Difference between Data Security and Data Integrity
Last Updated :
05 Sep, 2024
Data protection and integrity have become very important in this modern world for organizations, companies, and even for the people. Unique, basic, and important to the theme here are two concepts Data Security and Data Integrity. Although both oversee separate ideas in the sector of data management they are commonly referred to. Out of these two, it is vital to have a clear distinction between Data security and Data integrity to avoid confusion on the appropriate measures to be taken when protecting the information and maintaining the integrity of the information systems.
What is Data Security?
Data security refers to the prevention of data from unauthorized users. It is only allowed to access the data to the authorized users. In the database, the DBA or head of the department can access all the data. Some users are only allowed to retrieve data, whereas others are allowed to retrieve and modify the data.
Advantages of Data Security
- Protection Against Breaches: It controls access to save data as it has firewalls as well as Data encryption to avoid breaches of sensitive information.
- Compliance with Regulations: To ensure that an organization monitors data security measures and proactively adheres to pertinent data protection laws, such as GDPR, and HIPAA among others, strong Data Security measures are to be put in place.
- Preservation of Reputation: Protecting the data, then will help the organizations minimize the losses due to cases like hacker attacks and or data leaks while at the same time avoiding any bad reputation that may be resulting from such incidents.
Disadvantages of Data Security
- Cost: About Data Security, it can go a long way in protecting an organization’s data and information; however it can be very costly since it involves the procurement of more hardware and software, personnel to monitor and update the system and time to ensure that every possible loophole is closed.
- Complexity: The management and maintenance of data security can be intricate and rather challenging, more so in large organizations where data is constantly flowing through the organization both internally and externally and through a huge number of entry points that are potential targets to intruders.
- Potential for Reduced Accessibility: While most types of security measures focus on the ways of preventing unauthorized access, they can create problems for gaining access to data for other legitimate purposes within a business.
What is Data Integrity?
Data integrity is defined as the data contained in the database is both correct and consistent. For this purpose, the data stored in the database must satisfy certain procedures (rules). The data in a database must be correct and consistent. DBMS provides different ways to implement such types of constraints (rules).
It can be implemented by rules i.e., Primary Key, Secondary Key, Foreign Key. This improves data integrity in a database.
Advantages of Data Integrity
- Accurate Decision-Making: Ensuring data integrity enables people to make excellent decisions by using factual information hence promoting success.
- Consistency Across Systems: Data Integrity stabilizes data in the chain so that whenever it is accessed it has not transformed or been impaired in any way.
- Compliance and Auditability: As a rule, keeping data unalterable is mandatory to meet the requirements, while it helps auditors along with compliance personnel greatly while investigating stuff.
Disadvantages of Data Integrity
- Vulnerability to Human Error: It may be also affected through human interference through wrong data entry in the computer, or through alteration of data by a human being, which results in wrong information.
- Complexity in Management: Data Integrity must be protected with the means of data handling management and often, it is a challenging task in large and evolving organizations.
- Dependence on Technology: Data Integrity is accomplished with the help of various technological tools and systems and hence exposes the data integrity to problems related to failure or misconfiguration.
Difference Between Data Security and Data Integrity
| Data Security | Data Integrity |
|---|
| Data security refers to the prevention of data corruption through the use of controlled access mechanisms. | Data integrity refers to the quality of data, which assures the data is complete and has a whole structure. |
| Its motive is the protection of data. | Its motive is the validity of data. |
| Its work is to only the people who should have access to the data are the only ones who can access the data. | Its work is to check the data is correct and not corrupt. |
| It refers to making sure that data is accessed by its intended users, thus ensuring the privacy and protection of data. | It refers to the structure of the data and how it matches the schema of the database. |
| Some of the popular means of data security are authentication/authorization, masking, and encryptions. | Some of the means to preserve integrity are backing up, error detection, designing a suitable user interface, and correcting data. |
| It relates to the physical form of data against accidental or intentional loss or misuse and destruction. | It relates to the logical protection (correct, complete, and consistent) of data. |
| It avoids unauthorized access to data. | It avoids human error when data is entered. |
It can be implemented through : - user accounts (passwords)
- authentication schemes
| It can be implemented by following the rules: - Primary Key
- Foreign Key
- Relationship
|
Conclusion
Data Security is concerned with the unauthorized access, theft of the data or attempts to compromise the data so as to maintain its secretness and shield it from outside world intrusion. Data Integrity, on the other hand, makes sure that data is still valid, consistent, and accurate as it is used in its lifecycle to facilitate effectiveness in decision making as well as effectiveness of the operations. Both concepts are important for sustaining and building confidence in information systems as well as making certain that data achieves its purpose.
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