Fixploring the Benefits and. Drawbacks of Machine Learning in Cybersecurity to Strengthen Cybersecurity Defences

Although the initial motivations for carrying out cyberattacks stay the same, cybercriminals demonstrated a heightened level of sophistication in their methodologies due to the growing number of people, and deluge of data, devices, and programs in the contemporary company. The efficacy of conventional cybersecurity measures in identifying and addressing emerging cyber threats is diminishing. In light of the escalating cyber-threat landscape, there is a pressing need to deploy sophisticated tools and technologies that can effectively identify, examine, and promptly respond to emerging attacks and threats. Artificial intelligence (AI) has become a ground¬breaking technology with enormous promise across various industries. One area where AI has made a considerable influence is cybersecurity. AI, notably Machine learning (ML), enhances cybersecurity against more complex attacks and addresses significant concerns such as real-time attack detection, data leakage prevention, malware protection, vulnerability assessments, and many more. There is a growing interest in ML-based solutions that automate the identification of attacks and address sophisticated cybersecurity challenges. This article examines the advantages of the most promising ML applications now in use while highlighting the drawbacks and factors businesses should carefully consider when using ML- powered tools in cybersecurity and ensuring they are used alongside other security procedures.