For security professionals, data identification represents a crucial, albeit sometimes overlooked first step in data classification and protection efforts.
What is Data Identification?
Data identification involves recognizing and distinguishing the different types of enterprise data for classification. This process aims to gain insights into specifics like the data's source, format, and purpose, which are essential for accurate data classification. Essentially, data identification means finding where your sensitive data resides, including in cloud repositories and on physical hard drives, and taking necessary steps to secure it with encryption, physical access controls, and other measures.
Sensitive data can reside anywhere within an organization. This includes local machines, networks, mobile devices, and various cloud services. New security regulations have heightened the importance of identifying and securing data at rest. As businesses create and collect more data—ranging from proprietary information to financial and personal data—the ability to identify this data is foundational to protecting it effectively.
Data Identification vs. Data Discovery
While often used synonymously, data identification and data discovery are distinct concepts. Data discovery is a broad term that encompasses the identification, analysis, and understanding of data assets within an organization's infrastructure. It involves cataloging all data assets to gain a comprehensive overview, which is crucial for their protection.
On the other hand, data identification is a fundamental component of data discovery and focuses specifically on pinpointing sensitive data so that appropriate protection measures can be applied. Data identification not only facilitates better analysis and understanding of data assets but also informs data classification. Moreover, this process helps remind employees to handle the data with care and automatically triggers existing security policies to protect it.
The Challenges of Data Identification
Data identification is often considered one of the most challenging aspects of data protection due to its complexity and the granularity that changing data privacy regulations often require. While organizations will sometimes crudely group data into broad categories because of limited labeling capabilities, they'll find that such categorization does not keep pace with their business needs, let alone changing regulations. The difficulty arises not from understanding why data identification is important but from figuring out how to do it effectively. Profiling potentially sensitive data is complicated because such data can reside anywhere and can change over time, meaning a more complex strategy leveraging context, business logic, and automation is often required.
The Role of Automation in Data Identification
Automation significantly enhances the efficiency and precision of data identification processes. Automated tools can use metadata and machine learning to facilitate the identification of sensitive data and execute necessary protective actions, such as limiting data transfer within an organization. By integrating automation, companies can continuously update their data profiles and adjust to new compliance demands without heavy manual effort.
By offering suggestions and corrections, advanced data classification tools eliminate the need for rigid data categorization methods, providing flexibility and allowing data identification to become a more essential and dynamic component of data protection strategies. This approach will also improve the effectiveness of downstream security measures like encryption and data loss prevention (DLP), making data protection more comprehensive and adaptive.
Fortra’s Data Classification Suite: Your Solution for Data Identification
Fortra’s Data Classification Suite (DCS) offers a robust solution for data identification. DCS automatically detects sensitive data in motion and at rest, leveraging machine learning to identify and protect data based on categories relevant to your organization and its classification needs. DCS integrates seamlessly with our other data protection solutions, including our data loss protection tools and cloud security solutions, ensuring up-to-date compliance with regulations like the GDPR.
Fortra's DCS enables organizations to identify, classify, and secure information across all platforms, whether at rest or in motion. It manages access to sensitive data, disposes of redundant or obsolete data, and provides deep insights through dashboards, reports, and analytics. By employing machine learning, DCS enhances data protection strategies and ensures that sensitive information is safeguarded effectively. To get started, read more about DCS's data identification capabilities or schedule a demo to see it in action.
