Learn How Data Loss Prevention and Its Working is Beneficial for You

Data Loss Prevention in Cloud Computing

It is estimated that the market of cloud Data loss prevention will grow from USD 744 Million in the year 2018 to USD 2,508 million by the year 2023 where the CAGR is of +30%. The requirement of data loss prevention in cloud computing is assumed to grow because of many factors like rapid increase in cloud storage usage, regulatory compliance achievement, and trend of BYOD technology.

Birth of Data Loss Prevention

Nowadays the data loss prevention techniques are developing in popularity, usually in field of enterprise. It is so because organizations always look for a way through which they can reduce the risk of data breaches on cloud. A simple DLP solution depends upon different important technologies, which enable complete protection of confidential files. It tries to give its 100% in securing secretive files and immediately reacting upon disaster incidents. This entire post covers all the technologies that are employed with cloud DLP solutions.

Data loss prevention techniques are defined as the technologies, which perform content inspection as well as contextual data analysis through mailing programs like email messaging, network in motion, data at rest, etc. Based upon the defined set of policy and rules, the solutions address risk of inadvertent or sudden data leakage with all the possible scenarios.

Data Loss Prevention (DLP) Categories

The cloud DLP technology is successfully be classified in two categories:

  • Enterprise DLP: The solutions under this category are comprehensive and packed in an agent product either for desktops or servers. It involves physical and virtual gadgets to monitor the email traffic and network also.
  • Integrated DLP: This is limited up to the secure email gateways, web gateways, enterprise content management, data classification tools, email encryption services, and cloud access security brokers (CASB).

Before learning the technique through which cloud DLP works, it is important to acquire difference between contextual analysis and content awareness. It is very much important for comprehending the data loss prevention techniques. Talking about a simple difference between both then, content is considered as a letter and context as an envelope. The content awareness includes noticing of the envelope and looking into it for through analysis. On the other hand, contextual analysis involves examining of header part only. The basic idea behind content awareness is that – we want to utilize content for profiting us with content but, don’t want to be blocked because of it.

Techniques For Data Loss Prevention Working

When the envelope gets opened and its processing gets started, here comes the use of several content analysis techniques. These are utilized for triggering the policy violations, which involves:

  • Regular / Rule-Based Expressions: This is the common analysis approach that comprises of an engine to analyze the content for particular rules like 9-digit US social security number, 16-digit credit card number, etc. With this technique, the rules get configured and then, processes rapidly due to which it is considered as excellent first-pass filtering.
  • Database Fingerprinting: This mechanism involves looking for an exact match from live database or database dump. These both databases affect the ongoing performance but, users can take it as an option for structured data.
  • Exact File Matching: Here, the content of file is not analyzed instead, the hashed value of files is tried to match up with the exact fingerprints. This cloud DLP technique renders low false positives but, this does not work with files for several identical versions.
  • Partial Document Matching: It searches for complete or partial matching on particular files like several editions of forms that are filled by multiple users.
  • Conceptual / Lexicon: These policies alert complete unstructured ideas, which defy a simple categorization through the combination of certain rules and dictionaries. This technique is to be customized for enhancing the provided DLP solutions.
  • Statistical Analysis: Triggers the policy violations when something goes incorrect without any prior knowledge. It requires bulk amount of data for scanning from and to reduce the negative calls.
  • Pre-built Categories: Data loss prevention techniques involve pre-built categories, which includes rules and dictionaries for all the common type of confidential files. This can be anything like PCI protection, HIPAA, etc.

Conclusion

There are numerous other techniques available in market today, which deliver several types of data loss prevention techniques. Each and every DLP vendors have programmed their own algorithms for complete data protection. Always remember one thing that data protection should be basic concern at the time of adopting cloud services.

Author Bio :

As an Author, Ugra Narayan Pandey has experience of more than 5 years and he is now working as a Cloud Security Expert & Technical Writer.