Artificial intelligence (AI) in its many forms is leading the charge in spurring breakthroughs in digital security in response to the emerging challenges in the post-COVID environment. On the one hand, businesses are finding it challenging to handle security risks related to a range of concerns, including web domain, decision-making, quality control, and system openness, to name a few. However, to comprehend the relationship between AI and those problems, research over the past ten years has concentrated on security capabilities based on instruments like platform complacency, intelligent trees, modeling techniques, and outage management systems. The literature has long acknowledged how important artificial intelligence will be in directing industries and reshaping the transportation, health, and education sectors. This paper presents an Artificial Intelligence-based Security method for Banking Sector (AISBS). AI is one of the best technologies for mapping and preventing unforeseen hazards from consuming an organization. The proposed method can be used to categorize, and resolve cyberattack issues. Algorithms like the Enhanced Encryption Standard (EES) encrypt and decrypt data to guarantee the security of financial sector data. The K-Nearest Neighbor (KNN) algorithm generates predictions by using its training data to make predictions.