Why Monitoring Dynamic Data is Crucial for Modern Data Security

Enterprise data exists in three main states: at rest, in transit, and in use. Data at rest refers to data stored on devices or servers, while data in transit is moving across networks. Both are relatively easier to protect with encryption and access control. However, data in use, which is actively being processed or accessed, presents significant challenges. Unlike data at rest or in transit, dynamic data is vulnerable to insider threats, unauthorized access, and accidental exposure during processing. Securing data in use requires real-time monitoring, context-aware protection, and proactive risk detection, which traditional tools often fail to address.

Why Data Security Teams Must Monitor Dynamic Data

Enterprise data security personnel monitoring dynamic data is crucial for several reasons:

Increased Data Mobility and Expanded Security Boundary

With the globalization of business and digital transformation, enterprise data is no longer limited to internal networks, but widely flows to cloud platforms and multi-cloud environments. This allows large amounts of sensitive data to transcend traditional security boundaries, exposing enterprises to greater risks and complexities.

Sensitive data or code can be uploaded to an uncontrolled internet environment due to factors such as surging demand for remote work, new attack techniques, intrusions by advanced persistent threat (APT) groups, and weak employee security awareness, creating new data breach risks.

Frequent data breaches and stricter compliance requirements

In recent years, the proliferation of enterprise security vulnerabilities and the tightening of data compliance regulations have made data security a top priority for enterprises.

Governments around the world have taken data security issues seriously and have introduced laws and regulations related to data and personal information, which means that "data security" has evolved from a "mandatory" requirement for enterprises to an "optional for those who can afford it".

Responding to Insider Threats and Timely Stop Loss

How to orderly regulate the dissemination of sensitive data on the Internet without affecting employee productivity and respond to potential data leakage risks in a timely manner is the main problem faced by medium and large enterprises.

Monitoring dynamic data can continuously track the movement of data, so that when a data breach occurs, the system can respond in a timely manner, trace the source, prevent proliferation, and assist managers in monitoring the use of sensitive data within the enterprise to ensure its compliance and appropriate use.

Comprehensive egress data transmission channel monitoring ensures the effectiveness of data transfer policies and user activity audits, even in remote work environments.

Challenges in Protecting Dynamic Data with Traditional Security Solutions

Lack of Real-Time Monitoring and Response Mechanisms

For risk endpoints within the enterprise, such as employees downloading software from unknown sources at will, even if there is a policy prohibition, it is often difficult to strictly enforce due to the lack of real-time monitoring and response mechanisms, making the most insecure endpoints a weak link for attackers to exploit.

Fragmented security defense systems and data silos

Traditionally, data, network, and endpoint security have been managed independently by disparate teams, and this isolation has led to fragmented defenses and inefficiencies.

Data barriers between security products of different brands make it difficult for enterprises to trace and manage all logs uniformly when security incidents occur, which can slow down response speed, reduce operational efficiency, and even affect employee user experience.

Content Identification Limitations

Traditional unstructured data recognition methods, such as keyword-based or regular expressions, can only perform surface-level text matching and cannot deeply understand text semantics and contextual relationships.

This means that traditional products struggle to accurately identify sensitive information or specific data patterns without clear keywords, leading to high false positives or false positives.

Difficulty in dealing with complex data flow scenarios

In complex data breaches, data can take many forms, such as device-to-device transfers, modification of file names and extensions, multiple replications, compression and encryption, and screenshots.

Traditional products are difficult to track the full chain of data from download, local processing to outbound transmission, so it is impossible to accurately capture and record all data transmission, modification or transformation processes in the internal system.

How CyberServal DDR solves the challenges of dynamic data monitoring

Traditional data loss prevention (DLP) solutions often face multiple challenges in protecting data in motion, especially in real-time monitoring, data flow, and identification capabilities in complex scenarios. As a next-generation data loss prevention solution, CyberServal DDR combines advanced technologies such as real-time behavior monitoring, AI-based semantic analysis, and full-chain data flow tracking to provide more accurate and efficient protection against problems that cannot be solved by traditional DLP. The following are the technical advantages of CyberServal DDR in dynamic data monitoring, which can more comprehensively address the complex security challenges of dynamic data.

Advanced DLP Solutions for lack of real-time monitoring and response mechanisms

Traditional security solutions lack effective real-time monitoring and response mechanisms, making it impossible for enterprises to promptly detect and respond to internal risks, such as employees downloading unknown software at will. This makes some potential security risks a weak link for attackers to exploit. CyberServal DDR continuously monitors user behavior on endpoints, analyzes anomalous activity, and makes risk decisions in real-time. Its trust algorithms quickly identify and respond to suspicious behavior, enhancing data protection strategies. In addition, DDR's desktop software management function ensures that all software used by employees meets company security requirements, effectively preventing the download and use of unauthorized software.

Advanced DLP Solutions for Fragmented Security Defenses and Data Silo

Traditional enterprises usually adopt independent protection systems for data, network, and endpoint security management, resulting in fragmented defense. This isolation creates data barriers between different security products, inefficient defense, and difficulty in tracing logs when security events occur, resulting in slower response speed. CyberServal DDR breaks down this silo effect by integrating modules such as data leak prevention (DLP), network management, and desktop management to form a unified endpoint security platform. By building a centralized management system, DDR improves cross-team collaboration efficiency and overall protection capabilities, ensuring rapid response and resolution of security incidents.

Advanced DLP Solutions for Content Recognition Limitations

Traditional unstructured data recognition methods, such as matching based on keywords or regular expressions, usually only stop at surface text processing and cannot deeply analyze the semantics and context of the data, making it difficult to accurately identify sensitive information and prone to false positives or omissions. CyberServal DDR combines a large language model (LLM)-based AI content insight engine to deeply understand the semantic hierarchy of data and identify data patterns without clear keywords. The engine not only accurately identifies sensitive information but also flexibly adjusts the identification strategy according to the context of the text, effectively reducing false positives and false positives, thereby improving the accuracy and efficiency of data protection.

Advanced DLP Solutions for Difficult-to-handle complex data flow scenarios

In modern enterprises, data flows are often complex and diverse, involving various forms such as device-to-device transfer, file modification, compression, encryption, and screenshots. Traditional data protection products are difficult to achieve comprehensive monitoring of the entire data flow process, and cannot accurately capture and record the transmission, modification, and transfer of data within the enterprise. CyberServal DDR provides full-chain data flow tracking capabilities, which can comprehensively monitor the entire process of data from download to outreach. With kernel-driven monitoring and application-layer transport monitoring, DDR can identify potential risks in data flow and use anti-remake watermarking technology to ensure traceability in the event of a data breach.

Securing data across its various states—at rest, in transit, and in use—requires advanced DLP solutions like CyberServal DDR, which ensures continuous monitoring and real-time response. To stay ahead of evolving threats, it’s crucial to leverage next-gen security technologies powered by AI. For more insights into enhancing your data protection strategy, be sure to visit our website for the latest updates and download our comprehensive whitepapers. Stay informed and secure your data effectively.