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Data loss prevention (DLP) is a very critical tool for modern businesses.
Without a DLP solution, businesses can face data leakage, reputational risk and may result in significant fines or even criminal charges.
Additionally, it may have a detrimental impact on an organization’s operations and possibly cause it to fail.
According to a 2019 National Cyber Security Alliance research based on a Zogby Analytics survey of 1,006 small businesses with up to 500 workers, 10% of organizations went out of business because they don’t have strong DLP solutions.
25% declared bankruptcy, and 37% suffered financial losses as a result of network security breaches.
What is Data Loss Prevention (DLP)?
Data loss prevention (DLP) is a method for identifying and stopping data loss or exfiltration. To find information leaks or losses, many DLP solutions examine internal “endpoint” devices and network security traffic.
In order to maintain compliance with industry and data privacy standards, organizations utilize DLP to safeguard their personally identifiable information (PII) and private business information.
Enterprise data loss prevention is a network security strategy that makes certain that your private or sensitive information doesn’t leak outside of your company network security in a way that is dangerous or against the law.
Not only the data dispersed widely, but the users’ and employees access behaviors also pose a number of network security risks since they access company data from various locations and through a variety of computer platforms, devices, and mobile technologies.
The data network security faces some very difficult difficulties only from the combination of these variables.
For instance,
- The business might not have a clear understanding of who, what, where, or how employees to access, utilize, move, and share the data.
- There may be network security vulnerabilities since the firm may only have a limited amount of control over the data that is kept in the cloud.
- Due to the various security capabilities of public and private cloud providers, network security, SaaS, etc., the firm may experience inconsistent data loss prevention.
- The business may be dealing with an increase in network security breaches and insider threats brought on by well-intentioned staff members, nefarious insiders, and/or external cybercriminals.
What Types of Data Loss Protection Are There?
The term “DLP” refers to several data network security methods. They consist of the following:
1. Identification of data
Only when it is taught what sensitive information is and is not, is DLP beneficial.
Instead of leaving it up to people, businesses should utilize an automated data discovery and data loss prevention solution to guarantee accurate and trustworthy identification of data.
2. Securing moving data
Internal data transfers occur often, and external network security breaches frequently use these transfers to redirect data.
Data loss prevention software can help to ensure that data in motion is not transferred in an unauthorized route.
3. Protecting stored data
When data is not in motion, such as when it is stored in network security databases, other apps, cloud storage, PCs, mobile devices, or other types of storage, data loss prevention strategy protects it.
4. DLP endpoints
This kind of DLP technology safeguards data on endpoint devices, including mobile phones, tablets, and desktop PCs.
Data copying can be prevented, or all data transfers can be encrypted by network security.
5. Detecting data leaks
This DLP method entails establishing a baseline of typical DLP behavior before aggressively searching for anomalous conduct.
6. A cloud DLP
To manage and safeguard sensitive data in Software-as-a-Service and Infrastructure-as-a-Service systems, DLP solutions have been developed.
Related: SaaS vs PaaS vs IaaS: Definitions, Differences & Examples
How does Data Loss Prevention DLP work?
DLP solutions may use a number of techniques to detect sensitive data.
Some of these DLP techniques include:
a. Data identification
Similar to how individual fingerprints uniquely identify particular persons, this DLP procedure generates a distinctive digital “fingerprint” that may identify a particular file. The fingerprint will be the same on every copy of the file.
Outgoing data is scanned for fingerprints by DLP software to see whether any fingerprints match those in sensitive files.
b. Matched keywords
DLP software scans user messages for certain words or phrases before blocking messages that include them.
A DLP system can be set up to prevent outbound emails from including the term “quarterly financial report” or certain words that are known to be in the report if a corporation wishes to keep its quarterly financial report private until its earnings call.
c. Regular/Rule-Based Expressions
A DLP engine scans material for predefined rules like 16-digit credit card numbers, 9-digit U.S. social security numbers, etc. as part of the most popular analysis method used in DLP.
Since the rules can be quickly written and executed, this network security approach makes a great first-pass filter, albeit it can have significant false positive rates in the absence of checksum checking to identify legitimate patterns.
d. Matching patterns
Text is categorized using this DLP method based on how likely it is to fall within a protected network security data category.
Suppose a 16-digit number is present in an HTTP response coming from a business database.
This string of text is classified by the DLP system as very likely to contain protected personal information, a credit card number.
e. Matching files
The hashes of protected DLP files are compared against the hashes of files that are traveling inside or out of the network security.
(Hashing algorithms produce hashes, which are distinctive strings of characters that may be used to identify a file; they always produce the same result when given the same input.)
f. Analytical Statistics
DLP statistical techniques like Bayesian analysis or machine learning to detect policy violations in encrypted network security content.
Scans from a vast collection of DLP data, the greater the better because of any smaller risk of false positives and negatives.
g. Built-in categories
Credit card numbers/PCI protection, HIPAA, and other typical sorts of sensitive data are already organized into pre-built DLP groups with rules and vocabularies.
Conclusion
DLP solutions are important to prevent data loss. Attackers cannot access crucial systems when one organizations uses data loss prevention software like Acronis Cyber Protect.
If you are a large company, you may want to secure your sensitive data using specialized DLP tools or with programmers like Acronis.
To know more about Acronis Cyber Protect by Exabytes, contact us now.
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