Artificial Intelligence and Machine Learning Algorithms Are Used to Detect and Prevent Cyber Threats as Well as Their Potential Impact on the Future of Cybersecurity Practices

International Journal of Computer Science & Information Technology (IJCSIT) Vol 17, No 1, February 2025
DOI: 10.5121/ijcsit.2025.17101 1
ARTIFICIAL INTELLIGENCE AND MACHINE
LEARNING ALGORITHMS ARE USED TO DETECT AND
PREVENT CYBER THREATS AS WELL AS THEIR
POTENTIAL IMPACT ON THE FUTURE OF
CYBERSECURITY PRACTICES.
Md Al Amin
St. Francis College MS in Information Technology
ABSTRACT
As many devices connect to the internet and people save more information on the cloud and increase their usage
of digital communication, the threat level on the information sphere has increased significantly. With the
increase of device connectivity and use of money transactions cyber threats have become more sophisticated
than ever. Conventional discrete security policies are ineffective to address the behavioral and constantly
changing nature of today’s cybersecurity threats such as zero-day exploits, APTs or ransomware. In this light, AI
and ML are considered strategic technologies, which define cutting-edge, intelligent, adaptive as well as
scalable cybersecurity solutions.
AI and ML are very effective in the processing of big data from a variety of sources including the networks
traffic, logs, and endpoints behavior to detect, analyze and contain threats in real-time. In contrast, these
technologies update their algorithms over time depending on new information and are therefore capable of
addressing hitherto unseen vulnerabilities. Behavioral analytic system, Intrusion detection and prevention
system is the most used technologies to detect, analyze and prevent cyber threats. For example, ML can pick out
‘anomalous user activity’as a sign of insider threats or stolen login credentials while AI-based systems can stop
malicious actions by analyzing network traffic in milliseconds.
In addition, through AI and ML the threats are analyzed with a focus on the risk in the future based on the
previous exposition. That proactive capability alone cuts response time so organizations can take more effective
measures to enhance security and mitigate cyber threats to growing connectivity and increasing security on
digital transactions. that organizations can do a better job of managing risk. Another way that AI helps in the
incident management is making automated response mechanisms for such situations, and a system can
effectively counter spontaneous threats than coming in physically. Even though they still present a number of
difficulties, these technologies have certain special benefits. The use of AI by cyber adversaries by means of
data poisoning attacks, algorithm shifting attack as well as adversarial AI generate new forms of risks. The
criticisms regarding data privacy, bias and self-accountability of AI regarding increased cybersecurity also need
to be settled.
All in all, the use of AI and ML in cybersecurity is a game changer in terms of the identification, management,
and prevention of threats. These technologies bring in the flexibility and wisdom to protect the current
information territory from emerging threats. However, for their deployment to yield these benefits, solid
frameworks should support them to reduce risk and enhance reliability. AI and ML will remain essential tools as
the information sphere develops further, in order to protect the information space.

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KEYWORDS
AI, Defensive AI, Zero-Day Threats, Threat Mitigation, Behavioral Analysis, Compromised Accounts, Real-Time
Monitoring, Dynamic Analysis.
1. INTRODUCTION
Information space has grown rapidly due to smart devices, cloud computing, and modern
developments in communication technologies in the past few years, which makes systems extremely
vulnerable to cyber threats. The ongoing growth of digital commerce and the internet of things has
made people more subject to cyberattacks. The vulnerabilities about the main critical systems are
gradually increasing concerns as devices get intertwined and data rapidly shared between one
platform to another. It has been found that as the number of connected devices and networks rises,
there has been tremendous pressure on conventional security models to contain the number and level
of threat, including ransomware, phishing attacks, APTs and zero-day attacks [1], [2]. This has made
organizations vulnerable to more threats hence necessitating change in the way security is done.
Conventional security models are largely conceptual, and act based on certain rules and suggested
countermeasures regarding cyber threats. This kind of model worked well in previous years and
earlier times when threats were calculated and automated, not to mention constant. These
conventional methods involve slow reactions most of the time. It provides attackers with ample time
to penetrate into networks wreaking havoc but before adequate counter measures are deployed. The
inherent limitation of traditional approaches is that they do not incorporate a means of generating new
rules when the organization confronts novel threats it has never faced before.
However, recent developments in machine learning (ML) and artificial intelligence (AI) offer an
innovative solution for all of the issues associated with standard cybersecurity frameworks. AI and
ML have given the cybersecurity systems a pl atform to be intelligent and dynamic in their approach
to threats and safeguards them against impending threats. These technologies use big data techniques
to analyze normal behavior within the systems and networks they protect and thus be able to spot such
changes and anomalies as a sign of an attack. The speed at which AI and ML can sift through vast
amounts of data makes them superior to human analysts and analysts’ traditional tools, when it comes
to identifying new, quite often unique threats that may otherwise go unnoticed in the sheer volume of
network traffic, system logs, and endpoint interactions[3].
The most beneficial aspect of AI and ML in cybersecurity is the capacity to recognize the deviations
from the standard activity patterns. Of all AI categories, behavioral analytics has been found to be
rather effective in detecting lateral movements and insider threats. By studying user’s behavioral
pattern or daily routine, then an AI system can identify what is a typical trace for a particular user or
group of users. When activity is clearly outside of this norm— For example, if an individual is pulling
a file at 3 a.m., or downloading tremendous levels of information—AI models can quickly identify
this as potentially problematic. This gives a strong mechanism to detect the illicit activities that occur
in the network before they cause severe harm that would be fatal in case they are a breach.
Along the same line, AI and ML are applied to protect it from invasions through techniques known as
Intrusion Detection and Prevention Systems (IDPS). These systems receive and analyze traffic in a
real-time basis and thereby have the capability to detect any form of unlikely malicious activities on
the network. Structurally, AI is the only viable approach since it is capable of continually

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incrementally learning from the traffic at hand and detecting and mitigating threats that would
otherwise take human analysts a lifetime to even identify.
Apart from the behavioral assessment and intrusion assessment, AI and ML also feature in the
prediction section of threat intelligence. In the past, threat intelligence involved analysts analyzing
and use their skill and knowledge of the attack. This approach can be useful, but it is limited when the
next threat that has not been discovered is being pushed. Because predictive threat intelligence thus
leverages AI and ML, in that it uses data mining, threat repositories and historical attacks to provide
systems a way to forecast the next types of threats likely to occur, this dynamic is altered. Machine
learning algorithms used can detect patterns on how attackers are planning on launching their attacks
and facilitating organizations' ability to protect against new dangers. This predictive capability enables
the security teams to take pre-emptive measures, apply security fixes, and incur security mechanisms
to prevent exploit of the vulnerability thus limiting the attackers’ window.
In addition, with AI and ML, there is automation, which is vital in dealing with incident response, and
risk management functions. Reactive measures taken by the hands will likely take time and therefore
the attackers will have a field day making more advances. As AI mechanisms help identify, analyze
and respond to incidents, the overall response time and the impact of attack can be minimized greatly.
This puts the attack out quickly in addition to letting data security specialists avoid lower-level work
by handling critical tasks such as the identification of the root cause of the attack and work on
strengthening the company’s defenses.
However, AI and ML have become common in cybersecurity, though there is a drawback that comes
with it. While these technologies are as strong as they are, they do not exempt talent cybercriminals
from influencing them. The exploitation vulnerabilities of data and algorithm are another conducing
factor to AI in cybersecurity, and the general risk is data poisoning, where a perpetrator feeds an AI
system with a wrong or detrimental input data set. Such an attack might involve poisoning the data on
which the identified AI models depend and thus deceive the system into ignoring real threats or
otherwise labeling potentially dangerous activities as harmless[5]. Data poisoning attacks are
dangerous insofar as they go, and if they are not detected sooner than later; hence there should be a
need to put in place measures that can protect AI models from such attacks that compromise the
security and functionality of these models.
Another issue that has also been addressed is the vulnerability to AI being launched an attack on other
AI. It is however worthy of noting that just as the concept of AI and ML has continued to develop so
has the approach taken by hackers to subvert the same. For example, adversarial machine learning
procedures can always modify the inherent models of the AI systems just to be less precise and also to
generate a large number of false positive alerts to the security teams. These attacks can put a lot of
pressure on AI security solutions, and in fact, threaten an organization’s overall security position.
2. BACKGROUND AND RELATED WORK
Prior work in AI and ML in cybersecurity is concerned with improving the ways of detecting,
preventing, and managing cyber threats. IDS is one of the specific topics that must be addressed,
where various methods namely supervised, unsupervised and reinforcement learning are employed in
looking for signs of mischief in a network. This is more effective than the traditional approach of
threat identification, which is time-consuming. Security activity monitoring techniques track the
activity of users with the intention of detecting insiders and accounts that have been compromised by
comparing present activity with typical activity. It is also used in ransomware detection by analyzing

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file behavior, system transactions and network Unfamiliarity patterns to indicate a ransomware
incident. Further, ML-powered threat intelligence patterns the potential threats and strengthens
preventive solutions in cybersecurity. The implementation of XAI seeks the enhancement of the trust
relationship that exists between AI and human analysts in an organization. However, there are still
OBJECTIVES, such as adversarial attacks counter and data biases, that can be considered as the
promising directions for developing AI- based cybersecurity solutions further.
2.1. AI’s role in behavioral analysis as applied to cybersecurity.
This work explores how AI-backed behavioral analyze s tools work to monitor and alert on insider
threats, compromised accounts, and unauthorized access. The historical usage data, behavior patterns,
baseline, and dynamic analysis for real-time threats are all highly important in this method.
These inside jobs are becoming more technical to pull off and with the use of compromised accounts,
intelligent behavioral analysis tools from AI are critical in cybersecurity. These tools use gathered data
about past usage and analyze current behavior and activity to produce deviations from normal and
uncover danger before they occur.
In a financial institution, the workers can get under one roof, a consolidated database that has details
of their respective clients. For protection from malice and account abuse, a behavioral analytics
system based on AI technology is used [4]. The system monitors the activities of the employees so as
to determine the normal behavior of such employees. For instance, it learns an employee’s log-in
times, their normal interaction with the files and their overall usage of data. This provides the system
with a baseline to determine what is active or normal to the case at hand, and what is suspicious.
There is a day when the system registers suspicious use of an employee’s account. The login occurs at
2:00 AM from a foreign IP address which is very peculiar from the normal usual flow. The account
starts to gain access to other administrative files such as the confidential ones the user has never had a
chance to open. In addition, a huge number of files are downloaded within a short time exceeding the
amount the employee normally downloads, of over 200MB.
These irregularities are quickly detected by the AI system and prompt an automatic corresponding
response. Immediately, the cybersecurity team gets an alert, access to those sensitive files becomes
locked to avoid further leakage and an email is generated to the employee to confirm the worrying log
in.
When looking carefully, the cybersecurity team finds out that the account was affected by a phishing
attack. The attacker was interested in exfiltrating data, but due to the effective AI system, it was
prevented on the spot.
This case is a clear example of how great value AI is to security systems, specifically behavioral
analysis. This set of tools uses historical data and real-time monitoring to analyze risks and
immediately respond to threats, reducing consequences and improving organizational defense in
general.

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2.2. Intrusion detection system IDS using; Machine learning is used here for detecting
the intrusion.
Explains how ML algorithms can improve intrusion detection in more detail with reference to the
ways in which it improves upon the analysis of traffic and patterns of such networks through
algorithms. Supervised, unsupervised, and reinforcement learning methods of building IDS models
are also discussed.
The traditional IDS has changed with the help of ML because the systems have learned how to
analyze the traffic and detect new threats. Most of the existing IDS work under the traditional
approach that depends on pattern matching technology and therefore they lack capability to detect
new variants of known attacks or previously unseen attacks known as zero-day threats [2]. These are
handled by ML, whereby algorithms are used to probe through the network traffic in order to deduce
abnormalities.
An example of practical usage can be observed in the case of a multinational company that decided to
install an ML based IDS, in order to regulate the company’s network. The system learns the normal
traffic and the traffic which is malicious through prior labelled data such as Random Forest or SVM.
These algorithms gain capability to differentiate between normal and anomalous traffic depending on
characteristics such as size of packet, type of protocol, and source/destination IP addresses. In live
traffic, the system works in parallel with the learned data pattern and immediately raises an alert for
any suspicious activity. For instance, it detects a Distributed Denial of Service (DDoS) through
analyzing a pattern of repeated traffic from servers that are most likely to result in a malware
infestation so that it can isolate servers that are under attack.
Besides supervised learning the IDS utilizes unsupervised learning techniques, for example clustering
(K-Means or DBSCAN; which identify new threats that the system has not encountered before.
Unlike the supervised methods, these algorithms classify traffic behaviors and mark anything that
deviates from the grouped pattern as an intrusion. For example, an insider threat might be identified
when an employee downloads massive quantities of data to an external server at a time when such
action is rare. This is a typical system event, and the security team is informed of the change.
The coexistence of supervised and unsupervised learning facilitates the implementation of ML-based
IDS in both a known and unknown level threat. These systems learn new patterns of attacks, can
differentiate the real threats from noise, and minimize the number of false alarms. Consequently, IDS,
powered by ML, enhances network security profoundly, providing organizations with a leading
defense against constantly developing threats.
2.3. Threat Intelligence Using Machine Learning
Machine learning threat intelligence allows organizations to leverage past experience by identifying
potential threats and eliminating them. Learning from past attacks, weaknesses, and dangerous
actions, Unsafe areas explain new threats and provide recommendations for protective cybersecurity
actions. It is this approach that turns conventional disruptive measures to positive action, thereby
using predictive analysis to counter threats.
An autonomous organization adopted an advanced guard solution of Threat Intelligence through the
use of Artificial Intelligence. The platform initially processed quantitative data such as previous

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campaigns, malware type, attack patterns, vectors and external qualitative data that encompass the
dark web activity, reports of newly discovered bugs. From this, the system extracted information of
unauthorized discussions on hacker forums, regarding a recently developed ransomware, which is
aimed at enterprise cloud storage solutions. Based on this understanding, the system related this
information with data from previous ransomware attacks to gauge that the ransomware would
leverage unsolved vulnerabilities in cloud service APIs.
The data mining showed working results for the cybersecurity team as it leveraged the importance of
patching the several cloud API’s considered high risk. The system also documented signs of the
ransomware propagation including large numbers of files encrypted and large volumes of CPU usage.
The use of predictive analysis allowed for details regarding an expected schedule of the attack to be
identified in advance so the organization could construct defenses appropriately.
Several weeks later, the platform used to monitor the organization’s movements noticed some illicit
operation in the cloud servers, comparable to the forecast of the ransomware. Notification was raised
and all the related systems were quarantined to ensure that the ransomware did not lock crucial data.
The predictive intelligence helped make sure that potential vulnerabilities in the organization were
protected to prevent an attack.
This example shows how integrating ML-based threat intelligence into cybersecurity improves the
process by learning from past data and subsequently predicting possible risks in future. This proactive
approach enables the organizations to avoid risks, be prepared for the worst and guaranty quick
response to the new emerging threats.
2.4. Adversarial Attacks in AI Security Systems
They take advantage of the defects in deep learning AI and are a major threat to AI security systems.
These attacks are data poisoning, by which the training data is contaminated to poison the model, and
evasion attacks for which the adversaries slightly alter the inputs to get past the detectors. They both
expose the weakness of AI models and also stress the importance of a strong countermeasure against
various forms of sabotage. For instance, let us consider an intelligent anti-virus system designed to
smooth through different files, driving how dangerous or safe they are, relying on metadata and file
actions. The risk of a more invasive type of attack called data poisoning is that a hacker can feed the
system with wrong examples of the training data set. Such contaminated samples could involve
inserting harmless appearing files that could have negative attributes masked from the model.
Consequently, the system loses its efficiency, and more malwares can escape its notice during the real-
time running of the program. Of course, in an evasion attack the hacker could create malware
precisely to avoid detection by the given anti-virus. It alters all aspects of the malware’s code
slightly—changes that are invisible even to human researchers—and guarantees that the AI model will
classify it as benign. This particular strategy of attack takes advantage of the model's inability to
generalize and depends on a small number of parameters.
There exists a need for protection against adversarial attacks. Techniques such as Adversarial training,
where during training models are exposed to adversarial examples increase robustness. Other
approaches are: Adversarial decision trees, Anomaly detection system which alerts for suspicious
behavior, Defensive distillation which reduces the effect of small input perturbation. Moreover, it is
beneficial to explain which decisions an AI model has made, as well as which areas can be vulnerable,
with the help of explainable AI methods.

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The present work presents the issues of the adversarial attack and asks questions about the possibility
of exposing different AI methods to an attempt at manipulation in the present analysis. Defending the
AI models against adversarial interference continues to remain a critical challenge particularly as
hackers become smarter in their operations.
Figure-1: Adversarial Attacks in AI Security System
3. HANDLING REAL-LIFE INCIDENTS USING AI
Risk management of actual cyber occurrences with the help of artificial intelligence has changed the
speed of the threat’s neutralization. Since the system employs the use of AI, the process of identifying,
analyzing and implementing countermeasures for security threats takes a relatively short period than it
would take when done manually. Technologies such as SIEM use Artificial Intelligence for network
monitoring as well as threat identification and management with necessary responses.
In real life, an ML integrated to the SIEM platform can process large volumes of data from firewalls,
IDS and endpoints, among other sources. For instance, whenever a SIEM system identifies multiple
geographical locations attempting to log in within a few minutes – as is typical of a credential-stuffing
attack – the AI algorithms analyze the situation in real-time. By assessing the contextual information
of the compromised systems, user activity profile and intelligence feeds they assess the likelihood of
active threat.
It then launches automated playbooks, a pre-set response framework meant to counter given risk
factors. Possibly, the steps of the playbook may be, for example, the blockage of the account, enabling
of the MFA for all accounts and blocking of the IP-addresses at Firewall. Since decision algorithms
are built on AI, these actions are prioritized according to threat level and how important the
compromised systems are.
However, apart from simple responses, an AI system also provides rich context for each incident to
the cybersecurity team and identifies the root cause and system that was impacted. This allows the

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security teams to work on high priority incidents and provide informed decisions. For instance, if the
SIEM identifies patterns that suggest phishing attacks against employees, it can send a message to
notify users and adjust the firm’s spam filters to filter out such emails.
The use of AI in managing incidents and executing decisions real-time thereby strengthens an
organization against cyber threats. They make certain that the actual real-life cyber security incidents
are handled effectively with less response time, repetitive tasks, and more insights.
4. AI IN CLOUD SECURITY
Cloud security is an area where artificial intelligence is critical in solving issues that are specific to
cloud environments, beginning with scalability and dynamic systems and including shared
responsibility models. AI is also used in high-advanced security for data and individuals’ identities, as
well as continuous threats detection in cloud systems.
Cloud computing is especially exposed to unauthorized entry, data violation, and misconfigurations.
These problems are solved by AI handling the constant observation of users’ behavior and their access
to the program. For example, in analyzing login activities, geo location and device information the AI
models is able to detect inconsistencies. Let us consider a case where a user tries to log into the cloud
application from an unknown or unfamiliar location or device, then the system will alert and may
require entering more facts, for instance, MFA.
Another aspect where AI enhances the cloud security aspects is Data protection. Machine learning and
artificial intelligence depend upon encryption management, anomaly detection and automated
compliance checks apart from safeguarding the cloud data [6]. For example, if an AI model identifies
abnormally frequent data traffic such as the downloading of many files from a cloud storage service,
then the model can immediately limit the access, notify the security team and slow down the data
leakage.
AI can learn a lot about threat detection in cloud environments due to the capability of incorporating
big data to analyze them in real-time. The implemented machine learning routines can detect
previously unnoticed exploits, including zero-day malware, based on the patterns akin to the
dangerous behavior. For instance, in a cloud infrastructure, AI systems may find a distributed denial
of service (DDoS) attack since the traffic origin of an attack differs from the expected from a single
and multiple source; the system automatically countermeasures when it scales up the resources to
handle the load or blacklists Ip addresses that are considered malicious.
There is also misconfiguration which is one of the biggest threats within cloud environments that is
solved by AI. AI-assisted technologies look for vulnerabilities, for example, overprivileged access
settings in networks and also offer AI-based solutions to fix them. Using AI, organizations can
guarantee the constant fulfillment of security policies, make timely prevention of threats, and provide
the security of cloud infrastructures.

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Figure-2: AI in cloud security- Benefits and Use Case
5. AI FOR DETECTION AND PREVENTION OF RANSOMWARE
It is now impossible to walk through the corridors of the modern organization without accidents with
AI and ML – these are indispensable assets that help prevent ransom attacks and analyze files,
monitor transactions on a network. The current forms of ransomware protection are based on the
signatures for the threats and the methods have their problem when facing the new ransomware
variants or using the techniques for the first time. The above models have limitations due to the
inability of rule-based systems to identify new suspicious patterns and anomalies in real time; this is
however overcome by AI-driven models.
AI models help pattern and observe the behavior of files and access to them and such to identify
ransomware activity. For instance, ransomware may crypt hundreds and thousands of files in minutes,
such a rate is easily captured by monitoring file modifications with AI. When something like unusual
file encryption activity is detected, the AI-based systems immediately generate alarms and can also
halt the process. Likewise, if a system starts writing files in encrypted form with extensions that are
characteristic of ransomware, the AI alerts that the system has been infiltrated.
AI continues to perform well in network activity analysis which is paramount in combating
ransomware. For instance, ransomware interacts with command and control (C2) servers to get
instructions or to transfer encrypted keys. Using the ML algorithms, AI is able to identify such
communication patterns as outgoing traffic or connection with IPs and isolate the affected devices. An
example would be filtering domains out that are found in threat intelligence lists which are related to
ransomware traffic[7].
Further, system transaction monitoring is another part in which AI boosts ransomware protection. The
AI models can set up and monitor regular patterns of system functioning and can detect abnormalities
like granting unpopular user permissions, process that tries to access forbidden directories. These
indicators may be deployed in advance to prepare the ground for ransomware attack and, hence, they
may trigger instant containment actions.

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For instance, a financial institution had applied an AI-based ransomware detection capability which
enabled it to mitigate an attack at its infancy. The tool considered a workstation whose encryption
activity was different from the average detected connections to a domain with known malicious
content. The individually-set workstation was isolated by the system and the connection was severed
before all the data could be encrypted and lost.
When applied to file behavior analysis or network monitoring, as well as scrutiny of system
transactions, AI performs a 360-degree assessment of ransomware threats. The important fact of its
capability to learn new ransomware variants protects adequately from these growing complex threats.
6. HUMAN – AI PARTNERSHIP IN THE COURSE OF CYBERSECURITY
One of the most important trends in current security is the symbiosis of artificial intelligence and
human analysts who are capable of cooperating as a single team to handle modern threats. Artificial
Intelligence in this case is not expected to supplant the role of the analyst but enhance him or her by
enhancing ability to process data that can be used to identify, respond or even prevent emerging
threats. This collaboration maximizes the strengths of both humans and machines: the capabilities of
the computer, in terms of the speed and optimization of AI and the problem solving and
interpretational abilities of human beings.
AI systems work well with big data processing, real-time analysis of large datasets and finding
correlations while humans may not while working on them. For instance, in an APT case, an
algorithmic system might identify suspicious horizontal mobility within a network or find new forms
of malware. Nevertheless, these alerts are frequently detailed and need human input for verification,
analysis, and assessment of the resulting actions. There is a way for the analysts to find out more
about the context of the activity as something which might be risky or suspicious in the given
environment, including the organizational knowledge and business goals as well as threat intelligence.
In this partnership, XAI intervenes as it helps in the improvement of trust and system usability.
Classical approaches in AI called ‘black box’ do not give insight into how they make decisions and
that creates skepticism among analysts. XAI is relevant in dealing with this issue since the model
seeks to explain the actions or recommendations of an AI system. For instance, an XAI-based
cybersecurity system can inform an analyst that an alert was generated because user ‘A’ opened files
on server ‘B’ that are normally accessed on server ‘C’ after three incorrect login attempts. Of course,
such transparency makes it easier for the analysts to determine why certain decisions were made by
the AI and this leads to better responses to be given with more confidence in the machine.
Furthermore, when humans and AI work together, the process is always improving. AI systems can be
valued for responding to a particular situation by telling analysts either that something might be
considered a false positive or, conversely, that something might have been missed as a threat. Such an
iterative process makes it possible to develop AI in parallel with the emergence of new cyber threats
and still maintain connection and compliance with man authority and knowledge. Thus, people and AI
enhance a more robust and flexible cybersecurity model for countering new or continually emerging
threats by automating cybersecurity processes while making important decisions.
7. METHODOLOGY
The methodologies in this research focus on the impact of AI and ML regarding the dynamic practice
of cybersecurity, which then results in quick, precise, and ever-evolving actions against threats.

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Altogether, every methodology provides an association for threat discovery, analysis, and strategy,
developing a comprehensive defense mechanism for organizations.
7.1. Analysis of Behavior in Cybersecurity
Behavioral analysis with reference to Artificial Intelligence involves tracking of user activities and
identification of any form of deviation from the normal activity through benchmarking of the real time
activity. Described below are usage histories to which normal patterns such as login time and file
access routines are computed on. Electronic control in AI systems constantly checks for instances of
activity such as foreign IP logins or even large numbers of files downloaded. For example, a financial
institution’s AI system detected suspicious behavior when an employee’s account was accessed at 2:
from a foreign IP and as such immediate mitigation measures were put in place at 00 AM. This
methodology focuses on the strength of how insiders’ threats and compromised accounts can be
identified and responded to with the aid of AI backed tools.
7.2. IDS with Machine Learning Approach
MLS extensible IDS employs the use of logics of artificial intelligence in dealing with traffic in search
of threats. In supervised learning, Random Forest and SVM train traffic data sets then separate it into
traffic that is normal and traffic that is malicious. Other techniques like K-MeANS or DBSCAN in
unsupervised learning are grouping the traffic patterns so as to identify emergent type of anomaly, for
instance the zero-day threats. An example in real life involves using packet size and frequency to
detect Distributed Denial of Service (DDoS). This even orientation makes IDS flexible to focus on
both existing and emerging threats to ensure it is very effective at stopping modern, advanced cyber
threats.
7.3. Managing Incidents with the help of AI incorporated SIEM
SIEM solutions based on artificial intelligence collect information from firewalls, Intrusion Detection
Systems, and endpoints to evaluate threats in real time. AI compares login information, activity, and
intelligence feed to identify cases like credential-stuffing attacks. Countermeasures procedural
‘scripts,’ which are carried out automatically, include account deactivation, enforcement of MFA for
the accounts concerned, and quarantining of the infected computers. Furthermore, AI offers detailed
incident content, which helps teams to concentrate on the most important problems and study the
sources of the occurrences. For instance, AI remains capable of adapting spam prevention and
informing the relevant users of the phishing attempts. This approach reduces response time and also
improves the decision-making process.
7.4. The Positives of Risk Management through Artificial Intelligence
Risk management is revolutionized by AI because of the automation of threat recognition, threat
assessment, and deployment of counter measures. Supreme AI systems work with large data sets to
eliminate threats within a short time. Risk management decisions are heeded according to the threat
level and the significance of the system affected. Measures of flexibility like enhancing filters or
putting out alerts guarantee the optimum protection from new threats. For example, AI can prevent
dummy IP addresses from connecting and can freeze user accounts during an attack. It enhances the
organization’s capabilities to handle incidents and increase effectiveness in case of an incident
occurrence.

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7.5. Case-Based Analysis
Real life examples support the approaches used confirming the fact that AI is reasonable in each case.
For example, an IDS that relied on artificial intelligence identified the insider threat that put in place
multiple huge downloads that are unauthorized. Likewise, an AI supporting SIEM was able to disrupt
a phishing campaign by studying users behavior and adapting counter measures. These examples
clearly depict endow AI for monitoring different types of threats with easy efficiency to highlight its
importance in the current world’s cyber safeguard.
8. APPLICATION
Integrating Artificial Intelligence and Machine Learning into cybersecurity delivers immense value
and alters the way that organizations identify, avoid, and mitigate cyber threats [1]. The first benefit is
threat identification in a real-time sense; large data sets are scoured by the AI and ML for threats
beyond the capacity of analysts. Al this leads to faster response rates and also cuts on the amount of
risk sustained by the affected company.
The same way AI learns from the pattern that exist in the data it also makes it accurate in terms of
identifying new types of threats that are potential such as the zero-day vulnerability and the advanced
persistent threats. Furthermore, and primarily, machine learning allows for constant improvement
through the use of such concepts as adaptive learning in which models can improve or modify
themselves and their approach through new data and patterns of attack.
AI also executes process-oriented tasks, as logs analysis and incident prioritization, and emancipates
cybersecurity experts from mundane work. Also, AI-Integrated systems utilize resources most
effectively since the alerts concerning threats are presented under different priorities so that critical
threats are the most urgent. Lastly, AI and ML greatly enhance cybersecurity by offering solutions to
increasingly complex threats at a considerably faster, more accurate, and on a large scale.
Currently, AI and ML are transforming cybersecurity from an almost entirely traditional prevention
measure to a proactive approach to defence. When it comes to appreciation of these technologies in
relation to their development these technologies are expected to evolve even further so as to get rid of
these ever-changing threats in the cyber space. Key developments include:

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8.1. Autonomous Cyber Défense
Stand Alone Self-Acting describes systems based on AI which work independently to identify, control
and dismantle Cyber-Security Threats without human inputs. These systems employ the use of
machine learning algorithms to study the patterns of flow of traffic in a network, the resulting logs
from applications and the behaviors of users to detect signs of threats. Different from them is the fact
that they are capable of making changes to their strategies of attack in the light of previous lessons.
For example, when a new type of the virus exceeds the protection, the system modifies its diagnostic
programs in order not to allow such cases again. The autonomous defense is most useful in extensive
networks for the reason that there is simply too much data being processed to allow for personal
monitoring. These systems are designed to work in reverse or real time and help quarantine affected
devices, kill the offending process, and prevent the leak of the hole across the network, which greatly
improves an organization’s security.
8.2. Countering Quantum Computing Risks
That is why with emergence as a new technology of quantum computing, it poses great threats to
traditional cryptographic solution. This is where quantum computing will help to develop quantum
immune algorithms while using AI and Machine learning to show the vulnerabilities in current
encryption models. Same technologies shall also assist in hindering the attacks that will be made
possible by quantum capabilities, ensuring security and reliability of data in the new quantum world.
Therefore, further development of capabilities in the context of using AI and ML in cybersecurity will
follow the relevance of the adaptability of AI-based systems and integration of cooperative learning
technology, optimizing interfaces between human and machines, and readiness in using quantum
technologies in attacks; this prepares a highly progressive agenda for PACS.

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8.3. Self-guided Cyber Security
On the basis of AI, Stand Alone Self-Acting defines systems that implement a sole mode of operation
to detect, monitor, and neutralize Cyber-Security Threats without human intervention. These systems
utilize the technique of machine learning to analyze flow of traffic within the network; logs from
applications; and the behavior to look for signs of threat. Unlike them they can mutate their strategies
of attack in the light of lessons learnt from previous encounters. For instance, when a new type of
virus goes beyond the protection, the system adjusts the diagnostic programs to ensure that it is
impossible to record such incidences. And finally, the autonomous defense is most useful in extensive
networks for the reason that, well, there is simply too much data being processed, that personal
monitoring cannot possibly occur. These systems should work inactive or in real mode and assist in
isolating compromised systems, terminating the suspicious process, and stopping the leakage of the
hole across the network, which enhances an organization’s security significantly.
8.4. Federated Learning
Federated learning is an innovative approach where awesome AI models are trained over
decentralized data, say, several organizations or several devices without getting into the particulars
with raw data. In cybersecurity this approach promotes partnership between organizations in the
improvement of threat detection and response as well as privacy and data protection and compliance
with General Data Protection Regulation. For example, the federated learning system is in a position
to identify the attack pattern that has been noticed in different companies while it is not forwarding its
internal data. This also suggests that there is a better and more encompassing logic for coordinating
the act of protecting cyberspace that Favors all actors. In the same regard, federated learning also
preserves the data security at the time of training by employing the encryption and secure multiparty
computation technologies that make it the strong and innovative technological platform for the
collective defense against cyber threats that are challenges of the contemporary global society.
Artificial Intelligence in Formal Decision Systems Rom a purely reactive practice to a strategic and
predictive defense strategy. As advancements in the field of technology are regarded, these
technologies are poised for further evolution so as to do away with the constantly evolving threats in
the cyber world. Key developments include:
8.5. Interpolating AI into Formal Decision Making
The latter is applicable in enhancing the general capacity of human managed cybersecurity teams by
providing new syntactic and/or semantic perspectives and/or recommendations. In cyber events, [SIH]
treats threat information, network occurrences, and system opportunities in pursuit of the finest
response strategy. For example, if the ransomware attack is identified then an AI system may
recommend that servers must be shut down, some specific IP address should be blocked, and backups
must be utilized. The selection strategies are very fast especially when human operators are provided
with several response choices with other subsequent events expected by the AI. These tools are very
useful in some ways, especially when used to reduce the volume of load to the analyst who deals with
a large number of alerts. Last, the access of the artificial intelligence as well as integration of artificial
intelligence in decision making assist in eliminating the hypes between automation and work from
incidence handling.

15
8.6. By The Use of Countermeasures
Arguably one of the main issues that quantum computing is going to condemn about present day
cryptographic practices is with regards to attacks from algorithms like Shor’s. Of such risks, AI is
anticipated to play the leading role in addressing them. Firstly, AI can assist in developing new one’s
post-quantum cryptographic algorithms for communication security as well as the safeguard of
information in the future [8]. Second, AI systems are capable of the analysis of threats related to
quantum technologies and also can simulate respective attacks on existing infrastructures. For
instance, AI can recommend systems that are most susceptible to quantum decryption, and which may
require change in their encryption paradigms. Besides, it also reveals opportunities for organizations
changing the current protocols, which could make them quantum-safe when using new technologies
as it would be easier to automate the alteration too. This approach will safeguard the information from
its future threat that quantum computing will pose.
9. DISCUSSION
On the same note, [4] and [2] pointed out that Artificial Intelligence (AI) and Machine Learning (ML)
are revolutionary features in cybersecurity.
[4] in their paper, The AI Revolution in Cybersecurity identified that behavioral analysis tools as a
result of artificial intelligence are critical for dealing with insider threats and accounts that have
become compromised. They state that due to these attributes, it can create behavioral profiling and
detect deviations that suggest an attack is under way. This research further augments their works by
showing real-world examples like a financial institution that uses AI to recognize abnormal login
timings and unauthorized file downloads where AI could play the part of stopping security breaches
triggered by insiders.
[2], in their work "Intrusion Detection Systems with Machine Learning: “A Comprehensive Survey,”
in order to understand how ML refines the recognition of known and novel risks. They illustrate the
major disadvantages of IDS, based on the static approach and pattern matching that have been
described above and show how approaches based on supervised learning (for example, Random
Forest) and unsupervised one, such as K-Means clustering can help detect anomalies and even zero-
day threats. This research enhances their work by providing a real-world implementation of the
framework and the application of ML for DDoS attack and traffic anomaly-based insider threat
identification.
10.Conclusion
Several AI techniques are applied to cybersecurity as they allow for real-time threat detection and
response, dynamic approach to threat intervention, and intervention based on new patterns in
cyberspace. These technologies improve intrusion detection solutions, behavior profiling, threat
intelligence and ransomware protection with preventative tools that help mitigate exposure and
reaction time. Indeed, incorporating human-centered solutions with artificial intelligence tools makes
frameworks much more effective in cybersecurity. Nonetheless, adversarial attacks, and data biases
show that the problem requires proper defense and ethical application. In total, AI and ML are the
critical components for constructing secure digital environments and developing new approaches to
fighting complex threats and protecting today’s information assets.

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REFERENCES
[1] A. ALSHAIKH, M. Alanesi, D. Yang, and A. Alshaikh, “Advanced techniques for cyber threat
intelligence-based APT detection and mitigation in cloud environments,” in International Conference on
Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), P. Loskot and S. Niu, Eds.,
SPIE, Jun. 2023, p. 65. doi: 10.1117/12.2681627.
[2] S. Ali, S. U. Rehman, A. Imran, G. Adeem, Z. Iqbal, and K. Il Kim, “Comparative Evaluation of AI-Based
Techniques for Zero-Day Attacks Detection,” Electronics (Switzerland), vol. 11, no. 23, Dec. 2022, doi:
10.3390/electronics11233934.
[3] Ö. Aslan, S. S. Aktuğ, M. Ozkan-Okay, A. A. Yilmaz, and E. Akin, “A Comprehensive Review of Cyber
Security Vulnerabilities, Threats, Attacks, and Solutions,” Mar. 01, 2023, MDPI. doi:
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[4] N. Kaloudi and L. I. Jingyue, “The AI-based cyber threat landscape: A survey,” Jan. 31, 2020, Association
for Computing Machinery. doi: 10.1145/3372823.
[5] B. Dash, M. F. Ansari, P. Sharma, and A. Ali, “Threats and Opportunities with AI-based Cyber Security
Intrusion Detection: AReview,” International Journal of Software Engineering & Applications, vol. 13, no.
5, pp. 13–21, Sep. 2022, doi: 10.5121/ijsea.2022.13502.
[6] U. A. Butt et al., “A review of machine learning algorithms for cloud computing security,” Sep. 01, 2020,
MDPI AG. doi: 10.3390/electronics9091379.
[7] A. Al Mamun, H. Al-Sahaf, I. Welch, and S. Camtepe, “Advanced Persistent Threat Detection: A Particle
Swarm Optimization Approach,” in 2022 32nd International Telecommunication Networks and
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[8] P. Radanliev, “Artificial intelligence and quantum cryptography,” Dec. 01, 2024, Springer Science and
Business Media Deutschland GmbH. doi: 10.1186/s40543-024-00416-6.

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