Artificial Intelligence: Ethical Issues and its Applications in Cyber Security Nathan Vargoshe What is Artificial Intelligence? Cybersecurity and Applications Conclusion Artificial Intelligence can be defined as technology that demonstrates some form of basic intelligence, including the ability to learn and make decisions. Theoretically, advanced AI may even have the capacity to simulate and surpass the cognitive abilities of the human mind.1 This technology has been playing an increasingly large role in today’s society. Artificial Intelligence is undoubtedly becoming a large part of society today, and while this advanced technology introduces new issues, it also provides many benefits to a number of industries, including the cybersecurity field. It is important to note that this technology is still fairly new, and although there are ethical issues concerning the integration of AI into society, the technology continues to develop and improve. The benefits offered by AI in the cybersecurity field outweigh the controversies in, and since the industry of cybersecurity is ever-changing, AI is necessary to keep up with the changes. Cybersecurity is a field of study in the computer science industry that is dedicated to countering malicious attacks and exploits on networks. Having the proper countermeasures to these attacks are necessary, as the results of a successful attack can be catastrophic for the victim. AI technology has a promising future in the field of cybersecurity. Machine learning has been used to identify anomalous events on a network, which is often a signal of many types of attacks.4 Because of its ability to detect these signs of an attack, AI can be incorporated into intrusion detection systems (IDSs), which monitor network traffic and detect malicious activity.5 Ethical Issues How does Artificial Intelligence Work? The simple calculations and processes that occur in the human mind during every second of the day are actually much more complex than they initially seem, and developing a computer program to perform these same calculations takes time. Machine Learning is the term that is used to define how an AI “learns.” A technique known as genetic programming involves feeding an algorithm data and telling the algorithm what this data is. The algorithm interprets this data, and when it is asked questions about this data, developers determine if the results fall within a certain acceptable frame. Algorithms that succeed are built off and “reproduce,” while algorithms that fail are destroyed. This happens repeatedly until an algorithm emerges that can essentially understand and make decisions about the data that it is fed.2 Bias: AI has the potential to be biased due to it needing to learn from information that is given to it. Therefore, it has the potential to inherit the biases of the human creators.3 More Advanced vs. Less Advanced: As AI becomes more advanced, it becomes more integrated into our society. There are concerns on how AI would handle ethical dilemmas, and whether a more advanced AI would make more moral decisions over a less advanced AI.. Cybersecurity Issues: AI may also pose some threats to the industries that they are implemented in. For example, in the cybersecurity field, an AI tasked with assigning users on a network certain privileges may automatically deny a user certain privileges based on biases that it may have. Picture Credits: https://openclipart.org/tags/Artificial%20Intelligence https://www.istockphoto.com/ca/illustrations/artificial-intelligence?sort=mostpopular&mediatype=illustration&phrase=artificial%20intelligence 1 Parnas, David Lorge. 2017. “The Real Risks of Artificial Intelligence: Incidents from the Early Days of AI Research Are Instructive in the Current AI Environment.” Communications of the ACM 60 (10): 27–31. doi:10.1145/3132724. 2 Milad Fatehnia, and Gholamreza Amirinia. “A Review of Genetic Programming and Artificial Neural Network Applications in Pile Foundations.” International Journal of Geo-Engineering, no. 1 (2018): 1. doi:10.1186/s40703-017-0067-6. 3 Howard, Ayanna, and Jason Borenstein. 2019. “Trust and Bias in Robots: These Elements of Artificial Intelligence Present Ethical Challenges, Which Scientists Are Trying to Solve.” American Scientist, no. 2: 86. 4 Pierre Parrend, Julio Navarro, Fabio Guigou, Aline Deruyver, and Pierre Collet. “Foundations and Applications of Artificial Intelligence for Zero-Day and Multi-Step Attack Detection.” EURASIP Journal on Information Security, Vol 2018, Iss 1, Pp 1-21 (2018), no. 1 (2018): 1. doi:10.1186/s13635-018-0074-y. 5 M Rhodes-Ousley, Information Security: the complete reference (McGraw-Hill Education, 2013).