In the information age, data is an essential business asset. Many organizations develop strategies to ensure their data is safe from system intruders. Data security refers to the process of protecting databases, files, and accounts and enhancing protection measures to ensure data resources are secure. An organization needs to ensure the privacy of its clients and employees. Organizations that expose information on their employees or customers willingly or unknowingly damage their reputation (Zhang, 2018). The procedure of how customer and employee data and other intellectual property rights should be handled is defined in the data security policy. In the case study, there is a need to communicate data security policies to employees. To gain access to the employee's emails, there is a need to obtain consent from the employees. Employees need to be informed of the need for employee email checks. The checks should be geared towards increasing data security in the company. Employees should be assured that the privacy of their data and information in their emails will not be put at risk by the software application.
Banks and other financial institutions need to get data security right. Any miss can have detrimental impacts on the organizations, including the loss of huge amounts of money. In banks, data security policies need to have three essential elements; confidentiality, integrity, and availability. Data should be accessible when needed by the authorized people in banks. The data available should be free from manipulations and alteration. Data available should be real-time to facilitate timely decision making. Data security policy in banks needs to ensure sensitive data is securely located. Data should be classified, ranging from sensitive to less sensitive. The more sensitive financial data need to be located in a more secure place within the systems and secured with strong passwords (Xu et al., 2019). Data policy in banks needs to ensure only authorized people have access to data. When bank employees leave the organization, they should check out in the systems and not access the offices. When employees leave systems without checking out, the bank system data is exposed to data theft, abuse, and misuse. Data security policy in banks needs to ensure continuous data monitoring and the presence of real time alerts. Real-time alerts and close monitoring are critical in compliance. It enables information technology experts to detect any unusual activity on a real-time basis. Early detection of unusual activity helps to take the necessary precautions in recovering data.
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New employees in the organization are more likely to put an organization's data security at risk. Many new employees have a short duration interacting with the system. In many instances, new employees forget to check out of the systems after the day's work or are likely to share credentials such as passwords (Kumar et al., 2018). Intensive training for new employees is critical. The training should ensure that all new employees have grasped critical points concerning data privacy. After training, new employees need to be closely monitored to ensure they are adhering to what they learned during training.
Employees leaving the organization pose a major risk to data privacy in organizations. Some of these employees are either careless or are frustrated with their organizations. Employees who have given notice of leaving jobs are more likely to share their login credentials with other employees. Organizations need to develop effective policies that ensure a well-established process of leaving the organization (Wang et al., 2020). They need to report to the organization's information technology department to ensure their passwords are changed before leaving their jobs. The information technology department personnel need to delete such employees' credentials since they can exploit the system for vulnerabilities once outside the organizations.
Reference
Zhang, D. (2018, October). Big data security and privacy protection. In 8th International Conference on Management and Computer Science (ICMCS 2018) . Atlantis Press.
Xu, G., Li, H., Ren, H., Yang, K., & Deng, R. H. (2019). Data security issues in deep learning: attacks, countermeasures, and opportunities. IEEE Communications Magazine , 57 (11), 116-122.
Kumar, P. R., Raj, P. H., & Jelciana, P. (2018). Exploring data security issues and solutions in cloud computing. Procedia Computer Science , 125 , 691-697.
Wang, H., Ma, S., Dai, H. N., Imran, M., & Wang, T. (2020). Blockchain-based data privacy management with nudge theory in open banking. Future Generation Computer Systems , 110 , 812-823.
