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idden data threats: Why businesses must pay attention to every step of the data flow
In the digital age, enterprises are increasingly dependent on data, but the risk of data leakage is also rising. Data loss prevention (DLP) software has become an important tool for enterprises to protect sensitive information. This technology can monitor, detect and prevent potential data leaks during data use, data transportation and data storage. However, many businesses often overlook every step of the data flow when using these tools, which can lead to tricks and hidden vulnerabilities.
Data breaches often occur when sensitive information is shared inappropriately or inadvertently.
The risk of data leakage can occur in any link, from data creation to circulation to final storage. Therefore, enterprises need to have a comprehensive grasp of the entire data flow process. The functions provided by the DLP system include automatic detection, instant feedback and risk assessment, allowing system administrators to quickly identify which parts have security vulnerabilities.
Standard security measures and advanced measures
Standard security measures adopted by enterprises, such as firewalls, intrusion detection systems (IDS) and anti-virus software, are designed to combat malicious external and internal attacks. Although these measures are effective, as technology develops, relying solely on these standard measures is no longer enough.
As enterprise data environments become more complex, advanced security measures are receiving increasing attention. Technologies such as machine learning and behavioral analysis will be able to effectively detect abnormal behaviors.
Advanced security measures use machine learning algorithms to monitor data access anomalies and use user behavior analysis to detect potential data leakage. In addition, these technologies can provide internal and external communication monitoring on the client and server sides to ensure the security of sensitive information.
Specified DLP system
Designated DLP systems focus on detecting and blocking unauthorized data transfers. These systems use various methods to mark sensitive data to ensure that only authorized users have access to relevant information. Key to the success of this technology is accurately classifying information and establishing policies to limit the flow of data.
The data identification process is the core of DLP, and companies need to know what content they want to protect.
Different types in the data flow process
Data in motion
Data in motion refers to data that is being transmitted over the network. This type of data requires special attention because it is vulnerable to attacks as it passes through numerous states. Installing detection systems at data egress points to analyze data packets for potential leaks can significantly reduce risks.
Data in use
Data in use refers to the data that the user is interacting with. DLP systems can monitor and warn against interactions with sensitive data, such as screenshots, copies, prints, and faxes. This effectively prevents inadvertent leaks from occurring.
Data at rest
Data at rest, that is, data that has not been used or moved, is usually stored in a database or file share. Protection of this data at rest requires access controls, data encryption, and clear data retention policies.
Conclusion
Facing ever-evolving data threats, enterprises must recognize that every step of the data flow can become a potential source of information leakage. Strengthening data protection and monitoring is not only a technical requirement, but also a necessary measure to maintain corporate reputation and credibility. Threats hidden behind data flows are waiting to be discovered. Are you ready to be alert to these threats?
