ARTIFICIAL INTELLIGENCE IN THE SECURITY SYSTEM OF ENTERPRISE

Summary. Artificial intelligence (AI) is transforming a number of business functions, including enterprise security systems. Recent years have seen an increase in the use of AI in security systems to detect and mitigate security threats. The purpose of this study is to investigate the use of AI in enterprise security systems. The study aims to identify the novelties in the application of AI to enterprise security systems, the research voids in the existing literature, and the benefits of AI to enterprise security systems. Existing research on the applicability of AI in enterprise security systems will be analyzed through a literature review methodology. The study will shed light on the use of artificial intelligence in enterprise security systems and its future implications.

Cyberattacks have become more complex and frequent as the use of technology in business operations has increased.Cybersecurity is therefore becoming increasingly important for businesses.A security compromise can result in the loss of sensitive data, financial loss, reputational harm, and legal liability.Therefore, enterprises must invest in comprehensive security systems to safeguard their assets and customer data.
Novelties (research gaps): Artificial Intelligence (AI) is a relatively new technology with the potential to revolutionize enterprise security systems.AI can be used to detect and mitigate realtime security threats.However, there is limited research on the application of AI to enterprise security systems.This study aims to identify the novel applications of AI in enterprise security systems as well as the research voids in the existing literature.
Research Objectives: This study's primary objective is to investigate the use of AI in enterprise security systems.The specific objectives of the study are as follows: • to identify the novel applications of artificial intelligence in enterprise security systems.
• Existing literature on the use of AI in enterprise security systems will be analyzed to identify research gaps.

STRUCTURE OF THE PAPER
The main sections of the paper are the introduction, the literature review, the methodology, and the main body.The introduction provides context for the problem, the novelties, and the research objectives, as well as the paper's organization.Existing research on the use of AI in enterprise security systems is analyzed in the literature review section.The methodology section provides an overview of the research design, data, and analysis tools.This section of the main body discusses the function of AI in enterprise security systems and provides code SECTION XX.INFORMATION TECHNOLOGIES AND SYSTEMS examples to illustrate its application.The conclusion section summarizes the study's main findings and makes suggestions for future research.
LITERATURE REVIEW Application of AI in Security Systems In recent years, artificial intelligence (AI) has been a rapidly expanding discipline with numerous applications in industries including healthcare, finance, and security.Particularly, AI has demonstrated its potential to improve enterprise security systems by detecting and preventing security hazards.The purpose of this literature review is to provide an overview of the existing literature on the use of artificial intelligence in enterprise security systems.
Machine learning and deep learning algorithms have been utilized to detect anomalies in network traffic data.For instance, Kumar and Kumar (2020) proposed an approach based on machine learning for detecting network anomalies in enterprise networks.The authors classified network traffic as normal or anomalous with an accuracy of 94.45% using a Random Forest algorithm.
In addition to network traffic data, AI has also been applied to system records to detect anomalies.Zhang and Yang (2020) proposed an unsupervised deep learning method for anomaly detection in enterprise system records.The authors employed a stacked autoencoder to discover the normal behavior of system records and to identify anomalies that deviate from this behavior.The proposed strategy obtained an F1 score of 0.904%.
AI has also been implemented in enterprise security intrusion detection systems (IDS).A network intrusion detection system monitors network traffic and system records for indications of malicious activity.The use of AI in enterprise security systems is not without its challenges and limitations.A significant obstacle is the lack of interpretability of AI algorithms.Since the majority of AI algorithms are black machines, it is difficult to comprehend how they reach their conclusions, making it difficult to trust the results.In addition, AI algorithms may be vulnerable to adversarial attacks, in which malicious actors manipulate input data to circumvent the security system.
In conclusion, AI has demonstrated a great deal of promise for enhancing enterprise security systems by detecting and preventing security hazards.To truly realize the potential of AI in enterprise security, however, there are still obstacles and limitations that must be overcome.To develop more robust and interpretable AI algorithms and to resolve their vulnerabilities of AI algorithms to adversarial attacks, additional research is required.
AI is a technology that enables machines to autonomously learn from data and make decisions.AI can be used in enterprise security systems to detect and mitigate security hazards in real time.
AI can be integrated into enterprise security systems in the following ways: ➢ AI can detect and prevent unauthorized access to enterprise systems through intrusion detection.Algorithms based on artificial intelligence can identify anomalies in user behavior and potential security concerns.AI can detect bruteforce attacks, fraud attacks, and malware attacks, among others.
➢ AI is also capable of detecting and preventing fraud in enterprise systems.AI algorithms can identify peculiar patterns in financial transactions and detect fraudulent activities.
➢ AI can be used to collect and analyze threat intelligence data from a variety of sources.Algorithms based on artificial intelligence can recognize patterns and trends in cyberattacks and provide insight into potential security concerns.
➢ Security operations: Artificial intelligence can be used to automate security operations and enhance incident response times.AI can produce alerts, investigate security incidents, and respond to security threats automatically.➔ AI can automate security operations and reduce the workload of security personnel, allowing them to concentrate on higher-priority duties.
➔ Accuracy enhancement: AI algorithms can analyze large volumes of data and accurately identify potential security hazards.
➔ AI can use predictive analytics to identify potential security hazards before they occur, allowing businesses to take proactive steps to mitigate risks.METHOD This study employs a qualitative research design to investigate the application of artificial intelligence to enterprise security systems.The data for this study was compiled by conducting a comprehensive literature review on the subject.Various academic databases, including IEEE Xplore, the ACM Digital Library, ScienceDirect, and Google Scholar, are searched as part of a systematic approach to conducting the literature review.This review's search terms include "artificial intelligence," "security system," "enterprise," "machine learning," "deep learning," "intrusion detection system," and "authentication." The selected articles are then analyzed to identify research gaps, novelties, and trends in the application of artificial intelligence to enterprise security systems.The analysis is conducted using a thematic approach that identifies the major themes and subthemes in the literature.The themes that emerge from the analysis are then utilized to organize the body of this research paper.
Research Design This study's research methodology is a literature review.Existing research on the use of AI in enterprise security systems is systematically analyzed as part of the literature review methodology.The literature evaluation will identify the novelties and research gaps in the application of AI to enterprise security systems.Data Secondary data obtained from academic periodicals, conference proceedings, and pertinent industry reports were used for this study.These databases include Google Scholar, Scopus, and IEEE Xplore.
Tools for Analyzing Research This study's data will be analyzed using qualitative analysis techniques.The process of systematically analyzing data to identify patterns, themes, and relationships is called qualitative analysis.The analysis will entail a comprehensive examination of the collected data to determine the novelties in the application of AI to enterprise security systems, the research gaps in the existing literature, and the benefits of AI to enterprise security systems.
MAIN PART: The Function of AI in Business Security Systems

CONCLUSION
In conclusion, the use of AI in enterprise security systems has a number of advantages, including the detection of threats in real time, increased efficiency, enhanced accuracy, and predictive analytics.It is possible to use AI algorithms to detect and prevent unauthorized access, detect and prevent fraud, collect and analyze threat intelligence data, and automate security operations.The use of artificial intelligence in enterprise security systems is still a relatively new area of study, and several research voids must be filled.This study's findings can assist businesses in comprehending the novelties in the application of AI to their security systems and in taking proactive steps to mitigate security risks.
Raju et al. (2020) proposed a hybrid IDS that detects network intrusions by combining deep learning and fuzzy logic.The proposed method obtained a precision of 99.71%.Diagram above: security classifications for Artificial Intelligence Moreover, AI has been utilized to improve enterprise authentication systems.Li et al. (2021) proposed an authentication system based on facial recognition for mobile devices in enterprise networks.The authors utilized a convolutional neural network (CNN) to recognize the user's visage and authenticate them based on their facial characteristics.The proposed system obtained a 97.6%rate of accuracy.

Fig.
Fig. Changing Cybersecurity's Future with an AI-Driven Approach import pandas as pd from sklearn.ensemble import IsolationForest # Load data data = pd.read_csv("financial_data.csv") # Fit the Isolation Forest model model = IsolationForest(n_estimators=100, max_samples='auto', contamination=0.01)model.fit(data.drop('Class',axis=1)) # Predict the outliers y_pred = model.predict(data.drop('Class',axis=1)) # Add the predictions to the data data['Outlier'] = y_pred # View the results print(data.head()) СЕКЦІЯ XX.ІНФОРМАЦІЙНІ ТЕХНОЛОГІЇ ТА СИСТЕМИAI is revolutionizing enterprise security systems by providing real-time threat detection, enhanced efficiency and accuracy, and predictive analytics.The following are examples of AI implementations within enterprise security systems: AI algorithms can detect and prevent unauthorized access to enterprise systems via intrusion detection.AI algorithms can, for instance, analyze user behavior patterns and identify potential security threats such as brute force attacks, phishing attacks, and malware attacks.AI is capable of detecting and preventing fraud in enterprise systems.AI algorithms can examine financial transaction data to find patterns suggestive of fraudulent activity.AI can be used to collect and analyze threat intelligence data from a variety of sources.Algorithms based on artificial intelligence can recognize patterns and trends in cyberattacks and provide insight into potential security concerns.Security operations: Artificial intelligence can be used to automate security operations and enhance incident response times.For instance, AI can generate alerts, investigate security incidents, and respond to security threats automatically.An Isolation Forest model is used for fraud detection in financial data.The Isolation Forest model is trained to identify outliers in the data, which may indicate fraudulent activity.The predictions are then added to the data to aid in further analysis.
Graph above: selected attack patterns in the Artificial Intelligence Авторські права захищені | Creative Commons Attribution-ShareAlike 4.0 International License 313