Dear Asifullah Khan,

Current anti-virus information systems and malware detection applications using generative artificial intelligence and machine learning technologies are becoming increasingly important in the fight against cybercrime. Thanks to their ability to analyze large data sets, these state-of-the-art systems are able to identify threats more effectively and respond to them automatically, significantly increasing their effectiveness. Reports indicate that the introduction of AI into risk analysis reduces the likelihood of successful cyber attacks by 30%, and financial institutions such as JPMorgan Chase are already using these technologies to detect anomalies. It is worth noting that integrating AI with other Industry 4.0 technologies, such as Big Data and cloud computing, strengthens an organization's ability to monitor threats in real time. Statistics confirm that organizations using AI for risk management see a 70% reduction in the number of successful cyber attacks, highlighting the importance of these solutions in modern security management. Dominant theories in the field, such as Geoffrey Hinton's machine learning and Bruce Schneier's adaptive security, are being developed by many institutions, leading to significant improvements in the effectiveness of security systems. Findings from the analysis show that these technologies are crucial to strengthening antivirus systems and the effectiveness of risk management in the face of the growing threat from cybercrime.

Artificial intelligence technology has been rapidly developing and finding new applications in recent years. The main determinants, including potential opportunities and threats to the development of artificial intelligence technology are described in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

Article OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL I...

I conduct research in the problems of analyzing cyber-attacks carried out with ransomware-type viruses and in improving cyber-security systems. I have included the conclusions of my research in the following articles:

Development of malware ransomware as a new dimension of cybercrime taking control of IT enterprise and banking systems

Chapter Development of malware ransomware as a new dimension of cybe...

Determinants of the development of cyber-attacks on IT systems of companies and individual clients in financial institutions

Chapter Determinants of the development of cyber-attacks on IT syste...

The Impact of the COVID-19 Pandemic on the Growing Importance of Cybersecurity of Data Transfer on the Internet

Article The Impact of the COVID-19 Pandemic on the Growing Importanc...

Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of Information with the Use of Sentiment Analysis and Big Data

Article Cybersecurity of Business Intelligence Analytics Based on th...

The role of Big Data and Data Science in the context of information security and cybersecurity

Article The role of Big Data and Data Science in the context of info...

I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in an article of my co-authorship:

APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION

Article APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTEL...

Increase in the Internetization of economic processes, economic, pandemic and climate crisis as well as cybersecurity as key challenges and philosophical paradigms for the development of the 21st century civilization

Article Increase in the Internetization of economic processes, econo...

I invite you to join me for scientific cooperation on this issue,

My warmest greetings,

Dariusz Prokopowicz

Yes, AI can detect and respond to cyber attacks autonomously. This approach, known as autonomous cybersecurity, leverages artificial intelligence to defend networks and systems against cyber threats automatically. By using AI systems, organizations can achieve a more proactive and efficient approach to cybersecurity.

AI-driven cybersecurity systems can analyze vast amounts of data in real-time, identify patterns, and detect anomalies that may indicate a cyber attack. Once a threat is detected, AI can also initiate responses to mitigate the attack, such as isolating affected systems, blocking malicious traffic, or alerting security teams.

However, it's important to note that while AI can significantly enhance cybersecurity, it is not infallible. AI systems need to be continuously updated and trained on the latest threats to remain effective. Additionally, there are ethical considerations and potential risks, such as algorithmic bias and the possibility of AI being used by cybercriminals to launch sophisticated attacks