Volume 17, Number 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

  Authors

Md Al Amin' St. Francis College MS in Information Technology, United States

  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.

  Keywords

AI, Defensive AI, Zero-Day Threats, Threat Mitigation, Behavioral Analysis, Compromised Accounts, Real-Time Monitoring, Dynamic Analysis.