Analyzing the Impact of Regulatory Policies on the Adoption of AI Technologies in Cybersecurity

Geeta Sandeep Nadella, Hari Gonaygunta, Priyanka P Pawar, Deepak Kumar

Cybersecurity, AI technologies, Machine learning models, Cyberattacks, Legal frameworks, Regulatory policies

In the current digital environment, Cybersecurity is still a top priority because of the severe hazards that increasingly complex attacks pose to people, businesses, and even countries. This study provides a comprehensive analysis covering the distribution of cyberattacks, the performance of machine-learning models, and the legal frameworks controlling AI and Cybersecurity adoption. A first analysis indicates a standard distribution of cyberattacks, of which a significant fraction is hostile, underscoring the pressing requirement for effective cybersecurity defense. Further analysis of machine learning models like Multilayer Perceptron (MLP), Gaussian Naïve-Bayes, and Decision Tree-Classifiers demonstrates differing efficacies in identifying and reducing cyber threats. The Decision Tree Classifier displays high accuracy and precision, comparable performance with noteworthy recall rates is displayed by Gaussian Naive Bayes, and MLP demonstrates better training efficiency. The intricacy and significance of complete laws to reduce risks and promote responsible AI deployment are highlighted by regulatory framework analysis. The results highlight the need for cooperative efforts to create flexible, inclusive regulatory frameworks that protect the digital economy and foster innovation safely despite obstacles like regulatory gaps and international cooperation. To improve digital security and ethically utilize AI technology, governments, industry customers, and researchers may benefit significantly from this research's improved understanding of cybersecurity prospects and problems.

https://dx.doi.org/10.31873/IJEAS.11.04.02