Digital Diskus operates by using financial information and business analytics, and this data is sensitive. The variety of data it utilizes requires data proliferation and different new approaches in storing, protecting, and processing it (Ajah & Nweke, 2019). The company’s API and libraries can be retrieved by the industrial spies or criminals, which would lead Digital Diskus to severe consequences, so the possibility of such threats has to be eliminated.
There are several ways of decreasing the risk of attacks and malicious agents’ interventions. Firstly, the company can eliminate external access to its systems by establishing a secure network with limited users to enter simultaneously. Secondly, the two-factor authentication can be implemented to require entering a highly personal kind of data. Thirdly, artificial intelligence development can be used by Digital Diskus for setting algorithms to detect agents and malicious behavior. This kind of protection might be expensive, yet it is worth trying because it provides reliable security.
The business analytics system contains sensitive data; therefore, it requires the highest level of security build around it. The access to the information might be protected by a nonstandard agent that could be written by the system’s holder. However, such protection still puts systems at the risk of being attacked by the malicious agent intervention. Integration of several authentication steps or factors can protect data by giving access only to specific users (Rajagopal, 2016). If the agents are external, the built-in method of authentication is vital for data storage safety. The security system can also include technologies that require both the data source and the agent to authenticate themselves. For example, the malicious agent can be familiar with the analytics system architecture and, thus, be capable of accessing it by considering its weaknesses. If there is an authentication, only valid agents will be capable of going through its requirements.
Agents are part of the security architecture that provides a protocol to prevent data from being downloaded. With the expansion of cloud technologies, agent-based security architecture became more vulnerable and required decentralized and encrypted control (Xie & Liu, 2017). If attackers are familiar with the system, they can access the protocols or agents that protect the data and build a malicious agent to replace and obtain sensitive information. Moreover, an attacker can build up their own agent and use it to stole, manage, and change the business analytics information. The agent and its design are worthy of attack surfaces and need to have a high security level with well-written API.
References
Ajah, I. A., & Nweke, H. F. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), 32.
Rajagopal, B. R. (2016). Business analytics and cyber security management in organizations. IGI Global.
Xie, J., & Liu, C. C. (2017). Multi-agent systems and their applications. Journal of International Council on Electrical Engineering, 7(1), 188-197.