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Data Discovery

What is Data Discovery?

Data discovery is the process of identifying and exploring data within an organization. It is used to better understand the data’s meaning and potential uses. The core processes of data discovery involve analyzing and visualizing data from various sources to identify patterns, trends, and relationships to gain insights and inform decision-making.

How Data Discovery Works

Data discovery aims to help organizations understand their data better. With it, they can make informed decisions based on accurate and relevant information. This process typically involves data profiling, exploration, and visualization techniques. These help users understand the data’s structure, content, and quality.

Data discovery plays a pivotal role in enabling organizations to grasp the nuances of their expansive datasets. This invaluable tool aids businesses in making informed decisions anchored in accurate and pertinent data. The typical data discovery process leverages data profiling, exploration, and visualization methodologies. These techniques illuminate the data’s structure, essence, and quality, providing users with a comprehensive understanding. Contemporary organizations frequently wrestle with data sprawl, exacerbated by ceaseless data accumulation and a palpable absence of visibility in their data repositories. 

The “Shadow Data” phenomenon is a significant concern within this context. Shadow data refers to information outside the official channels, often overlooked due to its obscured nature, making it a prime candidate for data discovery efforts. Data discovery efforts help to uncover these hidden repositories, allowing them to be managed. 

Data management and discovery become even more challenging for organizations embracing cloud environments. The adoption of microservices and the complex tapestry of multi-cloud frameworks exacerbate the challenge, as they preclude a unified visibility point, underscoring the pressing need for enhanced data discovery solutions.

Data discovery tools and techniques get consumed by data analysts, business analysts, and other stakeholders. They use it to explore and understand data, uncover hidden insights, and make data-driven decisions. These tools can include data profiling software, visualization tools, and analytics platforms that allow users to explore and analyze data in real-time. As the data flow is neverending, automated tooling is crucial for organizations to stay on top of the evolving landscape, adapting their security posture to address the change. Without automated tooling, the information gathered would rapidly become stale, exposing the organization. 

Data Discovery Must Include Classification To Effectively Protect It

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Benefits of Data Discovery

Organizations are inundated with data from many sources, heightening the complexity of understanding and accurately classifying it. It’s not merely about distinguishing between diverse data types; it’s crucial to fathom the intricacies of their origins, interrelationships, and associated risks. Every dataset, from customer details and internal communications to proprietary algorithms, necessitates specific security measures based on inherent risks and value. While correct classification is paramount to safeguarding sensitive data, the sheer volume and the evolving digital landscape amplify the challenges. The advent of remote work, multi-cloud strategies, and the proliferation of Internet of Things (IoT) devices further obscure the boundaries of data storage and transfer. Ensuring it has the appropriate protections is virtually impossible without understanding what data exists and its proper classification.

  • Better decision-making: Data discovery helps organizations make better, data-driven decisions by providing insights into their data. By uncovering patterns, trends, and relationships in the data, organizations can make informed decisions based on accurate and relevant information.
  • Maintaining Compliance: Data discovery helps identify various types of sensitive data controlled by legal or regulatory frameworks. This data may have stringent requirements for how it is protected and shared, with significant consequences if not handled properly. 
  • Improved data quality: Data discovery can help identify data quality issues, such as missing or inconsistent data. By addressing these issues, organizations can improve the overall quality of their data, improving the accuracy of their decisions.
  • Increased efficiency: Data discovery can help organizations save time and resources by quickly accessing the needed data. This eliminates the need for manual data exploration and analysis, which can be time-consuming and prone to errors.
  • Competitive advantage: Organizations that can effectively leverage their data through data discovery have a competitive advantage over those that don’t. Organizations can stay ahead of the competition by using data to make informed decisions and identify opportunities.

Reasons for Data Discovery

Modern enterprises collect and process vast amounts of information while doing business. This data may originate from external parties such as vendors or customers, but it may also be generated throughout the course of operations. Organizations need a complete understanding of where their data resides and what is contained in it to avoid exposing themselves.

  • Data classification: Data discovery can help organizations classify their data based on sensitivity and criticality. This can help organizations apply appropriate security controls and comply with data protection regulations.
  • Access control: Data discovery can help organizations identify who has access to what data. Knowing this they can ensure that access is appropriate and in compliance with regulations.
  • Privacy compliance: Data discovery can help organizations identify personal data and ensure its protection aligns with privacy regulations such as GDPR, CCPA, and HIPAA.
  • Threat detection: Data discovery can help organizations identify potential security threats by monitoring data access and usage patterns. This can help organizations detect and respond to security incidents before they cause significant damage.
  • Audit and compliance reporting: Data discovery can help organizations generate audit reports and compliance documentation to demonstrate compliance with regulations such as PCI-DSS, SOX, and FISMA.
  • Data retention and disposal: Data discovery can help organizations identify data that has exceeded its retention period and should be disposed of in compliance with regulations.

Efficient Data Management

Manual processes for data discovery and data analysis are only sufficient for the smallest of organizations. As organizations grow, the volume of data to locate and analyze rapidly outgrows what can be discovered through manual assessment.

Automated data discovery solutions are necessary to analyze modern enterprises. Basic tools will be able to discover and analyze data in expected data storage locations such as databases and shared storage. Discovering all data organizations have stored in the cloud or Shadow IT requires more advanced tooling.

Shadow IT encompasses all unknown systems and services that may be created temporarily to accomplish an IT goal but linger well beyond their intended purpose. These systems often house sensitive information yet are poorly maintained.

Modern Enterprises Store Data With Multiple Cloud Providers Complicating Data Discovery

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Cloud resources are another challenge for automated tools, as many discovery tools are designed for on-premises. Advanced discovery tools can analyze all cloud providers used by an organization to discover the locations where data resides and classify it by what it contains, allowing teams to decide if it should remain in that location or if new security controls are required to protect it.

Dig Security Automates Data Discovery

Dig is more than just a data discovery platform; it provides Data Security Posture Management (DSPM) together with Data Detection and Response (DDR).

The DSPM component of Dig Security’s platform utilizes data discovery to identify the content and context of data stored in the cloud and prioritize risks through classification and static risk analysis. Organizations can better understand their multi-cloud environment and identify potential data loss risks by setting a security baseline.

The DDR component complements DSPM by adding dynamic monitoring capabilities to detect unusual patterns of data interaction and identify potential security threats in real time. An advanced threat model is used to identify anomalous user behavior and suspicious data interaction patterns to rapidly recognize changes that could indicate data in danger. 

By unifying static and dynamic monitoring, Dig Security’s platform reduces the chance of and minimizes the impact of data breaches. It enhances existing security controls to help organizations protect sensitive data and prevent potential breaches or ransomware attacks. With its advanced technologies, Dig Security’s platform provides significant advantages over traditional security solutions while reducing the burden on IT and security teams.