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    Data Loss Prevention: Promoting Data Security

    Data loss prevention (DLP) refers to a combination of practices and software for preventing data loss that ensure that an entity’s sensitive or essential data remains accessible to approved users and is not exchanged with or available to unauthorized users. And with many organizations building their entire business model around data collection and analysis, businesses need to implement a robust data defense to suit its increasing significance.

    How Does Data Loss Prevention Work?

    The operation of data loss prevention software is focused on an imperative pair of directives: defining sensitive data that must be protected and preventing its loss. Identifying sensitive data can be challenging as data can exist in several different states in your infrastructure:

    Data in use: It refers to active data in RAM, cache memory, or CPU registers.

    Data in motion: It refers to data transmitted either internally or through the public Internet via a network.

    Data at rest: It refers to data stored in a file system, a database, or some form of backup storage infrastructure.

    Enterprise DLP solutions are all-encompassing data-protection tools, while integrated DLP solutions focus on one state or can be integrated into a single-purpose, separate instrument. To search through data, DLP solutions deploy agent programs. To segregate data that is sensitive or deserves protection, these programs use a number of DLP methods.

    Rule-Based Matching: To find data that matches specific rules, agents use known patterns. 9-digit numbers, for instance, are typically social security numbers.

    Database Fingerprinting or Exact Data Matching: Agents check for exact matches to pre-supplied structured data.

    Exact File Matching: Agents search for records based on their hashes rather than their contents.

    Partial Document Matching: Agents search for files that partially meet pre-supplied patterns.

    Statistical Analysis: Some data loss prevention systems use machine learning or Bayesian analysis to try to identify sensitive data. You’d need a huge volume of data to train the system, which would also be vulnerable to false positives and negatives.

    Data Loss Prevention: The Advantages

    1. Protecting personally identifiable information and maintaining compliance. Many organizations have large databases full of highly confidential contact information of customers and businesses, ranging from email addresses to financial and medical records that could cause significant harm if they fell into the wrong hands. You need to make sure that the information stays safe not only because it’s the right thing to do, but because a host of laws, from HIPAA to GDPR to CCPA, require you to do it and mandate some of the ways you need to do it.

    2.Offering visibility into your data. It is part of the process of locking down the data to find out where the data resides in your infrastructure and how it travels around. In the era of public and hybrid clouds, this can be difficult, and software for data loss prevention provides the added benefit of offering you a large-scale view into your own data infrastructure.

    3. Safeguarding intellectual property. DLP attempts to avoid online pilferage or accidental leakage of information by corporate espionage.

    Conclusion

    The upsurge in the frequency and severity of data breaches poses significant threats. Compliance standards play a significant role, as does the increasing power and responsibility of CISOs in promoting data security. They connect with CEOs and other organizational leaders on a daily basis and add visibility to security issues such as data protection. Besides, many DLP offers are hosted services, making them appealing to organizations that do not have the in-house staff to build and enforce their own DLP policies.

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