Academic Master

Human Resource And Management

Security Management Goals

Different pillars define security management goals, including confidentiality, integrity, and availability. The main concern in this study is integrity, the purpose of which is to guarantee the consistency and accuracy of data. Thus, the function of integrity is to ensure accurate and reliable data by preventing unauthorized hackers from accessing the system containing sensitive data (Zhuang et al. 2017). Also, integrity ensures that the transmission of data arrives at the destination the same way it originates from the sender without any changes. Security is a major issue among small and medium-sized organizations as they fail to invest in security measures, thus creating a vulnerability to attack by unanimous persons or hackers. Thus, the hacker may gain access to the organization’s system, and the system administrator fails to notice the attack if the hacker manages to cover the tracks well. In such a situation, integrity ensures there is no manipulation of data, either intentionally or accidentally, thereby retaining the purity and trustworthiness of data.

Implementation of the integrity model helps in achieving three different types of goals. First, the unauthorized person will not manage to modify any data or programs installed in the organization (Zhuang et al. 2017). Second, there will be no improper or unauthorized changes done by an unauthorized hacker. Third, integrity guarantees internal and external data or program consistency.

Factors Undermining Integrity and Their Solution

The first thing to consider while achieving integrity is data completeness. Data completeness refers to evaluating gaps in data by examining the actual data and the expected data to be collected. Solving the issue of incomplete data can only occur by preventing the submission of data that is not complete until the expected data is available (Singh et al. 2016). The paper format will comprise the integrity of data due to human errors. Paperless data collection will require assigning mandatory fields while collecting the data. Hence, achieving data completeness will be easier and time-saving as there will be no need to revisit to check mistakes resulting in incomplete data. Another factor undermining integrity is data consistency. Consistency means there is a match between different data types and the expected data types flowing into the system. Achieving data consistency will require designing the system with menus that the user can easily select without entering the data in an empty text box. Thus, there will be consistency in all data, which simplifies other activities within the system, such as searching for results.

Another factor undermining integrity is data accuracy, which ensures that the data collected is correct and represents what it should accurately. Solving issues with data accuracy follows a different strategy as it often depends on the knowledge and training of the user interacting with the system (Singh et al. 2016). However, overcoming human errors requires the implementation of extra measures for all employees in the employee position. Another strategy may include the use of additional steps, such as collecting GPS location information and capturing pictures, which will help reduce inaccurate data. Data validity is another factor which affects the integrity of data. The issue with validity usually arises with paper forms, as once data is collected, it becomes hard to change such data. Solving issues with data validity requires the use of paperless data collection methods like the use of Smartphones, where changes to data can occur easily (Singh et al. 2016). Lastly, there is data timeliness, which refers to the expected time for the data to be effective for use. Solving such issues will require the integration of timely analytics or the implementation of real-time data collection methods. Therefore, achieving integrity by considering the above factors will help in achieving high-quality data.

References

Zhuang, C., Chengyin, Y., & Feng, Q. (2017). Scheme of Encrypted Cloud Data Transmission and Achieve the Data Integrity Valid.

Singh, S., Jeong, Y. S., & Park, J. H. (2016). A survey on cloud computing security: Issues, threats, and solutions. Journal of Network and Computer Applications, 75, 200-222.

SEARCH

Top-right-side-AD-min
WHY US?

Calculate Your Order




Standard price

$310

SAVE ON YOUR FIRST ORDER!

$263.5

YOU MAY ALSO LIKE

Pop-up Message