Oleksandr Laptiev 1 Doctor of Technical Sciences, Associate Professor the Department of Cyber Security and Information Protection Denys Zymbytskyi 2 Student, Department of Cyber Security and Information Protection 1,2 Faculty of Information Technology, Taras Shevchenko National University of Kyiv, 24 Bogdana Gavrilishina str., Kyiv, 04116,Ukraine, THE ROLE AND IMPACT OF ARTIFICIAL INTELLIGENCE IN ENSURING INFORMATION SECURITY Abstract Artificial intelligence technologies, including machine learning and deep neural networks, are changing approaches to cybersecurity. They are beginning to reshape the principles of information protection. This paper explores the growing role of AI in modern information security. Currently, monitoring and intrusion detection in information systems are labor-intensive processes that consume a significant amount of time, diverting the attention of cybersecurity professionals from routine tasks. Artificial intelligence can enhance the work of cybersecurity experts in various ways, from real-time threat detection to incident response and recovery. Artificial intelligence has the potential to significantly ease their workload, allowing them to focus more on timely responses to cyber incidents. Keywords : cybersecurity, AI, threat detection, risk prediction Introduction In an era defined by ubiquitous digitalization of information and the ever- increasing interconnectedness of our world, data security has become an urgent and serious challenge. Cyber threats continue to evolve, and the stakes are higher than ever. From personal information to critical infrastructure, the spectrum of cyberattacks looms wide, necessitating innovative and proactive approaches to safeguarding our digital assets [1-2]. Currently, artificial intelligence is an integral part of the cybersecurity domain, providing more reliable and efficient threat detection and analysis. Examples of successful AI usage in cybersecurity: 1. Real-time threat detection using machine learning: Many large organizations and cybersecurity providers employ machine learning for continuous network monitoring. This enables them to detect attacks and anomalies in real-time and respond instantly. 2. Machine learning in antivirus software: Modern antivirus solutions actively utilize machine learning to identify new types of malware. They analyze the behavior of programs and files, detecting potential threats even if they were previously unknown. 3. Automated vulnerability and attack detection: Artificial intelligence helps identify vulnerabilities in network software and operating systems, as well as detect attacks and intrusions, enhancing information security. 4. Phishing and fraud prevention: Many email filtering systems and financial institutions use AI to detect phishing attacks and fraudulent activities by analyzing textual data and user behavior. In the dynamic landscape of information security, the increasing complexity and frequency of cyber threats pose a significant challenge for individuals, organizations, and governments worldwide. As the volume of digital data continues to grow, and malicious actors employ increasingly sophisticated methods, traditional security measures alone are insufficient to protect confidential information. The application of AI in ensuring information security comes with challenges and limitations, some of which are as follows: 1. Effective model training requires access to large datasets, which can be problematic due to data confidentiality and privacy concerns. 2. Adversaries can also leverage AI for sophisticated cyberattacks, emphasizing the importance of developing security systems capable of resisting such attacks. 3. Automated systems may have false positives, leading to unnecessary alerts and potentially overburdening security personnel. 4. Threats are constantly evolving, necessitating regular updates to AI models to maintain their effectiveness. 5. Implementing and maintaining AI-based security systems require highly skilled specialists, and a shortage of such experts can hinder the adoption of these solutions. Existing security protocols often fail to detect and mitigate risks in real-time, leading to data breaches, financial losses, and damage to reputation. Furthermore, the reactive nature of traditional cybersecurity practices cannot keep up with adversaries who constantly adapt their tactics. Thus, there exists a contradiction between existing methods and approaches to combat cyber threats and the need to develop innovative and effective methodologies for addressing these threats. Presenting main material Cyber AI programs offer significant advantages for government and business leaders responsible for protecting individuals, systems, organizations, and communities from modern and relentless cybercriminals. AI serves as a force multiplier for experienced cybersecurity professionals throughout the entire cybersecurity lifecycle, including monitoring vast datasets to detect nuances in adversary tactics, quantifying risks associated with known vulnerabilities, and data-driven decision-making during threat hunting. The immediate and long-term benefits of integrating AI into an organization's cybersecurity ecosystem include: 1. Enhanced Protection and Recovery: AI's ability to detect imperceptible attacks improves security and incident response. 2. Time Savings: Artificial intelligence reduces the detection and response time cycle, rapidly assesses risks, and expedites decision-making by analysts through data- driven mitigation measures. 3. Reputation and Trust: AI strengthens the brand's reputation and trust in the organization's security systems and protocols. 4. Employee Satisfaction: Cybersecurity experts can focus on higher-level tasks rather than labor-intensive manual actions. 5. Compliance with Elevated Security Requirements: For government entities, especially defense and national security agencies, requiring the highest level of cybersecurity, AI extends information protection capabilities. 6. Competitive Advantage: AI-based automation provides a competitive edge. As AI becomes more prevalent, it becomes easier to augment human capabilities in government and defense cybersecurity, increasing impact and effectiveness. 7. Minimization of Human Errors: Integrating AI capabilities into manual and semi-manual processes can minimize errors and discrepancies. New skill sets will be in demand among cybersecurity professionals. Organizations will seek experts with experience in applying AI and machine learning technologies to cybersecurity rather than merely traditional cybersecurity skill sets. Prospects for the Development of AI-Based Security Systems The prospects for the development of AI in cybersecurity remain highly promising. Key directions to anticipate include: 1. Enhanced Threat Detection and Response: As AI continues to advance, more precise and faster threat detection systems will be created. Automated systems will be able to make more complex decisions and offer precise response strategies to cyberattacks. 2. Attack Prediction: AI systems will not only detect attacks in real-time but also predict the likelihood of future attacks. This allows organizations to take proactive measures. 3. Automated Vulnerability Remediation: AI will be actively used to detect and remediate vulnerabilities in real-time, reducing the probability of successful attacks. 4. Improved Collaboration Between AI and Humans: The development of hybrid systems that combine AI capabilities with human expertise will continue to enhance cybersecurity management. 5. Advancements in Quantum Cryptography: AI can be applied to analyze and develop quantum cryptography methods, ensuring high levels of security against quantum computer threats. These directions will make information security more effective and adaptable to the evolving landscape of cyber threats. However, it is crucial to consider ethical considerations, transparency, and the security of AI systems to avoid new vulnerabilities and risks in the field of cybersecurity. Conclusions As a result of the conducted research on the role and impact of artificial intelligence in ensuring information security, an attempt has been made to formulate innovative and effective approaches to combat cyber threats based on artificial intelligence. As more data and information become accessible through networks, the level of cyber threats and potential attacks increases accordingly. It is crucial to continue improving threat detection algorithms, enhancing attack prediction methods, and developing new data protection techniques. Collaborative efforts in the application of neural networks will enhance our understanding and adaptation to the evolution of cyber threats, leading to the creation of more reliable information security systems. The importance of further research and development in the field of information security, particularly with the use of artificial intelligence, cannot be underestimated in the modern digital era where the digital environment plays a key role in our daily lives and business processes. References 1. Brynjolfsson Erik. What Can Machine Learning Do? Workforce Implications / Erik Brynjolfsson, Tom Mitchell // Science. – 2017. – No 358(6370). – Р. 1530 – 1534. 2. Shaytura S.V., Minitaeva A.M., Feoktistova V.M., Ordov K.V. Blockchains in spatial data security – In: CEUR Workshop Proceedings. Selected Papers of the X Anniversary International Scientific and Technical Conference on Secure Information Technologies (BIT 2019). – 2019. – p. 70-74. 3. Serhii Yevseiev, Volodymir Ponomarenko, Oleksandr Laptiev, Oleksandr Milov and others. Synergy of building cybersecurity systems. Kharkiv. Publisher PC TECHNOLOGY CENTER. 2021 – 188 с. ISBN 978 -617- 7319 -31- 2 (on - line). ISBN 978 -617- 7319 -32- 9 (print). DOI: https://doi.org/10.15587/978 -617- 7319 - 31-2