Challenges and Solution for Artificial Intelligence in Cybersecurity of the USA

Running head: ARTIFICIAL INTELLIGENCE IN CYBERSECURITY OF THE USA 1
Challenges and Solution for Artificial Intelligence in Cybersecurity of the USA.
Vishal DineshKumar Soni
Department of Information Technology
Author Info :
Vishal DK Soni
Department of Information Technology
Campbellsville University
Campbellsville University
Electronic copy available at: https://ssrn.com/abstract=3624487

ARTIFICIAL INTELLIGENCE IN CYBERSECURITY OF THE USA 2
Abstract
The development of Information Technology can make a computer to act and think like humans.
AI is an exceptional aspect of information technology that requires the event of a machine that
reacts and works as a mind of the human. The artificial intelligence' key features include the
human senses' analogy. The system is capable of recognizing touch and speeches as features that
are placed within the system for running the normal life situation's potential activities without the
assistance of humans. Artificial intelligence, however, is the intelligence' agents' study that takes
the environment's condition and achieves its goal successfully. The majority of the systems in the
computing World are built for serving the purposes as per the situation's nature with the unique
features' application from the human's aspects' natural existing. Artificial intelligence is generally
an associate of the humans that apply problem-solving techniques and learning for understanding
activities' high levels in operation of the human-inspired elements, decision-making, and
emotional cycle. As opposed to human intelligence, artificial intelligence is machine-based
intelligence. This research paper is aimed at evaluating the current challenges related to artificial
intelligence for cybersecurity in the United States. The research paper will propose the
innovative solution for Artificial Intelligence in cybersecurity of the USA.
Keywords: Artificial Intelligence, Machine Language, Cybersecurity, Cyber Defense.
Introduction
In history, technology is at a point of inflection. Machine Learning and Artificial Intelligence are
advancing in a fast way as compared to the ability of society for absorbing and understanding
them; computing systems that employ Machine Learning and Artificial Intelligence

simultaneously are turning out to be more critical and pervasive. These new abilities can make
the World more affordable, safer, and ecologically sound; on the other hand, security challenges
are introduced by them that could jeopardize private and public life in the United States. Though
interchangeably used often, the terms Machine Learning and Artificial Intelligence indicate two
consistent concepts. Artificial intelligence, coined in the 1950s, is the computer science field that
shows the programs aimed at modeling "intelligence." This, in practice, means algorithms that
can learn or reason given the necessary base knowledge and inputs and are used for tasks, for
example, autonomous decision-making, recognition, and planning. Machine Learning is a
specified branch of Artificial Intelligence that uses algorithms for understanding phenomena'
models from experience or instances. The term AI will be used throughout this document to
discuss the topics that apply to the broader field. There are numerous challenges associated with
AI. AI systems must be secured, which comprises comprehending what it indicates to "be
secured" for them. Moreover, the techniques of AI in cybersecurity could alter the existing
asymmetric balance of defender-versus-adversary. These advances' accuracy and speed will
allow systems for acting autonomously, for reacting and defending at wire speed, and for
detecting covert and overt adversarial attacks and surveillance. Consequently, securing the future
of the Nation necessitates considerable research investment in both cybersecurity and AI. This
research paper is aimed at evaluating the current challenges related to artificial intelligence in the
United States. The research paper will propose the innovative solution for Artificial Intelligence
in cybersecurity of the USA.

Literature Review
The areas of AI on a very broader account are divided into several categories. These categories
are presented in Figure 1.
The United States has been advancing its technology from earlier years to present days, and the
focus of the country is still on the future for making better attempts of altering Artificial
Intelligence (Bland, 2016). The United States is making changes rapidly for elevating the future
in mobility, along with shaping the movement in all the industries from one place to another. As
per Bostrom (2016), with the help of increasing the technology market by making the global
population more dependent on Artificial Intelligence, the United States is making the entire
World an entrepreneur (Li, 2017). The United States significantly supervises the future's
prognosis by converting the existing problems and opportunities as active "hearts" of the future
drivers of organizational technology (Barden, 2013).
The simulation of human intelligence processes by machines has always aided in decision
making by many business organizations in this contemporary World. Many business enterprises
have embraced AI to help in the production and planning processes inside and outside their
business premises. AI aids in making vital business decisions in charge of the proper running of
firms (Jarrahi, 2018). Such processes include managing consumer relationships as well as
increasing efficiencies in operations.
Figure 1: Illustration related to the relationship amid the AI's diverse fields (Oke, 2008).
AI comprises physical and cognitive tasks' automation. It assists business people in performing
tasks better and faster, along with making better decisions. It allows the decision making

automation without the intervention of humans. Automation can be enhanced through AI;
consequently, minimizing tedious tasks and intensive human labor (Buckley, 2015).
With the current trend in the use of AI in business organizations, companies have continued to
explore opportunities and experiments to help improve business plans in the coming ages. The
growing interest from every industry to utilize AI emerges from the benefits such as increased
sales, improved customer experience, and minimizing labor-intensive operation expenditures
(Jarrahi, 2018). It has also incorporated machine learning, Chatbots, and robotic process
automation, which are increasingly becoming the trends in business related-technologies used by
contemporary businesses. 
Figure 1: Illustration related to the relationship amid the AI's diverse fields (Oke, 2008).
AI comprises physical and cognitive tasks' automation. It assists business people in performing
tasks better and faster, along with making better decisions. It allows the decision making
automation without the intervention of humans. Automation can be enhanced through AI;
consequently, minimizing tedious tasks and intensive human labor (Buckley, 2015).
Business is increasingly using IoT, which is a linkage of physical objects such as smart devices,
home appliances, and machines that get connected to the internet. It has aided businesses in
communication, collection, and sharing of useful data vital for business processes. The trend is
on the rise due to the fourth industrial revolution, which shows that businesses are gaining a lot
from it now and even shortly. IoT has provided businesses with real-time control over its
processes, improved customer services, and employee productivity (Wortmann & Flüchter,
2015). The digital transformation trend is continuing to grow at a higher rate, and businesses
have to capitalize on their potentials to stay in the industry.

Artificial intelligence assists in supporting numerous industries within the United States. This
technology's application in within the country is full in several manufacturing fields,
environmental engineering, security, healthcare, and education (Makridakis, 2018). Within
society, a crucial developer is Artificial intelligence. This technology's actual application is

actively more within the security and manufacturing sectors in the United States (Wasilo &
Thorpe, 2019). The United States significantly supports communication and learning with the
help of the individual programs by installing these special programs into AI (Jackson, 2019).
Every country around the globe requires security for political stability and the growth of the
economy. The United States applies much of the cost of intelligence in the safe for protecting its
economic interest and integrity in contradiction to terrorism threats. The Americans face more
risks in terms of security, and with the application of this technology, the United States
maximizes safety within the country's sensitive areas (Kolivand et al., 2018). AI assists in
reducing contact between humans; therefore, it increases the chances for the operation to detect
the terror risks at several levels. The infrastructure of Information technology and data protection
are among other areas of computing that need higher security from AI gadgets to scan the
specific areas' security. The United States implicates the intelligence system's usage with the help
of reinforcing the military facilities and has been valuable and shown the impacts of fighting
against terrorism. It has been argued by Liu et al., (2017) that the application of AI is a valuable
factor in facilitating the security aspects in the significant areas, such as government treasure
centers and airports. The security issues are of extreme importance, forcing the United States to
build a plan for future AI systems that will assist in eliminating every problem related to the,
along with stopping the terror groups' normal operations (Leylavi Shoushtari, Dario &
Mazzoleni, 2016).
The United States is the current leader of AI development geared toward assisting the
manufacturing sector. Within the United States, the industry of manufacturing is also a
significant commercial building factor. Therefore, the country focuses more on this sector

(Bland, 2016). The intelligence services systems within the manufacturing industry assist in
contributing more in the production. In the sets of industrial applications, automation is
accelerating manufacturing with the help of using AI in building non-stop products because of
the availability of such technology within the USA (Thornhill, 2016). The USA is rapidly
developing AI systems, and microchips have been imported by it from foreign suppliers for
building AI systems in the manufacturing sector. The ability to make sound AI systems in the
manufacturing industry is on computing power availability (Huang, 2004). To address this
situation within the USA, the administration has set a lucrative step for future technology
development, making different strategies to build a newer generation system fit future variations.
Even though AI is applied by the countries in the manufacturing sector, the labor market is under
more significant disturbance. The available jobs, on the other hand, are substituted by the AI
system within the USA economy. Because of this, there has been an improvement in the growth
of production in overall industries (Thornhill, 2016). For instance, the bottlenecks are identified
by the organizations in the packaging factories with the help of the automatic AI systems,
predicting failures, and decisions related to automated processing. Consequently, the United
States is applying AI in the manufacturing sector as compared to any other industry.
The AI systems have played a vital role in the healthcare sector by enhancing the welfare with
the improvements in the human environment and healthcare. It assists in setting good
governances, along with increasing renovation and innovation within the community. Scientists
within the USA uses AI for detecting and studying the ecosystem's scientific issues. AI systems
are also used widely in medical services as they assist in developing specific and unquestionable
applicability in predicting epidemics' emergencies and treating population. The social activities

are impacted by the AI, along with creating both negative and positive perceptions toward the
new AI services available within the community. Although the system's target is the building and
improving lives, the knowledge might alter to complicate the needs.
Within the United States, healthcare facilities are advancing, just like the facilities of healthcare
other developed countries like China. AI development in the facilities of healthcare has been one
key factor in building trust in the healthcare sector in the future. Data standards' implementation
in studying a particular issue related to health might be reliant on AI systems. Between the
machine operation and humanity, the interoperability makes sure that there is a success because
of the advancements in AI systems at every level within the United States (Farajpour &
Zerehnazi, 2013). This particular technology runs on a specific platform to establish a broader
expert team of the intelligence system and humans that can interrelate for providing effective
services in the medical field.
Artificial Intelligence for Cybersecurity in the USA
Investments of AI needs to advance the practice and theory of secured construction and
deployment of AI-enabled system. Substantial efforts in managing AI are required for producing
secure pieces of training; defend models from investigation and confrontational inputs; and
verify the robustness of the model, privacy, and fairness. This comprises secure methods and
decision-making based on AI for the regular usage of environments and AI-human systems. An
engineering discipline, practice, and science will be required for the AI integration into cyber-
physical and computational methods that comprise the distribution and collection of an Artificial
Intelligence corpus—involving systems, datasets, and models—for validation, research, and
education. Research investments for cybersecurity need to apply AI systems within critical

infrastructures to assist in resolving the persistent challenges of cybersecurity. Existing
techniques comprise monitoring of the network for software analysis techniques, detecting
anomalies to identify code's vulnerabilities and systems of cyber reasoning for synthesizing
defensive patches at an attack's first indication. AI systems can perform these analyses in seconds
rather than weeks or days; in principle, cyber-attacks could be defended and observed when they
take place. However, there would be a need for safe deployment in comprehending the
implications of several dimensions and these AI actions'actions.
The AI-systems require just as innovative cybersecurity methods and tools for improving their
resiliency and trustworthiness; AI can be used by the cybersecurity in the USA for increasing
consciousness, reacting in real-time, and enhancing its effectiveness overall. This comprises
adjustment and self-adaptation against the continuing attacks that change the existing attacker-
versus-defender irregularities. Strategies that assist in identifying the weaknesses of the
adversary, using methods of observation, and gathering learned lessons, can use AI for
categorizing several sorts of attacks, along with informing adaptive response at scale, for
example, quickly finding inconsistencies and know the way for repairing them. Cyber defenders'
expert's small team can protect networks effectively. That same system protection level could be
extended by using AI, making it abundant, and providing the domain knowledge essential for
addressing aspects; for example, such as degradation-of-system conducts and quality-of-service
restraints.
Enhancing the Systems' Trustworthiness
The technologies of AI can assist in capturing and processing the massive amount of data that
technology systems of the current period produced. This ability, in turn, helps in providing the

training data required for driving AI-system development and innovation. Aligned with priorities
of cybersecurity, reasoning based on AI could make both human-in-the-loop and fully automated
systems more reliable. Two proposed areas are the deployment and creation of more reliable
identity management and software systems. The proposed solution comprises leveraging AI for
detecting every error in programs, along with checking best practices, identifying vulnerabilities
in security, as well as making the designing of security easier for software engineers to
implement in the systems. The code often changes quickly in modern practices of development.
It would be valuable to use "coding partners" based on AI to assist analysts and developers who
are less experienced in comprehending complex, large software systems, and advising them on
the strength of proposed code changes and security. Furthermore, AI can help in steadily
operating and deploying software systems. After the development of the code, it is recommended
that AI is used for detecting low-level attacks, along with inspecting for logic errors, or
application configuration and domain, providing best practices for securing system operations,
and monitoring networks. The development of open-source software offers a high-impact and
unique opportunity for AI-based security improvements because of its prevalent usage by
government and commercial organizations in the USA. Though, because of its public nature,
there are many vulnerabilities of open source by an AI-based rival (Obeidat, North, Richardson
& Rattanak, 2015).
One more proposed area for AI is the access control and identity management. Many techniques
can be compromised by adversaries only by stealing tokens of authorization. It is recommended
that a system based on AI should be used grounded on a history of expected behavior and
interactions that are also difficult to circumvent, transparent, and lightweight. AI can reduce

threats and enhance accuracy for systems of biometric authentication. AI monitoring of
interactive patterns, however, could result in violations of privacy. There is a need for further
research for developing methods that think through the technical and ethical aspects
(Shneiderman, 2020).
Independent and Semi-independent Cybersecurity
Unlike other productive applications of AI, it is expected that AI can be used by both the
defenders and attackers in scenarios of cyber defense. The integration of AI has changed the
traditional strategy based on the elimination of susceptibilities or increasing an attack's cost.
Human-in-the-loop systems and systems that are independent of human actions must be prepared
for the worst cases, along with anticipating, responding, and analyzing occurrences of actual and
potential threats. Decisions based on AI affect multiple stakeholders, including system operators,
service providers, and data owners (Drmola, Pavlíková, Maďar, Budirská, Suchý, Harašta, &
Schmidt, 2015). The critical considerations at this point are how the stakeholders are informed
and consulted regarding autonomous operations, along with the way the decision making is
constrained and delegated. It is expected that independent attacks can be faced by cyber
defenders at numerous levels: classic deterministic planning can be used by the raids in a stable
cyber surrounding; attacks might comprise planning under indecision where there is the
uncertain environment; when attackers are little aware of the situation, they could use AI for
obtaining information, learning the way for attacks, executing survey, along with developing
strategies that comprise the victim network's model, and the products of cybersecurity.
Techniques and methods are required for making installed systems' resistance to autonomous
attack and analysis. The innovative and proposed technologies comprise the automated isolation

(e.g., restrictions based on behavior), defensive agility (i.e., usage of updates and simulations for
strengthening the defenses), and strategies that are mission-oriented (e.g., usage of domain
experts for categorizing responses and attacks). A mission-driven AI system always needs to
incorporate the intent of the organization's leader into decision-related to the security (e.g.,
operation of and access to the system). The techniques of AI can translate operations order or a
mission briefing into something addressable by an autonomous system of decision (Drmola,
Pavlíková, Maďar, Budirská, Suchý, Harašta, & Schmidt, 2015).
Within the security engineering, the involved mission execution and planning can be supported
by AI. It is proposed that AI is used for the identification of the vital cyber assets, along with for
realizing that these can change with the change in the goals or purpose of the mission. It can
assist in identifying and prioritizing the data's appropriate aspects, computation, classification of
information, and other security factors comprising the continuing adaptation of the AI (Schuster,
2018).
Predictive Analytics for Security
Predictive analytics that process external and internal information for assessing the probability of
a successful attack will be beneficial for the cybersecurity. The initial work has developed
techniques to earlier identification of confrontational operations in the lifecycle of an attack with
the help of using streams of data; for example, the cyber-relevant activity's distributed logs, or
traffic on the dark web. A lot of work can be seen for identifying linkages and patterns amid
datasets that connect human and the cyber domains, taking the edge of prior knowledge to
augment, discover, and tracking newer campaigns and activities. There is a need for further
research for uncovering the intent of adversary, capabilities, and human operators' motivation,

mainly when the defenses of a system are being tracked (Abraham & Nair, 2015). Further than
merely the factor of success/failure and detection, information regarding attacks can assist in
protecting methods and sources, along with providing new insights for improving resilience with
time. Focus areas comprise successful adaptation, operational security, and data sources. For
predictive analytics, obtaining the real, labeled, and clean data required is a challenge. Specific
options comprise lowering the "labeled" threshold for leveraging smaller sets of data, making
more realistic synthetic training data, and using unconventional streams of data. False flags can
result in collateral damage or misattribution when diverse AI analytics and datasets are used for
monitoring, tracking, and countering cyberattacks. Consequently, a higher validation standard
than other intelligence issues might be required for AI analysis of cyberattacks. Research is
needed for performing the multimodal examination, cross-validation, and identifying possible
flaws, risks, or gaps in the datasets.
New insights can be provided by the AI analysis that can assist in reducing operator error in both
contexts, including human-on-the-loop and human-in-the-loop, providing more sureness in the
results, and supporting larger systems in adapting with time. This type of analysis may involve
the system's internal state, the way the patches are applied regularly, the security controls
present, and the situational awareness level.
Conclusion
In history, technology is at a point of inflection. Machine Learning and Artificial Intelligence are
advancing in a fast way as compared to the ability of society for absorbing and understanding
them; computing systems that employ Machine Learning and Artificial Intelligence
simultaneously are turning out to be more critical and pervasive. This research paper reflects

information collected from a diverse set of engineering and scientific experts, suggesting that the
AI's future rests on the USA's ability to balance challenges and benefits of AI, mainly in the
cybersecurity area. It must be noted that the discussion represented above is from a single time
frame. The rapid technological advancements, new domains of application, and the interaction
among cybersecurity, AI, and ML will introduce new challenges and opportunities. The national
and international thinking regarding these issues as such is expected to alter with time, and these
insights and questions will require a review and a periodic update. The United States has been
advancing its technology from earlier years to present days, and the focus of the country is still
on the future for making better attempts of altering Artificial Intelligence. Artificial intelligent
assists in supporting numerous industries within the United States. Investments of AI needs to
advance the practice and theory of secured construction and deployment of AI-enabled system.
Substantial efforts in managing AI are required for producing secure training; defend models
from investigation and confrontational inputs; and verify the robustness of the model, privacy,
and fairness. The technologies AI can assist in capturing and processing the massive amount of
data that the current period's technology systems produce. Unlike other productive applications
of AI, it is expected that AI can be used by both the defenders and attackers in scenarios of cyber
defense.

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