Despite cyber threats coming in many forms, from ransomware attacks and malicious insiders to accidental abuse and nation-state actors, businesses may face real-world, tried-and-true adversaries every day.
Data leakage can be prevented by protecting it at the source. However, since data is created and resides across users, networks, cloud platforms, and devices, it requires time and effort to protect it. It can, however, be kept secure with the help of a few technologies, frameworks, and procedures.
Here are 10 data security best practices that businesses can follow to help keep their information safe.
1. Catalog all enterprise data
It is critical to understand what data exists in order to protect it. Distributed networks of data centers, network-attached storage, desktop devices, mobile devices, cloud computing servers, and applications flow and store data. Data creation, use, storage, and destruction must be understood by security teams.
The first step is to create and maintain a comprehensive data inventory. All data, from ordinary to sensitive, must be classified. Failure to perform and maintain this due diligence may result in some data being unprotected and vulnerable.
The sheer volume of data that businesses create, store, and use makes understanding data operations a daunting task. Consider using data discovery tools to automate this process. These automated tools use a variety of methods (crawlers, profilers, and classifiers) to find and identify structured and unstructured data.
2. Understand data usage
Data is not a static entity, it moves as the application is used. Data can be dynamic, static, or in use. In order to properly protect data, it is imperative to understand the different states of the data and how the data transitions between schemas. Understanding how and when data is transferred, processed, and stored can help you better understand the protection you need. Failure to properly identify the state of the data will result in insufficient security.
3. Classify the data
Not all data has the same value. For example, personally identifiable information (PII) and financial records are more valuable than technical white papers.
After taking inventory of data and understanding its purpose, it needs to be valued, classified, and labeled. Classification labels enable businesses to protect data based on the value of the application. The taxonomy terms used are determined based on the needs of the business, but data is generally divided into four categories:
(1) Public (free of charge);
(2) Internal (remaining within the enterprise);
(3) Sensitive (compliant data, requiring protection);
(4) Confidentiality (non-compliant data, causing damage if leaked).
Consistent and appropriate data classification also helps determine where and when data should be stored, how it should be protected, and who has access to it. It also improves compliance reporting.
Many data discovery tools can classify and label data according to data classification strategies. These tools can also enforce classification policies to control user access and avoid storing it in insecure locations.
4. Use data masking
A powerful weapon in preventing data loss is making the stolen information unavailable to cyber attackers. Security tools can provide this functionality.
Data masking enables users to perform tasks on functionally formatted data based on real data, all without requiring or exposing the actual data. Data masking techniques include encryption, character shuffling, and character or word substitution. One of the most popular techniques is tokenization, which replaces real values with fully functional virtual data. Real data (such as full-featured dummy data in place of real values or credit card numbers) is in a central location for hardening, with access restricted to the desired users.
5. Use data encryption
Data encryption uses encryption algorithms and keys to ensure that only the intended entity can read the data. Encryption is used for data stored on drives, within applications, or in transit. It is widely available in operating systems, application and cloud platforms, and standalone software programs.
If encrypted data is stolen by a cyber attacker, it cannot be read, so the cyber attacker cannot derive any value from the data. Encryption is considered such an effective method that many regulations use it as a safe harbor to limit liability after a data breach. Encryption should not be considered a panacea for data security, but it is one of the best ways to protect valuable information.
6. Implement strong access controls
Data that has value or is regulated, is only available to those who need access to do their jobs. In addition, establishing strong access control mechanisms to determine which entities have access to which data, and managing and regularly reviewing the permissions of these entities.
Authorization and access controls range from passwords and audit logs to multi-factor authentication, privileged access management, and mandatory access controls. Whichever mechanism is used, ensure that it authenticates entities and grants access according to the principle of least privilege. Robust access control requires comprehensive monitoring and auditing to quickly identify anomalies or abuse.
7. Create a data collection and retention policy
Data collection and retention policies are unpopular subjects, but they exist for a reason. Data collection and retention policies establish norms related to data management and protection. These policies establish the following rules:
- What data is collected;
- When and how to retain;
- Which data must be encrypted;
- Who has access to the information?
Data that does not comply with data usage and retention policies should be purged. In addition to supporting internal operations, the policy supports compliance with regulations such as GDPR and CCPA.
8. Conduct safety awareness training
Like cybersecurity, data protection is a team effort. Educate employees and users who have access to the data on the importance of data security. Ask them to discuss their role in data security and what data users should collect and store and what data should not be shared.
Informed and empowered employees are more likely to support safe efforts rather than undermine them by trying to circumvent controls. Those closest to data stewardship can also provide valuable support by identifying anomalies that may indicate potential problems.
9. Backup data
Availability and integrity are as key to security as confidentiality. Data backup provides these functions. Backups are copies of data that reside in different locations. Backups enable data retrieval if the working copy is unavailable, deleted, or corrupted.
Backups should be made on a regular basis. The backup can be a full copy or an incremental backup that saves only changes. Backups should therefore be protected.
10. Use Data Loss Prevention (DLP)
A data loss prevention (DLP) platform is a key element of any data security strategy. Data Loss Prevention (DLP) consists of technologies, products, and technologies that automatically track sensitive data. Its Safeguarding Use Rules review electronic communications and data transfers. They prevent data from leaving the corporate network or being routed to internal resources that are not covered by the policy. Data Loss Prevention (DLP) can also be used to prevent corporate data from being transferred to unauthenticated entities or through illegal transfer methods.
Conclusion
Data security incidents don’t happen out of thin air, and it requires these best practices not to be used as stand-alone activities, but as part of an enterprise’s defense-in-depth strategy. Organizations should employ a combination of most, if not all, of these components to create an efficient data security program.
