Data loss prevention: Know the basics

Data loss prevention is a cybersecurity approach that entails creating safe data processes to minimize accidental disclosure. A complete data loss prevention (DLP) plan requires effective data leak prevention. Data leaks are a simple avenue for cybercriminals to exploit. Exposed data, such as leaked credentials, enables illegal access to an organization's computer systems.

This is why it’s crucial to avoid this type of cyberattack in order to protect information and ensure no private data gets exposed. In this article, you’ll learn the basics of data leak prevention and how it works.

What is a data leak?

In order to talk about data loss prevention, it’s important to know about data leaks.

A data leak is the inadvertent disclosure of confidential information, either electronically or physically. Internal or external equipment, such as external hard drives or laptops, might be the source of data breaches. If a cybercriminal discovers a data leak, the knowledge might be used to prepare for a data breach assault.

What is Data Loss Prevention (DLP)?

Data Loss Prevention is a set of tools that are used to ensure no sensitive data is lost or accessed by users that are not authorized.

These technologies perform both content inspection and contextual analysis of data sent via messaging applications such as email and instant messaging, file servers or cloud applications, and cloud storage. Their main goal is to avoid any kind of risk associated with accidental leaks.

There are two types of data loss prevention technologies. First, the Enterprise, which is packaged with agent software for desktops and servers, physical and virtual appliances for monitoring networks and email traffic, or virtual appliances for data discovery. Then, the Integrated DLP technology, which is limited to secure web gateways (SWGs), secure email gateways (SEGs), email encryption products, enterprise content management (ECM) platforms, data classification tools, and data discovery tools (CASBs).

How Data Loss Prevention works

Understanding the distinctions between content awareness and contextual analysis is necessary for fully comprehending any DLP solution.

Let’s give a more simple point of view: if the content were a letter, the context would be the envelope. While content awareness requires collecting the envelope and analyzing its information, the context includes exterior characteristics such as the letter's header, size, format, etc. The concept behind content awareness is that we want to be open to a particular context, even while we want to use context to obtain additional information about the content.

So, what happens after the envelope has been opened and the content is analyzed? When it comes to data loss prevention, it’s crucial to use content analysis techniques to identify policy violations, including:

Ruled base/ regular experience

The most prevalent analysis method employed by DLP comprises an engine examining content for specified rules, such as 16-digit credit card numbers, 9-digit U.S. social security numbers, etc. This approach is an ideal first-pass filter since the rules can be created and executed fast.

Database fingerprinting

This method, also known as Exact Data Matching, examines exact matches from a database dump or live database. Although database dumps and active database connections impact speed, organized data from databases may be extracted in this manner.

Exact file matching

In this case, the contents of files are not examined, but their hashes are compared to precise fingerprints. This method yields few false positives but could be more effective for files with several similar but not identical versions.

Partial document matching

This method examines specific files for a complete or partial match, such as numerous copies of a form filled out by various people.

Statistical analysis

In this case, machine learning is utilized to identify policy violations in encrypted content. The more data must be scanned, the better, or else false positives and negatives are likely.

Pre-built categories

Typical forms of sensitive data, such as credit card numbers/PCI protection, HIPAA, etc., are categorized using pre-built rules and dictionaries.

How can we prevent data leaks?

How information is managed will vary among sectors, companies, and individuals. You must comply with some broad principles in a regulated business, such as PCI DSS, HIPAA, or FERPA.

However, it is ultimately the responsibility of your firm and its employers to adhere to preventative and safety requirements daily. Simply said, most data leaks are operational issues, not typical cybersecurity issues. Cybercriminals do not cause data leaks, but they can take advantage of them.

The following are the three standard methods for preventing data breaches

Validate cloud storage configurations

As cloud storage becomes more prevalent, the data moving in and out of cloud storage grows tremendously. Sensitive information may be exposed in an unprotected bucket if the appropriate procedures are not followed. This is why cloud storage configurations must be evaluated before deployment and when they host critical data. Continuous validations reduce the danger of data exposure and can even inform you proactively if unauthorized access happens.

Automate process controls

At a sufficiently bigger scale, validation becomes challenging because computers are vastly superior to humans in maintaining regularity. This is why automated process controls should serve as executable documentation to verify that all cloud storage is protected.

Monitor third-party risk

Your vendors have the same access to the information that you have. Even if you do not reveal your client's data, you will still be held liable for the data breach in the eyes of your consumers and, frequently, the government. This makes analyzing third-party risk, fourth-party risk, and cyber security risk assessments equally as vital as managing information risk and cyber security internally.