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Volume 4, No. 1 January 2025 - (176-192)
p-ISSN 2980-4868 | e-ISSN 2980-4841
https://ajesh.ph/index.php/gp
AI in Motion: Securing the Future of Healthcare and Mobility through
Cybersecurity
Ahmad Bacha1, Murad Khan2, Abdul Mannan Khan Sherani3, Heta Hemang Shah4,
Noman Abid5, Nahid Neoaz6*, Mohammad Hasan Amin7
1,2,3 Washington University of Science and Technology, United States
5 American National University, United States
4 Independent Researcher, United States
6 Wilmington University, United States
7 Kettering University, United States
Emails: abacha.student@wust.edu1, khanm@students.an.edu2, asherani.student@wust.edu3,
Heta2973@gmail.com4, nomanabid12345@gmail.com5, nahidneoaz@yahoo.com6*,
amin3672@kettering.edu7
ABSTRACT
The development of artificial intelligence (AI) is bringing significant transformation to the healthcare and
mobility sectors, although it is creating new cybersecurity challenges. This research aims to analyze the
cyber threats arising from the application of AI in health and mobility systems, and explore security
measures to mitigate these risks. The qualitative research method included literature analysis and data
collection from various relevant case studies. The results show that AI improves efficiency in the
healthcare sector, such as more accurate diagnosis and personalization of patient care, but also presents
threats such as patient data breaches and algorithm manipulation. In the mobility sector, AI plays a role
in the development of autonomous vehicles and intelligent transportation systems that face the risk of
hacking and data manipulation. Various solutions, such as blockchain, federated learning, and
homomorphic encryption, have been identified as effective approaches to improve AI security. The
implication of this research is the importance of cross-sector collaboration between policymakers,
technologists, and cybersecurity experts to ensure the safe, ethical, and reliable use of AI. With
strengthened security measures, AI can continue to provide significant benefits to society and industry
sectors.
Keywords: Artificial Intelligence, Block Chain, Data Security, Healthcare, Security, Transportation.
INTRODUCTION
Artificial intelligence has been recognized as among the few technology this century that is
gaining traction to revolutionize almost every operation in the mobility sector and beyond
including the health industry. All of these sectors that were all segregated are now merging slowly
under the context of integrated systems and internet of things. This convergence aims at
promoting formation of efficient, accessible and personalized services. However, there is an
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emerging difficulty in adding security as the foundation for artificial intelligence to work in an
innovative society. Using Artificial Intelligence is now in the process of transforming healthcare
starting with diagnosis and extending to treatment and lifestyle of the patients (Syed & ES, 2023).
or instance, in the services of artificial intelligence, the automated algorithms can work for
diagnosing imaging with better efficiency and work in prognosis of diseases and patient
throughput. Nevertheless, with the progression in the healthcare system, there is,_FOR the
digital environment a number of threats as well. Telemedicine services together with electronic
health records and medical devices with which large amounts of patients’ identifying data are
collected and transmitted attract hackers. Threatening any of these areas threatens the safety
and privacy of patients hence why cybersecurity is important when using experts in healthcare
(Reddy & Nalla, n.d.).
In mobility AI is improving the ability to drive automobiles autonomously, intelligent roads,
and real time transportation network. These are all expected to redefine mobility of people and
goods by enhancing safety, efficiency and availability. For instance, self-driving cars apply
artificial intelligence to interpret data that a car sees and where it is to navigate properly. But this
remains a blind spot because mobility solutions directly rely on the connected systems, thus are
vulnerable to cyber threats. Hackers may take full control of this system, manipulate the
transportation of vehicle, the PhISH, or whatever, or even acquire all the details of the specific
user. This is why cybersecurity is now an important instrument in combating the threat to AI-
dependent transport molarity to achieve meaningful outcomes (Rithin Gopal Goriparthi, 2023).
The potential that both AI health care and mobility have are enormous but what it does is only
showcase the plus side’s then increase the negative impact on the other. How people can use
self-driving automotive, for example, ambulances or drones to bring medical supplies in an
emergency? While such innovations could go a long way in changing the healthcare delivery
system some new challenges in cybersecurity ensue. Security regimes must exists and threats
must be identified in a timely manner as well as healthcare and transportation sectors and
technology influencers (Rithin Gopal Goriparthi, 2023). Following is an evolution of AI health care
and mobility with special reference to the fact that security processes are still central to both.
Reducing cybersecurity threats allow various stakeholders to achieve the optimum use of
artificial intelligence systems with safety, ethical implication and reliability within an evolving
technological environment held (Vijayakumar et al., 2024).
Based on the above background, the purpose of this research is to analyze the cyber threats
arising from the application of artificial intelligence in the health and mobility sectors and explore
security measures that can be applied to mitigate these risks. This research aims to provide a
deeper understanding of how AI technology can improve efficiency and effectiveness in both
sectors, but also potentially pose major cybersecurity challenges. The benefits of this research
are to contribute to the development of science in the field of artificial intelligence, especially in
the context of cybersecurity in the health and mobility sectors. This research can be a reference
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for academics and researchers in understanding various aspects of threats and relevant security
solutions. In addition, this research provides insights for policy makers, IT professionals, and
medical personnel regarding the implementation of security strategies in the use of AI. By
understanding the potential risks and solutions, the healthcare and mobility sectors can be better
prepared for cyber threats. By raising awareness of the importance of cybersecurity in AI
applications, this research will help create a safer and more trusted digital environment.
RESEARCH METHOD
This research uses a descriptive qualitative approach to analyze cybersecurity threats to
artificial intelligence (AI) systems in the health and mobility sectors. This type of research was
chosen because it aims to describe in depth the phenomena related to AI implementation and
its impact on data security and related systems. Data was collected through a literature study
that included scientific journals, official reports, and publications related to AI regulation and
cybersecurity. Secondary data sources were used to provide a comprehensive overview of this
research topic.
The data analysis technique used was content analysis, where the data was thematically
analyzed based on relevant categories such as security threats, regulations, and mitigation
technologies. The data is systematically analyzed to identify patterns and relationships between
variables that support the research argumentation. The results of this analysis are expected to
provide relevant insights to address the challenges faced in AI integration in the healthcare and
mobility sectors, particularly in the aspect of cybersecurity.
RESULT AND DISCUSSION
AI in Healthcare: Transforming Patient Care
Artificial Intelligence (AI) is the future of the healthcare industry thanks to it learns and
applies new models of treatment and services that make patients more comfortable and provide
better outcomes. While in diagnostics, treatment and even in customizing the treatments
according to the size of the patient’s body, AI is gradually reintroducing itself into healthcare. This
is important today mainly because most modern healthcare systems are tasked with much higher
expectations due to ageing populations, chronic diseases, and dearth of resources in most
regions of the world where AI can be of value (R G Goriparthi, 2023). However, for one of the
most significant disciplines AI has identified an effective means of enhancing diagnostic
capabilities. It may also be applied for radiologists, that is, for deciphering of roentgenograms,
MRI, and CT, pathologists, as well as other specialists as they are capable to do it quicker and
with almost comparable accuracy. The same fluorescent-based tools at one instance assist in
diagnosing diseases like cancer, heart disorders, and neurological ailments in their preliminary
stage that is compact for discrete survival. For instance, in training of models the large amount
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of data, it can be easier to develop features that may be unnoticed when seeing the images in a
diagnostic point of view of diseases (Damaraju, 2022).
It is also come up with an enhancement of the, the so called personal medicine where
treatment is given according to the genetic information, starting from DNA pattern, life history
and records of the patient. Big data methodology can assist with several aspects to enable the
formation of proper treatment approach in reference to specific data types such as genomic data
or clinical records. One of the main application areas is oncology with the help of AI to identify
specific treatments corresponding to different types of cancer and not just prescribe certain
drugs in the hope that they will somehow affect a particular disease (Chen et al., 2023).
Furthermore, passive applications of diagnosis and treatment, AI is enhancing the patient
surveillance and management. Wearable smart devices with an ML component can observe
patients’ states, and will have a stable foundation for early diagnosing. For instance artificial
intelligence will assist the doctors in the management of the patient in the event that something
abnormal such as irregular heart beating or high blood sugar levels is observed. A number of
these technologies may be very wise for chronic diseases for instance diabetes, hypertension and
heart diseases (Chirra, 2022).
AI is a critical solution to enhancing the functioning of healthcare systems. Asked how
artificial intelligence fits in healthcare, Dr Tomar said: ‘AI fosters productivity in many industries
including working in the hospital and in supply chains.’ Virtual assistant, or virtual health assistant
and natural language processing chatbots are employed for constant patient support, answering
patients’ questions, appointment scheduling and healthcare consultation (Gadde, 2022).
However, the integration of AI in healthcare as we seek to find out in this paper has some
negatives. On this we concur, issues some of which include but data privacy, how these
algorithms is going to be fair, good measures in cybersecurity and some other need to deliberate
on so as to come up with safe artificial intelligence. On one hand, we can explain this by the fact
that in today’s world the focus in AI technology development is made not on the expansion of its
spheres of application but on the development of new systems and their improvement (Syed &
ES, 2023).
AI in Mobility: A new generation in mobility
Modern and complex technologies referring to artificial intelligence AI, are innovating
mobility, developing transport, substituting the previous typical intelligent systems. Beginning
with automated automobile transport to the adaptive traffic management, the integration of AI
is slowly altering the way in which people and goods traverse cities and countries. Not only has
the elements of utility and freedom in movement, but it is also providing solutions to the typical
difficulties, such as traffic congestion, contamination of the environment, and security. Lighting
and traffic signals, are perhaps possibly one of the greatest use cases of how AI can be used to
enhance the environment and traffic (Reddy & Nalla, n.d.). These vehicles rely on high-level AI to
process information coming from such devices as sensors, cameras and LiDARs to co-drive or
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rather to merely observe and make decisions regarding road conditions and situations at the time
of time, on the go. Today, the application of the self-driving cars and trucks for the commercial
business is highly believed to reduce the rate of the fatal road accident which is mostly as a result
of carelessness. In addition, spontaneous fleets may cause developing a delivery cycle and reduce
the delivery time in general, for instance, in e-commerce and supply chain. However, to
encourage more extensive use of the AVs in our society their cybersecurity needs to be improved
so that they cannot be hacked or be involved in any other unlawful operation (Javaid et al., 2023).
AI is also making a massive contribution to public transport through various aspects like the
issues of route, schedules and operating. Current ITS uses AI involves the analysis of information
obtained from the GPS, sensors, and traffic cameras to provide sufficient prediction of traffic
patterns. For example: Currently, traffic light can be programmed to change as per the rush hours
or Cortex can control over the Road transports like Buses, Trains etc., can change their path
depending on delay or event . Of such systems, the general result is that it increases reliability
and decreases time for the commuter. Companies like Uber and Lyft that offer hailing rely on
artificial intelligence for qualifications of customers and drivers, route and demand forecasts
(Paul et al., 2023). In transportation AI is enabling the development of smart city structures that
have interconnected transport systems. By applying movement concepts, numerical population
data and environmental factors, AI assists urban lists to effectively propose efficient low-emission
and environmentally-friendly transportations alternatives. Other standalone electric and shared
mobility markets such as e-scooters and bike are also expected to be supported by AI in terms of
operation and management of fleet, as well as in prediction of maintenance requirements (Merlo
et al., 2023).
Figure. 1 showing challenges of AI in mobility
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Two more critical areas where AI needs to function efficiently in mobility include disaster
and managing health facilities. Mobile clinics with auto-drive and drones can effectively transport
patients or medicine, carry organs to the transplantation place, etc., which is urgent for saving
time in case of emergencies. The following innovations not only transform the way health care is
delivered but also reveal the connection between AI, mobility and others (Cen et al., 2024). This
paper will endeavor to disseminate the information that similar to any other novel endeavor, AI
mobility has its problems and the person can think of the specimens such as; inadequate
regulation, adoption, and cybersecurity threats. Premium self-driving vehicles and smart
automobiles be vulnerable to attack, the car’s safety and confidentiality are in peril. All these
gaps will only be responsive if policy makers, technologists and cybersecurity experts collaborate
closely together. This special section examines current AI trends and how they are likely to shape
the future of mobility: this is making it safer, more efficient and sustainable, as AI alters
movement in a connected world (Desai & Desai, 2023).
The Role of Cybersecurity in AI-Driven Systems
Therefore, there is a great fear as artificial intelligence technologies emerge, and its
relevance to operations such as healthcare and mobility is considered susceptible. Il was already
pointed out that AI systems rely on data and networks, thus, the larger the amount, the more
vulnerable it becomes to cyber threats. The loss of cybersecurity is still an important problem
because with newer technologies like AI, not only can the core formations of its capability be
destroyed but also any data, user security protocols and overall trust in the AI systems (Vasani et
al., 2023). All of this goes to show that cybersecurity must continue to appropriately implement
its uses across industries including healthcare and mobility. In the context of the practical
application, AI-engineered solutions are transforming the sphere of patient care by means of
such tools as diagnostic equipment and treatment and health solutions for individual patients.
However, as medical data is usually private, such systems become highly appealing to hackers in
their turn. Causative attacks in a health records setting can be devastating, which is depicted by
the very high ripper effect; the fruits of a successful attack encompass identity theft, alteration
of data in record, manipulation of AI algorithms used in diagnosing patients and choosing
treatment regimens (Ali & Mijwil, 2024). For example, a hacker can gain unauthorized access to
a healthcare organization and modify the result in a diagnostic AI and end up providing dangerous
diagnoses to the lives of people. It is therefore important for the safety of medical practice and
the privacy of the patient that Health care related AI systems be protected. Several steps which
may include data encryption, executive authentication and surveillance in real time are some of
the steps that is being taken to eliminate any form of attack on healthcare AI systems (Gupta et
al., 2023).
Also, in mobility AI technologies include self-driving car and smart traffic and transportation
management and connectivity which can revolutionary’s urban mobility. However these are
combined or AI systems are greatly dependent on the data from GPS, sensors and clouds etc.
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This makes it very easy for the hackers to do all forms of cybercrimes; hacking, data manipulation
and even denial of service attack (Y. Yang et al., 2023). A specific AI car might also be some sort
of victim in the hack attack or the operational systems of the car might also be captured.
Consequently, protection and enhancement of cybersecurity of mobility solutions with the help
of artificial intelligence is the necessary condition for safety and flawless operation of transport
systems. However, recent developments in Information Technology have shifted the focus to the
defense of the AI systems on which the conventional Information Technology systems are built
(Alchi & Dodiya, 2023). Roughly, the current AI models and, particularly, the models based on
machine learning algorithms have a weakness called adversarial attack, and an attacker can feed
the model with specific input that the attacker wants the model to make wrong conclusions or
decisions at. For example, adversarial attack has the ability to fool an AV AI to not observe a
pedestrian or even misread a traffic sign. To avoid such a shocking incidences of such an attack
of this nature, the AI developers are employing techniques such as the adversarial training in
which an AI goes through several attack processes in the training process (Zhang et al., 2023).
With the growth of the use of AI systems and with the decision making authority of an
organization transferring to the AI systems, accountability for cybersecurity becomes more
important. Improvement of the problems concerned with the absence of both the ideal
explanation and the rules of the regulators is vital in order to create appropriate AI ethical
contract and make AI systematic really clear and to be responsible as the tool which has to gain
public trust and cannot be used as the tool of manipulation (Zhang et al., 2023). Major
stakeholders in AI systems cannot afford to work individually when it comes to minimizing cyber
security threats. Moreso, it means the joys of potential uses of healthcare or mobility can be
realized if everyone starts seeing cybersecurity as the process that can help to either enhance or
mitigate the benefits of the new technology (Argaw et al., 2020).
Cybersecurity Risk Challenges in Healthcare, New Mobility
As the use of AI in health and mobility aspects expand, new cyber threats become apparent,
which threatens everyone. These shifts towards connected, AI based systems have offered a
number of opportunities across these industries. But at the same time it voices primary flaws
that have to be solved to avoid misusage of the new perspectives the union offers. They become
the focuses for developing countermeasures on the new threats as such to safeguard the
efficiency of such developments. In healthcare, one of the most significant cybersecurity threats
stem from the r[acomple:he increasing demand for adopting electronic records, networked
medical devices as well as healthcare systems (Thomasian & Adashi, 2021). EHRs are now firmly
embedded in practice and are resulting in improved access and coordination of patient’s records.
But EHRs are also very attractive to the hacker because he will get the chance to steal the data
that is useful for identity theft or fraud. Also, health facilities are potentially vulnerable to ransom
ware attacks in which attackers are likely to demand for money after locking data. Now that the
current healthcare regulations show that most healthcare systems globally are incapable of
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investing much on strong cybersecurity the risks of data violation and disruption of operations
are very high (Kalid et al., 2018).
Figure 2. showing trends of healthcare in cybersecurity
IT infrastructure is in risk by new types of diagnostic and treatment instruments based on
artificial intelligence, regarding cybersecurity in the healthcare industry. The increased adoption
of such tools increases the concern for the AI systems—the circuit breakers, as it were, in these
support structures are vulnerable to blowing, meaning Covid patients may be misdiagnosed,
given incorrect treatment recommendations, and worst of all, be affected. For instance, a
different attacks could make AI model to classify medical images to the wrong categories and
hence cause high rates of misdiagnosis or false positives (Banos et al., 2014). Similarly in the
mobility segment, artificial intelligence such as self-driving cars, intelligent traffic systems and
transport infrastructure and networks expose the system to cybersecurity threats. Self-driving
cars still become another sweet spot for hackers because they used the network of a set of
sensors and communication system. In the case of an attack on an AV, the best results for the
attackers were complete control of the car, changing the route, or leading to an accident. Besides,
one can easily resemble that insufficient security in V2V and V2I networks can turn the same flow
control into a way of manipulating traffic, interrupting transport means, or threatening public
security. This often means an incident can go a lot more viral because these platforms are
relatively open and heavy on the data (Shukur et al., 2023).
A new concern in mobility is that giant cities’ artificial intelligence impact is exposed to
risks, this which governs traffic, energy products, and services. An act of cyber terrorism, for
instance if performed on the central control center of the city, may cause serious transportation
hitches affecting several services or even lead to citizens’ deaths. For example, an attacker could
specifically change traffic signals to misprogram, or make them nonfunctional, or even install fake
data into smart self-driving platforms to cause disruption of services (Saini et al., 2022). As
societies adopt more of smart technologies, they would merge into a single network and because
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this is under development, the security of such system must be well considered. Also, data
created by AI systems in healthcare and mobility is a relatively new field and it has created a new
threat to cybersecurity. The other issue is related to the protection of data: more and more
specific user data is transferred through the networks and devices. Policies regulating governance
have to ensure that the dato is protected and that AI solutions operate in compliance with GDPR
within Europe or HIPAA within the United States of America (Saini et al., 2022).
To counter the new threats, organizations have to use sophisticated security paradigms
that are distributed across multiple levels. These measures cover everything from enhancing the
encryption paradigms, getting secure techniques of communication, periodical security
evaluation, and using of the AI based threat profiling techniques to recognize the threats in an
organization in real time. However, to reduce the effects of adversarial attacks there is need to
enhance models that can withstand adversarial attack, application of secure software
engineering, and cooperation between healthcare, transport and cybersecurity experts. The use
of artificial intelligence in the procurement of healthcare and mobility is to totally revolution the
most vital sector of our lives with new possibilities of regularity. Regarding these issues, the
stakeholders can be in a position to ascertain the secure as well as ethical usage of the newer
technologies to protect sensitive information, enhance system reliability and gain approval from
the public in regard to embracing the newer technologies (A. H. Khan et al., 2024).
The book Information Technology Solutions to Cyber Security
It remains a necessity and has arguably never been more important since as much as AI is
transforming healthcare and mobility it also poses new threats to cyber security. In recent
complex AI systems, based on data-sets and frequently quite selective algorithms new threats
arise that must be shielded. As a result, many emerging new technology solutions that are being
developed targets to counter the current cybersecurity threats. Of these innovations, these are
not developed with the primary intention of stopping third parties from breaching the system
and attacking it, but for the security of the methods and information that is used in developing
these smart systems (M. Yang et al., 2023). More specifically, this paper recommends identifying
and integrating artificial intelligence threat detection and prevention systems as one of the key
technologies improving cybersecurity to AI systems. In traditional security the tools and
mechanisms utilized are program oriented they function according to designated plan and
signature based detection takes time to respond once a new variant of virus or threat comes up
(Y. Khan et al., 2016). To generate and analyze vast sets of data, plenty of time is needed in the
human system, while AI integrated applications can monitor and spot erratic behaviors that may
look like a hint to a cyber-attack interface. For example, AI is being applied to enhance the speed
of threat detection to help the current security initiatives that scan for the new types of attack
when they emerge. With AI in risk management of hacker opportunities, organizations are able
to enhance security on their AI enriched health and movement applications (Chen et al., 2023).
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Figure. 3 showing cybersecurity analyst rules
Another new idea, also in the process of being incorporated to enhance AI security, is block
chain technology as it starts being applied. Consequently they assert that the simplest use of
block chain is to prevent or reduce incidents of data manipulation since it is distributed and its
core feature is immutability. In healthcare it has been suggested that block chain for the
protection of the data of the patents and in a way that their records and other personal data
cannot be hacked or altered by other people. To this end, in mobility, block chain can help in the
safeguard of exchange of data between self-driving vehicles, the traffic signals and other smart
formations (Qayyum et al., 2023). By integrating block chain in generation of transparent record
of data sharing, the stakeholders can enhance accountability of the AI systems with the two
sectors. Another great technique of making the AI protection even stronger is the federated
learning. In most conventional artificial learning approaches data tends to be centralized,
therefore indicating that very sensitive details are compiled and archived. This of course raises
big questions on the privacy and security of data; and these concerns are especially valid in the
health care scenario where most data is sensitive. Federated learning, on the other hand, makes
it possible to develop AI models through training at such decentralized devices while the data is
still local. This means that the data remains where the data is generated so that the data cannot
be exposed or breached. However, only the more recent findings of the models or new
improvements of models as well are being revealed. This is especially useful in helping to improve
the state of the art of care in the health sector, as it maintains the patient’s privacy while at the
same time training the artificial intelligence from large amounts of data within various settings.
As for mobility it may allow enhancing the AI algorithms responsible for Autonomous Vehicle
control decisions while considering the data backup & preservation (Flynn et al., 2023).
At the moment, two of the most discussed approaches in AI cybersecurity are, indeed,
federated learning and differential privacy. A method of noise addition and removal within the
differential privacy approach prevents the accentuation of the specific data records. Employing
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such technique, data can be trained on AI models in organization without exposing the identity
of the users or patients. Perhaps, especially in the use framework in healthcare where patient
anonymity must be maintained and use framework in mobility where some private data of trip
during commuting is sensitive (Arefin et al., 2017). However, encryption can still be considered
one of the fundamental premises in cybersecurity in the case of the application of AI systems.
New developed encryption approaches such as homomorphic encryption allows the AI models
to process the encrypted data without decrypting them. This is especially important especially in
areas such as the health sector because the information filed under a patient should not be
disclosed. Homomorphic encryption promises that if data is ever leaked, it is as yet safe and used
by AI systems to perform some computations on it without imparting it to others. This innovation
helps to achieve safe ways of transferring data in order to work with other sites such as the
hospitals, research facilities and many other health care facilities without revealing the identity
of patients (Tröster, 2005).
AI is also being used in extending cybersecurity to protect AI models against adversarial
attacks. In these kind of attacks, the attackers exploit their knowledge about the vulnerability of
the design of an AI system to feed the latter with the wrong inputs for the purpose of getting the
wrong outputs. To minimize these risks, researchers are developing processes as adversarial
training, in which an AI is trained with probable harm scenarios (Mazhar et al., 2017). This will
allow the system to learn how to classify adversarial examples before the latter can be defended.
In addition, there is the model robustness testing that is used to identify the security
vulnerabilities in the AI algorithms to enable the developers to apply fix on the vulnerabilities
before the vices are exploited by the wrong minds. This has policy implications in another sense
because the diseases that threaten cybersecurity as a result of applying AI in some major spheres,
including healthcare and mobility ones, require the introduction of completely new facility
(Qayyum et al., 2024). They also incorporate new technologies including Artificial Intelligence on
threat detection, Block Chain, Federated Learning, Differential Privacy, Encryption, and
Adversarial Robustness among others. Cybersecurity solution also has to evolve concurrently
with AI to be the authentic one that will support the progressive technologies’ real growth and
productive application in healthcare and mobility fields. Thus for organizations to get the best
potential it deserves those two fields are worth for organizations to continue to invest in and
develop these innovations hence reducing the risks associated with the two fields (Mijwil et al.,
2023).
Burden of Regulation: Has terrorism also triggered new regulatory standards to cybersecurity
of artificial intelligence systems?
That is why, it is important to build a stable and effective legal regulation of cybersecurity
when AI is introducing in the spheres of healthcare and mobility. These frameworks are needed
to protect against appalling incorporation of AI technologies and to actively counter the
opportunities that exist within systems-level connections. While more different kinds of AI tools
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or applications are applied in different enterprises, data leakage, hack attacks, system or network
failures’ threats are more severe (Neprash et al., 2022). This is why it becomes most appropriate
to establish regulatory benchmarks concerning these risks in order to effectively address them
and ensure important data, patients and members of the public’s confidence in service delivery.
Now, let review the gaps and issues in healthcare industry that impelling the legal demands of AI
cybersecurity to shift based on concerns current issues present in healthcare industry, AI
deploying Medical Device, EHRs, and clinical decision-making. The modern codes and rules
meaning Health Insurance Portability and Accountability Act existing in the United States of
America is oriented on the protection of the patient’s information (Wright et al., 2016). However,
numerous AI implementations in health care require new rules meeting the new challenges that
use of AI algorithms entails. For instance, the diagnostic or treatment recommendation AI models
have to meet certain standards to uncompromisingly call for quintessential standards of non-
ethnic bias and are categorically unsuitable for patient health risk. AI regulation for the
healthcare subsector must also take into account processes of how operations are explained and
how the regulator holds the operator to account, as well as the behavioral psychology of applying
algorithmic systems to clinical decision-making (M. Khan et al., 2024).
That is why by regulating cybersecurity of AI in the sphere of healthcare together with the
protection provided by the advanced privacy laws it is possible to ensure that AI systems meet
the standards and respond properly to the privacy, protecting the data of the patients. click here
The use of any AI tools that acts commercial on health data implies the regulation compliance
with a great number of data protection norms and canons such as encryption, access
management, and others. In addition, any regulatory authorities need to provide definite
opinions regarding the direction of defending this data emerging from the AI systems in
healthcare organizations, generating big data, hack able or otherwise (Lee, 2022). As with most
industries, the regulation of part of the AI value chain is essential for the safe application of AI
technologies in the mobility sector as their tools – Self-driving vehicles, intelligent roads, and
connected transportation systems. According to the level of the development of emerging
technologies in the automotive market, cybersecurity in self-driving automotive vehicles is one
of the most complex problems as such automobiles consist of interconnected systems of sensors
and communication platforms as well as artificial intelligence algorithms. Several academic
programs in cybersecurity are required as self-driving cars will be on the road and constantly
communicating with other cars and traffic infrastructure – these MAY BE vulnerable to cyber-
attacks that endanger the lives of passengers and disrupt the functioning of cities and their
transportation networks (Husnain & Saeed, 2024).
Therefore, the right authorities should develop cybersecurity standards for connected and
autonomous vehicles (CAVs). These standards should therefore aim at providing preventive
measures against the threats like: remote hacking, GPS spoofing and data manipulation amongst
others. Further, it must be required for the manufacturers to maximize, and bi-safeguard the
Ahmad Bacha, Murad Khan, Abdul Mannan Khan Sherani, Heta Hemang Shah, Noman Abid, Nahid Neoaz,
Mohammad Hasan Amin
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Volume 4, No. 1 January 2025
higher levels of cybersecurity characterizing AV during their construction as well as operation.
But the public policies needed to support and ensure that the information sharing between
automobiles, roads, other entities, and automobiles and other systems are secure and are
protected from invasions of the privacy rights of violation of the private data. Next, there is the
use of Artificial Intelligence in traffic and energy in smart cities; and use of AI in Public service
delivery is also another regulatory consideration (Rawat et al., 2022). These are measures that
have to be undertaken to avoid utilization of any loophole within the various components of the
whole smart city system by the cyber criminals; hence the regulation structure for smart cities
has to incorporate certain specific rules concerning data protection, manner of communication
in smart cities and risk management. Imperative to these frameworks is the shielding of the
current and potential smart city environment from cyber security risks; therefore, engage cross-
sectional multi-stakeholders, including government ministries, technology developers, and city
planners (Husnain et al., n.d.).
In addition to Sector specific regulation, there is also a provision on international
cooperation for cyber security matters connected with AI. Every country and every geographical
area has its legislation in place regarding the protection of the data as well as cybersecurity, which
finally does not give a chance to establish the particular list of norms regarding the safety of
artificial intelligence at the international level. The GDPR in European Union and other
regulations in correspondent countries are precedent for cybersecurity of AI based on the
principles of data protection and privacy (Chernyshev et al., 2019). I found, nevertheless, that to
counter threats that imply global participation one must call for international participation in the
creation of predictable and easily enforceable rules for AI safeguard. This includes the
establishment of principles, regulation and the application of best practice, layered security
features and the demonstration that the AI technologies that underpin the solutions are applied
in an open, ethical and secure, without prejudice to location. An example of Aval’s international
cooperation is an AI Cybersecurity Initiative that was launched by the Organization for Economic
Co-operation and Development (OECD), the purpose of which is to ensure the AI technologies
are used safely and securely in other countries (Shihab & AlTawy, 2023). This has to be present;
because with the creation of such systems, the developers and users of such artificial intelligence
based system will be forced to engage in banking related activities within stipulated ethical
standards; there are always risks of hacking that would make these systems irrelevant.
In this regard, there is an adequate demand for regulating measures for cybersecurity of AI
systems to minimize risks of artificial intelligence technologies Is linked to risks for the health care
section and mobility CLU. These frameworks should present answers on issues to do with privacy,
protection, transparency, and responsibility concerning the artificial intelligence technology for
the safety, efficiency, and resourceful use of AI (Saeed et al., 2024). As AI technologies continue
being adopted further and as they advance, it shall be important for the governments, other
regulatory authorities, as well as the industries come up with kinds of cybersecurity regulations
AI in Motion: Securing the Future of Healthcare and Mobility through Cybersecurity
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that should cater for the dynamic, complex nature of AI structures. By so doing, it creates an
environment that will ensure people are safe, one can build credibility as well as offer a way
through which the enhancement of AI within various fields can be enhanced.
CONCLUSION
The research highlights the transformative potential of artificial intelligence (AI) in various
sectors, including healthcare, mobility, and city management. The integration of AI has
significantly enhanced efficiency and innovation, but it has also introduced critical cybersecurity
challenges. Sensitive patient data, smart car systems, and connected urban environments face
risks from malicious actors who may exploit vulnerabilities in these systems. This underscores
the importance of robust preventive measures, such as Threat Intelligence, Blockchain
technology, Federated Learning, and advanced Encryption methods. These cybersecurity
solutions are critical for safeguarding AI systems, ensuring data integrity, and maintaining the
trust required for the responsible use of AI across sectors.
The findings of this study contribute to a better understanding of the interplay between AI
advancements and cybersecurity, providing a framework for future research and innovation. In
the health sector, this research emphasizes the need for security standards that protect sensitive
patient information, while in mobility, it underscores the importance of regulations to secure
autonomous vehicles, connected infrastructure, and smart cities. Moving forward, this study
advocates for collaborative efforts to refine cybersecurity measures in tandem with the evolution
of AI technologies. By addressing these challenges, AI can continue to transform industries
responsibly, enriching society and unlocking its potential as a revolutionary tool for progress.
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Ahmad Bacha, Murad Khan, Abdul Mannan Khan Sherani, Heta Hemang Shah, Noman Abid,
Nahid Neoaz, Mohammad Hasan Amin (2025)
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