Layers, Methods, and Techniques of Protecting Information Systems

How to Protect Information Systems

Information systems are an integral part of the business world. Today, many organizations, especially online companies, rely on the system in their entire operations. For instance, Amazon uses information systems in e-commerce to collect and store customers’ records. Such data is highly sensitive, thus raising the need to safeguard it against unauthorized persons. Firms wishing to adopt and maintain information systems should ensure that the platform is secured from internal and external threats.

One of the methods utilized in protecting information systems is authentication. Under this technique, individuals are required to identify themselves before accessing data (Bourgeois, 2016). Various forms of identification can be used, including biometrics and passwords. To achieve maximum security, organizations adopt multi-factor authentication (Bourgeois, 2016). The complexity of the process makes it difficult for users to compromise the system.

Encryptions and firewalls are also highly utilized in safeguarding information systems. Encryptions are used in data exchange to enhance confidentiality in message transmission (Bourgeois, 2016). Firms that rely on encryptions restrict data access to individuals in possession of decrypting keys. On the other hand, firewalls protect an information system against external attacks, such as hacking and virus attacks (Bourgeois, 2016). Although these techniques may prove effective, firms should always assess the presence of drawbacks to ensure that their systems are safe.

Similarities and Differences Between Structured Data, Unstructured Data, and Big Data

Data is an important asset in every company. The information helps managers and other stakeholders understand business processes and make timely changes when needed. Different categories of data that firms can use in their operations include structured, unstructured, and big data. Each serves a different purpose in the organization. Employees should understand the similarities and differences between each type of data to determine areas in which they can be applied.

Structured, unstructured, and big data are similar in their importance within a business. They are used to facilitate critical functions and decisions in a firm. The process can be proven by the rate at which firms generate these types of data. A study conducted in 2011 indicated that structured and unstructured data accounted for 23.7% and 61.8% of organizational records (Eberendu, 2016). An increase in the availability of big data was also noted during the research. The findings show that these data are equally essential in every industry.

The three categories of data differ in structure and scalability. Like the name suggests, big data is voluminous and comprises the other two sets of data (Eberendu, 2016). Unstructured data “has no particular structure and may be stored as emails, texts, and images” (Eberendu, 2016, p. 48). On the other hand, structured data is highly organized with a definite format, which can be interpreted easily (Eberendu, 2016). The data can be presented in the form of tables and charts. Regardless of their variances, these types of data are used to generate vital information in the organization.

Similarities and Differences Between Hardware and Software

Hardware and software are essential devices in modernized businesses because they facilitate tasks on computerized systems. More often, these components operate interdependently. Therefore, compatibility is a crucial factor that determines functionality. Although the two may appear similar in their use, they are distinct in several criteria.

The two devices are ontologically different. The hardware is a physical device, while the software is a set of instructions, which facilitate the execution of tasks on a computer (Duncan, 2017). Besides, the hardware can run different systems, while the software requires frequent modification to perform the intended task (Duncan, 2017). However, one component cannot function effectively without the other, an aspect that makes them equally important.



Bourgeois, D. (2016). Information Systems for Business and Beyond. n.p.: CreateSpace Independent Publishing Platform.

Duncan, W. (2017). Ontological distinction between hardware and software. Applied Ontology, 12(1), 1-28.

Eberendu, A. (2016). Structured data: an overview of the data of big data. International Journal of Emerging Trends & Technology in Computer Science, 38(1), 46-50.

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