Artificial Intelligence Ethics and Cybersecurity: Overview and
Prospects
Dengxu Li
School of International Education, Quanzhou University of Information Engineering, Quanzhou, Fujian, China
Keywords: Artificial Intelligence, Ethics, Cybersecurity.
Abstract: This paper summarizes the current situation and future outlook in the field of artificial intelligence ethics and
cybersecurity. With the rapid development of AI technology, the industrial upgrading and social progress it
brings cannot be ignored. However, it is accompanied by increasingly severe cybersecurity challenges, such
as information leakage and system crashes, which seriously threaten user privacy and social stability. The
article points out that although AI ethics and cybersecurity have been studied in depth respectively, the
exploration of the intersection of the two is still insufficient. By reviewing relevant research results at home
and abroad, this article emphasizes the importance of AI ethics in ensuring that the use of technology does
not violate human rights and interests and meets ethical standards, and discusses the challenges and solutions
that cybersecurity faces in the era of big data and advanced analytics. The article aims to fill the gap in the
intersection research of AI ethics and cybersecurity, propose countermeasures to deal with the existing
challenges and contribute to building a harmonious and stable social environment.
1 INTRODUCTION
With the rapid development of modern computer
technology, Artificial Intelligence (AI) technology
has become more and more mature and has penetrated
into many fields such as healthcare, education,
finance, etc., which has promoted the progress of
society while upgrading the industry. Artificial
intelligence (AI) technology in OpenAI ChatGPT
application: a review of ChatGPT in writing English
essay mentions that AI technology (such as ChatGPT)
has been developed to facilitate human activities and
productivity. By automating and intelligently
handling tasks, AI can significantly increase
productivity and enable humans to focus on higher-
level work (Fitria, 2023).
However, while AI is becoming more and more
developed, some cybersecurity issues cannot be
ignored, with frequent incidents of information
leakage and website crashes, which greatly threaten
the privacy and security of users, and even pose a
serious threat to social stability. Therefore, while
researching AI, what should not be ignored is the
network security problem, how to strengthen network
maintenance, to ensure that the information is safe
and reliable is a topic that needs to be studied in depth.
AI ethics and network security have always been
two very important topics, which are related to the
stability of social development and the privacy and
security of users. From the current point of view,
although the two topics have been studied more
deeply, the intersection of the two topics has not yet
been insufficient and there are large gaps. This paper
aims to fill the gaps between the two, deeply analyze
and study the impact and challenges of AI ethics and
cybersecurity and put forward countermeasures
against their vulnerabilities, so as to contribute to the
construction of a harmonious and stable society.
Reviewing the research results on AI ethics and
cybersecurity at home and abroad, it is found that
both have been studied in depth. For example,
J.E.(Hans) Korteling and G.C. van de Boer-
Visschedijk discussed how to safely and reasonably
develop AI systems in order to ensure that their use
does not infringe upon the rights and interests of
human beings and at the same time conforms to moral
and ethical standards (Korteling, 2021). Further,
Amin Al Ka'bi in Challenges Encountered by AI
Applications in Today's Interconnected World states
that in the AI/ML field, ethics and transparency are
crucial as they ensure credibility, accountability,
security, and interpretability, which are key to the
widespread adoption and acceptance of these
Li, D.
Artificial Intelligence Ethics and Cybersecurity: Overview and Prospects.
DOI: 10.5220/0013205200004568
In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (ECAI 2024), pages 13-18
ISBN: 978-989-758-726-9
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
13
technologies in today's interconnected world (Al
Ka'bi, 2022). In terms of cybersecurity, Network
security AIOps for online stream data monitoring
demonstrates some of the current issues for
cybersecurity in the field of technology, emphasizing
the dynamic thresholds, deep learning, the role of
SOCs, situational awareness, and the cybersecurity
tools and techniques in addressing modern
cybersecurity challenges (Nguyen, 2022). In
Enhancing cybersecurity protocols in the era of big
data and advanced analytics, it is mentioned that with
the exponential growth of big data, the diversity and
complexity of data is increasing, and the different
formats and sources of data as well as the speed of
data processing require cybersecurity protocols to
have greater adaptability and flexibility (Nwobodo,
2024).
AI ethics are closely related to cybersecurity. In
the future, with the development of technology and
the expansion of application scenarios, people need to
pay more attention to AI ethical issues and actively
search for effective solutions to ensure the healthy
development of AI technology and social well-being.
2 OVERVIEW OF AI
TECHNOLOGY DEPARTMENT
AI is a cross-discipline based on computer science
and technology and cross-fertilized by multiple
disciplines such as computing, philosophy,
psychology, etc. It is a new technological science that
researches and develops theories, methods,
technologies, and application systems used to
simulate, extend, and expand human intelligence. The
research in this field covers a wide range of aspects
such as robotics, language recognition, image
recognition, machine learning, deep learning, natural
language processing and expert systems. AI can
simulate the information process of human
consciousness and thinking, and although it is not
human intelligence, it is able to think like a human
being and may even exceed human intelligence in
some aspects. As discussed in A Scholarly Definition
of AI: Advancing AI as a Conceptual Framework in
Communication Research, the authors define AI as
non-human machines or artificial entities with the
tangible ability to perform, solve tasks, communicate,
interact, and act logically in the real world, as occurs
in biological humans (Gil de Zúñiga, 2024). They
also stated that AI is mainly defined as computational
technology capable of achieving specific tasks,
imitating human behavior, cognitive heuristics and
intelligence.
The Dartmouth Conference held in 1956 marked
the emergence of AI as an independent field of study.
An important branch of early AI was semiotics,
whose main research directions included natural
language processing and logical reasoning, etc. The
1969s and 1970s were the early years of the
development of AI. In 1961, John McCarthy
introduced the term “artificial intelligence”, which
meant that the research on AI was gradually deepened,
but due to the limitations of technological
development and computing power, the development
of AI was relatively slow. 1980s, connectionism and
neural networks became an emerging field of research,
and in 1986, the back propagation algorithm opened
the way to the research and development of neural
networks, and after this, the research direction of AI
gradually shifted to machine learning and deep
learning. 21st century to the present is the beginning
of deep learning. The beginning of the 21st century to
the present is the rise of deep learning, which began
to rise in 2006 with the proposal of deep confidence
networks. Deep learning analyzes and learns the
special features of data and their deeper meanings
through multi-level neurons, and is widely used in
natural language recognition, image recognition,
speech recognition, etc. In 2016, AlphaGo defeated
Go world champion Lee Sedol, demonstrating the
potential of AI and reinforcement learning
technology in the field of AI. This was followed by
the birth of ChatGPT, which was introduced in 2022.
ChatGPT, as a powerful language model developed
by OpenAI, has strong language processing
capabilities and the ability to converse with human
beings, making it a new milestone in AI. The
introduction of ChatGPT marks a major breakthrough
for AI in the field of science and technology, and also
signals that AI will gradually integrate into all aspects
of human life.
The technical classification of AI mainly includes:
machine learning, deep learning, natural language
processing, computer vision, robot vision, robotics,
biometrics, portrait recognition, image recognition,
text recognition, aerospace applications, information
processing and many other types.
An important application of AI in the field of
cyber security is intrusion detection. The system is
able to automatically identify and analyze anomalies
in network traffic through robotic algorithms, thus
enabling timely detection and blocking of virus
intrusions and network attacks. For example, the
malicious code analysis and detection system based
on machine learning can automatically identify and
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extract the characteristics of malicious code and
analyze them, so that network attacks such as Trojans
and worms can be effectively identified.
AI can also predict cyber attacks that have not yet
occurred by analyzing historical data and network
behavior. This predictive ability can help enterprises
and organizations deploy preventive measures in
advance, which can greatly reduce the losses caused
by cyber attacks. Another important task in
cybersecurity is malicious domain name detection.
By introducing data enhancement techniques and
some neural network models, the system is able to
automatically detect malicious domain names, thus
protecting network security.
Currently, AI has infiltrated into people's daily
life as well as all walks of life, and some potential
cybersecurity issues are gradually emerging as AI
becomes more and more prevalent.
The AI technology in vehicles such as
automobile's automatic driving, unmanned vehicles,
drones and other means of transportation, although
not directly related to cybersecurity, the
communication network it uses may become the
target of cyber virus attacks. Once the automatic
driving system is invaded by a virus, it may lead to
serious consequences such as leakage of driving data
and loss of control of the vehicle, seriously
threatening privacy and personal safety. Applications
of AI in financial services include investment
management, risk prediction, stock prediction, and so
on. AI improves the efficiency and accuracy of
financial services. As demonstrated by LONGBING
CAO in AI in Finance: Challenges, Techniques and
Opportunities, AI can assist financial institutions in
risk assessment and monitoring, identifying potential
dangers through complex models, thus dramatically
improving risk management.AI algorithms can
quickly analyze large amounts of transaction data and
identify abnormal transaction behavior, thus
effectively preventing financial crimes (CAO, 2022).
3 EXISTING ISSUES IN
ARTFICIAL INTELLIGENCE
ETHICS
With the rapid development of AI technology, it is
increasingly being used in many areas of life. This has
a positive and positive impact on service quality and
productivity. However, as technology advances, so do
the privacy risks associated with the collection and
use of user data. The development of AI technology,
especially complex algorithms like deep learning,
relies on the training and optimization of large
amounts of data, much of which contains sensitive
information such as location trajectories, behavioral
preferences and personally identifiable information.
The lack of clear guidelines for data use and strict user
permission processes during the data collection phase
may increase the risk of data leakage and misuse.
The risk of data leakage mainly stems from
technical vulnerabilities, human errors or malicious
attacks. CYBERSECURITY RISK mentions cases
such as SolarWinds, in which a cyberattack not only
affected the direct target, but also spread to many
client organizations, including some large U.S.
federal agencies. This suggests that data breaches or
cyberattacks can trigger a chain reaction with indirect
effects on the wider economy (Florackis, 2023).
Data misuse raises more complex ethical issues as
it involves social justice and equity in addition to
privacy violations. Certain companies may use user
data for inappropriate marketing, tailored pushes, and
discriminatory pricing, thus negatively impacting
consumer rights. More importantly, the use of data for
algorithmic decision-making can lead to “algorithmic
discrimination” and exacerbate social inequality if the
training data is insufficient or the algorithm is biased.
For example, gender bias in historical data may lead
employment algorithms to discriminate against
female applicants, while regional disparities may lead
banking algorithms to refuse to serve people in a
particular region.
To solve this problem, a combination of ethical,
technological and legislative elements is necessary.
Legally, relevant laws and regulations must be
updated to strengthen the regulation of data collection,
processing, transmission and storage, and to clarify
the parameters of data use and the responsible parties.
In order to address the difficulties posed by data flows
in the context of globalization, efforts should be made
to harmonize and unify international data protection
legislation. In order to prevent the misuse or leakage
of user data, we should improve data security
protection at the technical level by implementing
encryption technology and using measures such as
anonymous processing. Auditing and traceability
procedures should also be established in order to
ensure the legality and compliance of data use.
Companies should adhere to the values of fairness,
openness and respect when using data, while
reinforcing social responsibility and ethical
obligations. At the same time, they should improve
their personal privacy protection skills and raise
public awareness of data protection, so that they can
work together to create a thriving data ecosystem.
Artificial Intelligence Ethics and Cybersecurity: Overview and Prospects
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In the field of machine learning, although the
algorithms themselves are not subjectively biased,
they may unintentionally reflect social and cultural
biases and discrimination when processing data. This
phenomenon has become one of the hotspots of AI
ethics and fairness research. The bias and
discrimination of machine learning algorithms
mainly stems from the following aspects.
The process of developing machine learning
algorithms begins with data collection and processing,
which usually involves bias aka data discrepancy.
Since training data is usually created by people, social
and cultural biases are inevitable. If historical data
from a hiring system shows gender bias, i.e., the
percentage of successful female applicants is
significantly lower than that of male applicants, then
an algorithm trained on that data may replicate that
bias in hiring decisions, thereby treating female
applicants unfairly. In addition, an important factor
contributing to bias is the underrepresentation of the
dataset. When the dataset is not fully representative
of all groups, the algorithm may not be able to
correctly learn the characteristics of the minority
group, leading to biased decision making.
Next is the design and training of the algorithm.
When dealing with difficult problems, algorithm
designers may unintentionally bias or discriminate,
especially by simplifying assumptions. In addition,
the optimization goal during algorithm training is
usually to maximize some kind of accuracy rather
than to maximize justice. This may cause algorithms
to prioritize high accuracy over specific
demographics. For example, in an image recognition
task, if there are few examples of a certain image type
(e.g., dark-skinned people) in the training dataset, the
algorithm may not be able to correctly learn the
features of that type of image, leading to recognition
bias.
4 AI ETHICS FROM A CYBER
SECURITY PERSPECTIVE
In the field of network security, although the
introduction of AI technology has brought great
innovation to the defense mechanism, it also faces
many difficulties and serious challenges. First, the
endless growth of complex threats is the first
difficulty that AI defense systems must address.
These include ransomware, phishing, and traditional
virus propagation, as well as advanced persistent
threats (apt) and distributed denial of service (DDoS)
attacks, which pose more diverse and subtle threats to
cyberspace security. Traditional defense strategies no
longer seem to be effective in the face of ever-
innovating attacks. While AI systems are able to
detect and react automatically, maintaining effective
and accurate judgment in a complex and dynamic
attack environment has become a major challenge.
In addition, vulnerability exploitation is another
major issue facing AI in cybersecurity. As software
systems become more complex, new vulnerabilities
are often discovered. Attackers can often take
advantage of these weaknesses to launch attacks
quickly. AI systems have the ability to monitor
network traffic and detect abnormal behavior in real
time, but the protection efficiency of AI systems may
be seriously affected by the presence of unknown
security vulnerabilities or new attack techniques.
Therefore, how to improve the defense capability of
AI systems in terms of vulnerability exploitation has
become an urgent problem.
In order to cope with the ethical challenges of AI
in the field of cybersecurity, we need to strengthen the
ethical construction of AI from multiple dimensions.
First, the key to ensure the healthy development of AI
is to improve laws and regulations. In order to control
the use of AI in cybersecurity, clarify the roles and
responsibilities of all parties, and maintain the
progress of AI technology, the government should
formulate clear laws and regulations. Changwu
Huang in An Overview of AI Ethics mentions that
discussing the ethics of AI will inevitably involve the
legal considerations (Huang, 2022). At the same time,
it is necessary to establish a strict regulatory
mechanism to crack down on illegal behaviors and
maintain the security and order of cyberspace.
Second, more regulation is needed to ensure that
AI ethics are properly maintained. As mentioned in
Ethical framework for AI and digital technologies
with the popularization of AI and DT, a series of
ethical issues have surfaced, such as privacy leakage
and algorithmic bias. It is the responsibility of the
government to establish and improve regulatory
mechanisms to ensure that the application of AI meets
ethical standards and prevents potential social risks
(Ashok, 2022).
In addition to establishing joint forces and
collaborative regulatory mechanisms across sectors
and domains, the government should strengthen the
regulation of AI technologies. At the same time,
businesses and non-governmental organizations are
urged to participate in the supervision and utilize their
technology, resources, and other advantages to jointly
promote the application of AI ethics.
In addition, enhancing technology safety is the
cornerstone of safeguarding AI ethics. Security and
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privacy protection need to be considered at the R&D
stage of technology to ensure that AI systems strictly
comply with applicable laws, regulations and ethical
norms when handling sensitive information. At the
same time, technological innovation should be
strengthened to continuously improve the defense and
self-learning capabilities of AI systems in order to
cope with increasingly complex cybersecurity
challenges.
5 CHALLENGES AND PROS-
PECTS
With the rapid development of AI technology, its
application in the field of network security is
increasingly promising. In the future, AI technology
will show the following key development trends in
the field of network security. AI technology will
further enhance the intelligence level of network
security defense, and through machine learning, deep
learning and other algorithms, it will realize the
intelligent identification, warning and response to
network attacks. This trend will greatly improve the
efficiency and accuracy of cybersecurity defense, but
at the same time, it also puts higher requirements on
the accuracy and transparency of the algorithms. As
the technology matures, AI will be able to handle
cybersecurity incidents more autonomously and
automate emergency response. This will shorten the
time from detection to response and reduce human
error, but it may also trigger ethical discussions about
the interpretability of machine decision-making
processes and attribution of responsibility.
Cybersecurity threats are constantly evolving,
requiring AI technologies to have the ability to
continuously learn and evolve. Future cybersecurity
systems will be able to continuously learn new attack
patterns and automatically update defense strategies,
but this self-evolutionary process may also bring new
ethical challenges, such as algorithmic bias and
uncontrollability.
As the application of AI in cybersecurity deepens,
the transparency and interpretability of algorithms
becomes an urgent issue. Users and society need to
understand how algorithms make decisions to ensure
their fairness and reasonableness. Lack of
transparency may lead to public distrust of the
technology and even ethical controversies.
The application of AI in cybersecurity inevitably
involves the collection, processing and analysis of
large amounts of data. How to protect personal
privacy and data security has become an important
ethical issue. Strict data protection mechanisms need
to be established to ensure the security and privacy of
data during collection, storage, transmission and use.
In automated emergency response and intelligent
defense systems, it becomes difficult to determine the
attribution of responsibility when the system makes
mistakes or causes problems. Clear accountability
mechanisms need to be established to ensure that
those responsible can be held accountable when
problems arise, while protecting technological
innovation and development.
Given the potential impact of the ethical issues
mentioned above, it is particularly important to
establish a comprehensive and systematic code of
ethics for AI. This will not only help regulate the
R&D and application behavior of AI technology and
reduce ethical risks, but also help enhance public trust
and acceptance of the technology.
In order to promote the construction of AI ethical
norms, the construction of international cooperation
mechanisms is crucial. Different countries and
regions have different views and positions on AI
ethics, and through international cooperation, mutual
understanding and consensus can be enhanced, and
ethical norms and standards of universal applicability
can be jointly formulated. At the same time,
international cooperation can also promote the
exchange and sharing of technology and promote the
healthy development of AI technology.
Therefore, this paper suggests strengthening
international dialog and cooperation to jointly
promote the construction of ethical norms for AI.
Through the formulation of clear ethical principles,
the establishment of an effective regulatory
mechanism, and the strengthening of ethical review
in the research, development and application of
technology, we can ensure that the research,
development and application of AI technology meets
ethical requirements and promotes the sustainable
development of society.
6 CONCLUSIONS
This paper focuses on the ethical and cybersecurity
issues facing the rapid development of AI technology.
The paper first outlines the application of AI
technology in the fields of healthcare, education,
finance, etc. and its positive impact on the society,
and then points out the threats to user privacy and
security and social stability posed by cybersecurity
issues such as information leakage and website
crashes. Then, the paper deeply analyzes the close
connection between AI ethics and cybersecurity, and
Artificial Intelligence Ethics and Cybersecurity: Overview and Prospects
17
reviews domestic and international research results in
this field, such as the ethical discussion on the
development of AI systems and the importance of
ethics and transparency in the AI/ML field. In
addition, the paper introduces some current key issues
and technical solutions in the field of cybersecurity,
such as the application of machine learning and deep
learning in cybersecurity monitoring. Finally, the
paper looks into the future direction of AI ethics and
cybersecurity, emphasizing the importance of paying
attention to ethical issues and actively finding
solutions to ensure the healthy development of AI
technology and social stability.
REFERENCES
Fitria, T. N., 2023. Artificial intelligence (AI) technology
in OpenAI ChatGPT application: A review of ChatGPT
in writing English essay. In ELT Forum: Journal of
English Language Teaching (Vol. 12, No. 1, pp. 44-58).
Korteling, J. E., Visschedijk G. C., Blankendaal, R. A. M.,
Boonekamp, R. C. & Eikelboom, A. R., 2021. Human-
versus Artificial Intelligence.Frontiers in Artificial
Intelligence. 622364-622364.
Al Ka’bi, A., 2022. Challenges encountered by artificial
intelligence applications in today’s interconnected
world. In Proceedings of the 2022 International
Conference on Electrical, Computer and Energy
Technologies (ICECET) (pp. 1-6). Prague, Czech
Republic. doi: 10.1109/ICECET55527.2022.9873465
Nguyen, G., Dlugolinsky, S., Tran, V., & López García, Á.,
2024. Network security AIOps for online stream data
monitoring. Neural Computing and Applications, 1-25.
Nwobodo, L. K., Nwaimo, C. S., & Adegbola, A. E., 2024.
Enhancing cybersecurity protocols in the era of big data
and advanced analytics. GSC Advanced Research and
Reviews, 19(3), 203-214.
Gil de Zúñiga, H., Goyanes, M., & Durotoye, T., 2024. A
scholarly definition of artificial intelligence (AI):
advancing AI as a conceptual framework in
communication research. Political communicate-
on, 41(2), 317-334.
Cao, L., 2022. Ai in finance: challenges, techniques, and
opportunities. ACM Computing Surveys
(CSUR), 55(3), 1-38.
Florackis, C., Louca, C., Michaely, R., & Weber, M., 2023.
Cybersecurity risk. The Review of Financial
Studies, 36(1), 351-407.
Huang, C., Zhang, Z., Mao, B., & Yao, X., 2022. An
overview of artificial intelligence ethics. IEEE
Transactions on Artificial Intelligence, 4(4), 799-819.
Ashok, M., Madan, R., Joha, A., & Sivarajah, U., 2022.
Ethical framework for Artificial Intelligence and
Digital technologies. International Journal of
Information Management, 62, 10243.
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