Artificial intelligence (AI) involves computers performing tasks that commonly are usually done by humans. Some of these tasks include visualizing images, decision making, and text recognition. Applications such as expert systems, natural language processing, machine vision, and speech recognition are vital applications that contribute to AI. It was founded in the year 1956 as an essential academic discipline. Since its establishment, it has experienced several waves of optimism and shortcomings and, most critically, loss of funding. However, AI has also faced modern approaches, renewed funding, and longer success spells. Nonetheless, since its inauguration, AI research has incurred, tried, and discarded several approaches. This does not exclude brain stimulation, formal logic, modeling human problem solving, and the imitation of animal behavior. According to Haenlein and Kaplan (2019), early in the 21st century, machine learning completely dominated the field, helping to solve challenges throughout the academic field and industry. Therefore, AI can be effectively used by cybersecurity experts to implement the best cyber security practices and reduce malicious activities that are likely to arise.
The history of AI can be traced back to the era when artificial beings were considered essential story-telling devices in antiquity as it began through myths, rumors, and stories. The father of AI was John McCarthy, as he played a vital role in coining AI. McCarthy later became the inventor of the listed processing language (LISP). In addition, the characters in Mary Shelly’s fiction acted as the origin of what is currently considered the ethics surrounding AI (Fitzpatrick, 2019). However, the emergence of digital computers paved the way for the concept to explore and analyze the probability that human intelligence can also be reduced in a procedural manner with the help of symbolic AI.
The idea of AI was developed long ago but remained inactive until the past decade. This arose because the famous cognitive scientist Marvin Minsky was more optimistic about the future that technology had (Fitzpatrick, 2019). There are several categories of AI research, and they are centered on specific goals and the use of particular tools and machines. Traditionally, the AI research goals included the knowledge presentation process, the various forms of reasoning, planning, and perception. Furthermore, it also entailed the ability for individuals to move and manipulate through objects and tools.
Nevertheless, it is clear that AI plays a significant role in the cybersecurity review because cyberattack has been vast and massive over recent years. This is a clear illustration that organizations need to review their cybersecurity posture with the aid of human intervention. The department involving homeland security recently announced its first strategic plan and framework regarding the implementation and integration of AI in the domestic security docket (Galaz et al., 2021). The plan primarily focused on the principles necessary to ensure America remains competitive in technology issues. However, AI also offers a crucial platform to the Department of Health Security (DHS) new opportunities. These are vital in accomplishing the various missions to protect the homeland, recognize and identify, prevent the criminal actors, and most importantly, protection of cyberspace.
AI presents a series of advantages and also applications in several key areas, including cybersecurity. With the rapid cyberattacks and the increased multiplication levels of modern devices, AI and other modern machines play a vital task in monitoring cyber-criminal cases and the automation of threat detection (Haenlein & Kaplan, 2019). In most scenarios, AI is used to detect and recognize the risks of cyber threats and any malicious activity that is likely to happen. The traditional systems could not detect any software malware created regularly, illustrating an area where AI can be of greater importance. With the aid of modern, enlightened innovation, AI is customarily instructed to ascertain any existing viruses, adware spyware, and most significantly spot the ransomware attacks before they penetrate the system (Haenlein & Kaplan, 2019). In addition, AI provides a platform for the use of a more predictive intelligence together with natural language processing, which curates on its own by scraping through news, and the various studies on cyber threats.
Additionally, AI programs are capable of creating greater adaptability in homeland security. Using the system driven by AI, the latest information trends can be easily formulated and adapted to suit the machine, thus boosting overall performance. Moreover, it is familiar with types of data networks that are complex hence making it easier to identify and remove possible security threats without engaging professionals. Therefore, AI assists specialists in detecting and correcting malfunctions that may occur as a result of cyberattacks. The intervention of AI in solving the issues enables experts to undertake the processes quickly and appropriately.
AI also plays a crucial role in breach prediction in homeland security as it is responsible for determining IT asset inventory accuracy. It offers a platform that enables the organization or individuals to plan because it identifies how and where compromisation is likely to happen effectively. Furthermore, it offers better endpoint protection as antivirus and VPNs also aid in protecting malware and the risk of ransomware attacks, but they only work based on the given signatures. An illustration of this is the technique of keeping up with the signatures for an organization to stay protected. However, this can only be a concern if there is a lag in the virus definitions.
The AI-driven endpoint security takes a different route by establishing an adequate basis of behavior for the endpoint through a thorough and rigorous form of teaching. Then, if an ordinary or extraordinary issue arises, AI immediately identifies it and takes the appropriate action by sending a notification to the support staff or technicians or reverting to safe mode. The aforementioned approach plays a crucial role in ensuring security against menace rather than waiting for signature updates. Implementing AI on homeland security or cyber security fights all the existing threats and prepares in advance for the fight against powerful threats.
Despite AI addressing most cyber security concerns, it is not regarded as the ultimate fix because it also has several shortcomings and challenges associated with its involvement in the system. It is still difficult in some countries and organizations to implement AI on a small scale because the cybercriminals are Al-savvy (Vaishya et al., 2020). The AI solutions are used by hackers hence making it quite tricky at times. The concept allows the hackers to gain access to the organization’s records and progress, and they might reverse their findings to create a new threat. In addition, cyber threats tend to evolve, so even if an organization introduces AI technology, it does not guarantee that the organization will be immune to the threats.
Another emerging issue regarding AI is the higher rates of adoption barrier. AI still needs a lot of computing power and human resources to overcome these hurdles. However, AI has also been associated with emerging issues such as automation of the spurred job loss, privacy violations, market volatility, automatization of the weapons, and socioeconomic inequality (Fitzpatrick, 2019). Therefore, it is the role of the various organizations across the globe to prepare adequately on how to deal with or avoid these major concerns or face potential risks. Venomous use of AI may result in an impending warning to physical tasks. Remarkably, AI has brought harm because of generating pictures hence posing a threat that authoritarians could use.
The “new ways” can go wrong any time, and Professor Stephen Hawking encourages students to explore the world’s mysteries that are not yet solved. According to him, humans will not survive on this planet in the next 1000 years not unless they look for another world to live in (Bondarenko et al., 2017). Thus, the risks associated with AI can be mitigated through the definition of an end-to-end model operation process. This will aid in integrating the available applications like the data platforms rather than duplicating the existing efforts. Nevertheless, AI risks and challenges can also be addressed through combining machine learning automation, developing and considering how ethical issues might prevent an individual from using AI effectively. This will allow humans to apply AI accordingly with limited dangers associated hence delivering a practical outcome.
In conclusion, AI plays a vital role in enabling cybersecurity professionals to develop appropriate strategies for embracing security by minimizing malicious activities performed by unauthorized individuals or malware viruses. However, it also has several risks and shortcomings, such as violation of data privacy and, at times, the AI systems, which render wrong results. The AI risks are mitigated by ensuring that the superweapon systems remain under human control because if they slip out of human control, more significant damage might occur. The setbacks can also be controlled by having the AI systems explain their decision to humans, aligning the primary interests of humans and machines, and teaching the computers regarding human values and behaviors. Nonetheless, ensuring humans still get jobs despite the computers doing much of the work contributes significantly in mitigating AI deficiencies hence helping the humans retain their jobs. Machine learning effectively uses predictive analysis to minimize and reduce employee turnover rates.
References
Bondarenko, V. M., Ilyin, I. V., & Korotayev, A. V. (2017). Transition to a new global paradigm of development and the role of the United Nations in this process. World Futures, 73(8), 511-538. Web.
Fitzpatrick, M. (2019). Artificial intelligence and nuclear command and control. Survival, 61(3), 81-92. Web.
Galaz, V., Centeno, M.A., Callahan, P.W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., McPhearson, T., Jimenez, D., King, B., Larcey, P., & Levy, K. (2021). Artificial intelligence, systematic risks, and sustainability. Technology in Society, 67, 101741. Web.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. Web.
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339. Web.