AI in Information Technology


AI enables people to automate repeated learning and searching processes through the use of data. The introduction of artificial intelligence systems in medicine is one of the most potent modern trends in world healthcare. Artificial intelligence technologies are also fundamentally changing the global cybersecurity system. The significance of AI in information technologies is to be recognized in all areas of human life. Accordingly, the need to develop and improve AI is due to the fact that civilized society needs to apply it every day.

The aim of the research is the importance of applying artificial intelligence in the field of technology, particularly in medical and cybersecurity issues. Accordingly, the paper will address the significance and value of applying AI (Prediger 33) and (Nadikattu 29). However, attention will be focused on the historical aspect of the development of technology (Haenlein et al. 12). Moreover, crucial is the statistics of AI use by different companies and countries (Wang et al. 67). The paper discovers the distinctiveness of applying AI in the security area (Gill 34). The foremost advantages of using AI for organizations are defined (Johnson 154). The hurdles and warnings to defense and their feasible inhibition through technology usage are reviewed (Talwar and April 15). The analysis concentrates on the advantages of practicing artificial intelligence in doctors’ work. (Amisha et al. 2328). A study of potential areas in which AI can be used to facilitate the work of physicians has been conducted (He 34) and (Davenport and Ravi 94). Therefore, areas where it is impossible to replace manual work due to the high risks, are examined.

The Significance of Artificial Intelligence in Information Technology

Artificial intelligence (AI) is the discipline and technology of creating smart machines (software) capable of taking on specific functions of human intellectual activity. For example, they can choose and make optimal decisions based on previous experience and rational analysis of external influences. This concept was introduced in 1956 by Dartmouth College professor John McCarthy. He wondered if it could teach a machine to use language and improve itself by trial and error. In recent years, artificial intelligence has penetrated almost all areas of human life or at least those where data is a significant component. (Haenlein et al. 12). It also provides the system to process and interpret information, analyze it and draw the required conclusions. Moreover, AI applies and adapts this knowledge to achieve the purposes for which the technology was implemented. Humans face artificial intelligence every day in ordinary life.

Additionally, autonomous cars can analyze the situation on the road. However, artificial intelligence includes product recommendations that may interest a person is created as a result of the analysis of the visited Internet pages. Financial news reports, sports articles, and notes are produced on news portals. The importance of artificial intelligence shows itself in such categories. AI enables people to automate repetitive learning and searching processes through the application of data. However, AI is different from robotization, which is based on the use of hardware (Nadikattu 29). The goal of AI is not to automate manual labor but to reliably and continuously perform numerous large-scale computerized tasks. This kind of mechanization expects human intervention to initialize the system and request questions correctly.

Moreover, deep neural networks enable AI to achieve unprecedented levels of precision. For example, working with Alexa, Google Search, and Google Photos is deep learning, and the more humans use these tools, the more effective they become. In the field of healthcare, the diagnosis of cancerous tumors on MRI images using AI technologies is not inferior to the outcomes of highly qualified radiologists in terms of accuracy. AI empowers personalities to arrange the most out of information. With the advent of self-learning algorithms, the data itself becomes an object of intellectual property (Prediger 33). The information contains the answers that people need to find applying AI technologies. As knowledge performs a much more significant role now than ever before, it can provide a competitive advantage. When practicing the same technologies in a competitive environment, the one with the most accurate data wins.

In addition, statistical data show how relevant and familiar artificial intelligence has become in the modern world. Thus, 4 billion gadgets today are equipped with artificial intelligence technology. Most often, it is a voice recognition system. In 6 years, the market for AI software is expected to improve by 50 rates, from $ 1.4 billion to $ 59.8 billion (Wang et al. 67). The predicted growth of world GDP in 2030 due to AI will reach almost 16 trillion dollars.

Furthermore, 38% of professions in the United States will be irrelevant due to the development of artificial intelligence. More than half of US top executives apply technology to analyze the performance of their employees and the company’s business processes. In 2022, one in three corporations will have at least one robot in the state, such as a chatbot for sales. There will also be a total of 1 billion cameras in the world that will recognize the face and compare it with the bases of law enforcement. At the same time, more than 30,000 lives a year must be saved by autopilot vehicles (Wang et al. 67). The leading country in AI development is the United States, which has invested $ 10 billion in technology research and improvement. At the same time, China registers more patents every year; their number is expected to double over the next 5 years.

Artificial Intelligence in Information Security

Artificial intelligence makes a notable contribution to combating modern information threats. In most cases, implementing AI technologies in an organization’s information security reduces the time it takes to identify problems, respond to incidents, and lessen personnel management costs. The debate about the practical application of artificial intelligence has long been sharpened. Nevertheless, these tools entered the market only when the accuracy of their work began to justify the cost, and the capabilities of intruders became so broad that it became impossible to counter them (Gill 34). Although data management processes have significantly progressed, digital threats are similarly evolving due to their dynamic potential.

The use of AI in security is justified primarily by two determinants: the need for a rapid response when a cyber incident occurs and the absence of qualified defense specialists. Lack of knowledge is one of the principal advantages that favor attackers against businesses. If an institution does not have enough IT workers, probably, it has already been subjected to intrusions or interventions that it has not yet noticed. Detecting suspicious behavior or interruptions as they occur is not always possible. When cyber-attack has already begun, the lowest possible Mean Time to Respond (MTTR) is required; it is vital to reduce risk and minimize potential damage (Gill 34). By implementing artificial intelligence and deep learning algorithms to cybersecurity, companies can gain time, a significant element in this situation. Artificial intelligence-based protection systems will be indispensable for detecting anomalies in many information security events, for example, by analyzing the logs of the IDS, data from SIEM systems, or SOAR solutions. This information, united with data from already worked and closed IS incidents, will represent a marked quality dataset on which the system can be easily exercised.

Classic systems of deviation analysis are usually based on predetermined rules set by operators: exceeding the volume of traffic, a number of unsuccessful authentication attempts, a specific amount of the consecutive triggering (Johnson 154). Artificial intelligence-based systems will be able to make decisions independently, ‘without regard’ to rules previously created by IS employees, which may have already lost relevance and do not consider the changed IT infrastructure.

Peculiarity detection can help protect user information — for example, an online banking service can collect and analyze customer activity patterns to quickly identify compromised accounts. Financial institutions may also use machine learning systems to evaluate borrowers, analyze financial risks, and anti-fraud methods. Another example of using artificial intelligence practices in cybersecurity is dealing with internal perpetrators. Knowing a user’s typical behavior, the system can warn analysts if the employee’s work pattern has changed significantly (visiting suspicious sites, prolonged absence at a work PC, changing the conversation model) (Johnson 154). Security systems equipped with computer vision and speech processing can promptly inform the guard of unauthorized access attempts, analyze the working activity of employees using webcams, evaluate the correctness of managers’ communication.

However, it must be remembered that cybercriminals also use AI-based systems. Dishonest techniques are made to trick anti-fraud systems; voice may be spoofed for fraudulent calls asking to transfer money, IVR phone technology is used for phishing and theft (Talwar and April 15). The malware also applies elements of artificial intelligence that allow attackers to elevate their privileges much faster, move around the corporate network, and then find and steal the data of interest. Thus, the technologies that have been made available are being used for both good and bad, which means that trained cybercriminals should be fought with the most sophisticated defenses and methods.

Artificial Intelligence Uses in Medicine

AI-enabled tools can derive meaningful information from massive amounts of data, contributing solid approaches that can profit various areas. Medicine is no exemption, where AI can be applied to resolve perplexing and time-consuming tasks. It can be a valuable resource for medical professionals, helping them realize their complete expertise and potential across the healthcare system. AI can be applied for many purposes, including drug development, treatment decisions, patient care, and financial operations.

Before expert systems began to process medical information in the 2000s, predictive models in healthcare could only account for a restricted number of variables in well-prepared medical data. Today’s machine learning tools, which use artificial neural networks to learn highly complex relationships or deep education technologies, often outperform human capabilities. AI machinery can help medical institutions, executives and researchers leverage millions of medical reports, patient records, clinical studies, and journals to extract valuable information. Developing AI is now a priority for many countries around the world. When considering the implementation of intelligent systems, the primary benefit will be to increase diagnosing various diseases (He 34). A doctor’s practice and experience may not be enough to identify a particular problem in the human body in time. In contrast, a neural network with access to a considerable volume of data, literature, and histories will be able to quickly classify any case, correlate it with similar problems, and suggest a treatment.

Vaccine advancement and succeeding clinical examinations are lengthy and costly processes. AI can diminish the time to produce innovative medications many times over by investigating the molecular constructions of tablets and introducing new ones according to provided necessities. For example, in 2019, Insilico Medicine formulated diverse pill alternatives to treat muscle fibrosis in this way. The algorithms took 21 days for this engagement, after which the specialists picked several appropriate remedy alternatives and examined them on laboratory animals in 25 days. Thus, it demanded forty-six days to choose the appropriate drug. (Amisha et al. 2328). However, the traditional medicine development process takes about eight years and costs pharmaceutical companies several million dollars. New technologies give hope that they can help us get remedies faster for illnesses that cannot be cured today: multiple sclerosis, Alzheimer’s disease, and others.

Another unresolved problem is the imbalance and shortage of pharmaceutical personnel at the top and middle levels. According to the World Health Organization, for people worldwide to have access to health care by 2030, low-income countries need 18 million more workers (Davenport and Ravi 94). Due to population growth, an aging society, and changing clinical disease patterns, the situation is unlikely to stabilize. These circumstances will only enhance the requirement for proficient personnel and make access to care more challenging. Consequently, innovative technologies must include AI and a database in the subject field. Hence, innovative technologies must contain artificial intelligence plus an experience foundation in the topic field. They will relieve physicians from regular daily responsibilities: entering medical records or making detailed analyses of an extensive array of data from the history of diseases. Therefore, medical workers will concentrate their experience and efforts on resolving grave symptomatic cases and choosing treatment.

Popular AI technologies can assist the healthcare practice advance patient and medical staff achievement, decrease the price of medical services and enhance the property of care. Today, artificial intelligence cannot solve complex medical dilemmas: it will not independently invent and design a device from the future that can scan the human body in a couple of seconds, identify any problems, and prescribe the best treatment. However, even the current capabilities are very interesting for doctors, patients, and clinics.


The installation of artificial intelligence in the technological environment is expanding. Intelligent systems, based on advanced data collection and analysis tools, are penetrating all business and public life spheres. However, the influence of such an entrance will be especially evident in medicine; doctors can consume more time communicating with patients. Moreover, the level of progression of vaccines and drugs of severe diseases is growing. Additionally, AI is practiced by law enforcement agencies to track down dangerous criminals. However, even ordinary companies can use it to protect intellectual data. Thus, at the present stage, the role of AI in society is significant. At the same time, it is essential that the development of this industry continues in the future.

Works Cited

Amisha, Malik, et al. ‘Overview of Artificial Intelligence in Medicine.’ Journal of Family Medicine and Primary Care, vol. 8, no. 7, 2019, p. 2328. Web.

Davenport, Thomas, and Ravi Kalakota. ‘The Potential for Artificial Intelligence in Healthcare.’ Future Healthcare Journal, vol. 6, no. 2, 2019, p. 94. Web.

Gill, Amandeep Singh. ‘Artificial Intelligence and International Security: The Long View.’ Ethics & International Affairs, vol. 33, no. 2, 2019, pp. 169-179. Web.

Haenlein, Michael, and Andreas Kaplan. ‘A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence.’ California Management Review vol.61, no.4, 2019, pp. 5-14. Web.

He, Jianxing, et al. ‘The Practical Implementation of Artificial Intelligence Technologies in Medicine.’ Nature Medicine, vol. 25, no. 1, 2019, pp. 30-36. Web.

Johnson, James. ‘Artificial Intelligence & Future Warfare: Implications for International Security.’ Defense & Security Analysis, vol. 35, no. 2, 2019, pp. 147-169. Web.

Nadikattu, Rahul. ‘Artificial Intelligence in Information Technology.’ International Journal of Computer Trends and Technology, vol.65, no.1, 2018, pp. 29-32. Web.

Prediger, Lukas. ‘On the Importance of Monitoring and Directing Progress in AI.’AI Matters, vol.3, no.3, 2017, pp. 30-38. Web.

Talwar, Rohit, and April Koury. ‘Artificial Intelligence —the Next Frontier in IT Security?’ Network Security, vol. 2017, no. 4, 2017, pp. 14-17. Web.

Wang, Weiyu, and Keng Siau. ‘Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity: A Review and Research Agenda.’ Journal of Database Management, vol.30, no.1, 2019, pp. 61-79. Web.

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