Human Vulnerabilities in Cybersecurity: Analyzing Social Engineering Attacks and AI-Driven Machine Learning Countermeasures

With an emphasis on how social engineering attacks take advantage of human behaviour to get past conventional barriers, this paper examines human weaknesses in cybersecurity. It looks at how attackers use cognitive biases, emotions, and trust to facilitate network hacks, data breaches, and other types of cybercrime. The study highlights how common and sophisticated social engineering tactics are becoming in the connected digital world of today. The study analyses the financial, reputational, and security ramifications of social engineering attacks by thoroughly reviewing the body of existing research and analyzing in-person case studies. The study's focus on AI-driven machine learning algorithms as a necessary countermeasure is one of its noteworthy features. The effectiveness of social engineering attacks is greatly decreased by these algorithms, which are tested for their capacity to improve encryption protocols, spot phishing efforts in real-time, and discover abnormalities in user behaviour. The study emphasizes that although AI-powered solutions offer a technological advantage, human factors remain the most often exploited cybersecurity vulnerability. By addressing technological and human vulnerabilities, the findings add to the continuing conversation about enhancing cybersecurity and offer practical advice for increased resistance to social engineering attacks. Keywords: Mechanism, Social Engineering, Cybersecurity, Vulnerability, Cyber intrusion, Machine Learning