Data Control

What is Data Control?

Data control is the management policy of an organization’s data. It is concerned with monitoring and reporting on an operations’ efficiency and resolving problems. These capabilities inspect, validate, notify, document, report, and track issues concerning data access. Data control is an essential part of data management policy.

The Primary Objectives of Data Security Controls

Confidentiality, integrity, and availability, or the CIA triad, are the three pillars of data security controls.

• Confidentiality: The notion of least privilege ensures privacy by allowing only the minimum level of access necessary for each user. Guidelines for proper authorization and preventing illegal access are essential to maintaining confidentiality.
• Integrity: Data that is guarded against unauthorized and accidental changes. You can use digital signatures and hashing techniques to check the integrity of the data.
• Availability: The ease with which authorized users can access data and computer networks is considered the availability of data.

Controlled Data: How Data Control Works

A report reader can alter the data source utilized by the report using the data control. It is important to examine reports’ behavior without data control to see how this works.

Each report, without a data control, corresponds to a separate data source. Every control and chart in the report derives its information from that singular source of information. If you want to see different information in the report, you will need to either change the report and choose a new data source or disconnect and reconnect the data source to a new data set.

When a new data control solution is implemented, the old one is still used by default. In any case, the data control allows anyone reading the report to switch to a different data collection to which they have access.

Examples of Data Control

There are several ways through which an organization can enforce data controls. Here are some examples:

• Data Encryption : Encrypting information with an algorithm renders it illegible without the key or the appropriate permissions, significantly boosting data security.
• Data Masking : With data masking, sensitive information gets concealed by replacing actual characters with fictitious ones.
• Data Erasure: As time passes, information becomes obsolete and must get deleted permanently from all storage media.
• Data Resilience: A smart technique to protect against data loss or deletion by mistake is to create backups and duplicates of the data.

In most cases, companies use a combination of the data security procedures described above to achieve the highest level of data protection that is practically attainable.

Data Control Challenges

While the importance of data controls cannot be overstated, they are not without challenges. Here are three significant data control challenges that companies must prepare to face:

• Duplicate Data: Duplicated data can occur due to either human error or technical failure. This issue, which grows in the absence of data management, needs to be addressed within data control. Consequently, computing and storage space costs rise, and the resulting analysis may produce inaccurate or misleading conclusions.
• Hidden Data: Another difficulty arises from confidential data. Plans for regulating data should consider the possibility of securing information hidden from a typical viewer. There is a lot of value in unstructured, “hidden” data if only it could get uncovered and controlled.
• Inaccurate Data: Inaccurate data might occur from not discovering all of the hidden data but is typically brought on by human error, such as entering incorrect or omitted information when filling out a form.

5 Data Control Best Practices

To address the challenges and get the most of your data control solution, here are five of the best practices in data control:

1. Have a Budget

A data security control does not have to be expensive, but organizations must create an appropriate budget for their data control needs.

2. Determine the Type of Information that Needs to be Secured

There may be a range of sensitivities among data types. When dealing with more sensitive data, there is a bigger potential for harm to a data subject. A small amount of extremely sensitive personal data might have devastating effects if compromised.

So, while deciding on a security measure, companies must account for the specifics of data safeguards and their overall level of sensitivity.

3. Keep an Eye Out for Potential Threats

Both internal and external factors pose security risks.

Thus, organizations need to implement more stringent and complex controls, especially when processing personally identifiable information (PII), in response to risks with higher likelihood or greater impacts. Conversely, less sensitive personal details may need less stringent safeguards.

4. Follow Standard Procedures and Practices

Professional knowledge is necessary for cyber and information security. Therefore, organizations must select a data security measure that controls data following industry standards. Additionally, organizations need to take into account certain regional and international standards.

5. Examine the Capabilities of Your Data Security System

In the case of a security breach, whether physical or technical, your security tool should be able to restore availability and access to sensitive data quickly. Additionally, it should be able to render the data inoperable to anyone who does not have access permissions.

Conclusion

Ensure data quality across all types of data with effective data control. The advantages of data control include:

• Gathering business artifacts
• Determining key data items
• Defining data quality objectives
• Conducting compliance audits

Sator provides easily implemented data management procedures to ensure that regardless of the types of data your organization uses, it is secure. Satori enables dynamic data masking and classification, and data access controls to help your organization protect its sensitive data.