You must be vigilant and make sure that the data belonging to your business is secure. Data security best practices shouldn’t be restricted based on the size of the company; this has never worked and won’t work in the future.
It would help if you were everywhere, blocking every potential security hole—from the server to the endpoint, on the web, in the workplace, and on your customers’ systems.
Why? Since the risk is actual and increasing. It goes without saying that although hackers frequently target huge corporations, they also find smaller firms appealing. The reasoning is clear. Small organizations adopt a “not much to steal” mentality by employing laxer controls and vulnerable data protection measures.
Hackers gather consumer data with the obvious goal of financially exploiting businesses and the general public. In actuality, 71 percent of breaches are typically driven by money.
It is obvious that consumers lose out to hackers, and those losses mount up.
Top 14 Data Security Best Practices
Recognize Databases and Data Technology
Over the past few decades, databases have evolved considerably. The most popular database type is relational. Using this technology, data can be shown in a variety of ways depending on the requirements of the user or administrator. Structured Query Language (SQL) is the most used language for interacting with databases. SQL enables users to instantly pass queries to database servers. However, when not implemented properly, this flexibility creates a serious vulnerability. Though it employs Structured Query Language like most databases, SQL Server is not to be confused with the SQL language.
Early database systems used apps to connect the end user directly to the data. Physical security was typically sufficient in a private network to safeguard the data.
Determine the Sensitive Data’s Classification
You must be fully aware of the many sorts of data you own in order to protect it successfully. Your data repositories will be scanned by data discovery technology, and the results will be reported. After that, you can use a data classification technique to classify the data into categories. For its searches, data discovery engines typically use regular expressions, which are incredibly flexible yet difficult to create.
By avoiding keeping sensitive data in places that are not safe and controlling user access to it, data detection and classification technology help you lower the likelihood of inappropriate data exposure and data loss. To enable you to protect it in accordance with the importance of the data to the company, all critical or sensitive data should be distinctly identified with a digital signature that indicates its classification. Data discovery and classification can be facilitated and improved by third-party solutions like Netwrix Data Classification.
The classification may be updated as data is created, altered, saved, or communicated. Controls should be put in place to stop users from inflating the classification level, though. Only privileged users, for instance, should be able to lower a data classification.
Make a Policy for Data Usage
Data classification alone obviously isn’t enough; you also need to develop a policy that outlines the different forms of access, the requirements for data access based on categorization, who has access to the data, what constitutes proper data usage, etc. Remember that all rule infractions ought to have obvious repercussions.