Finding hidden vulnerabilities in software is like searching for a needle in a haystack. Traditional methods struggle, but what if AI could lend a hand? Researchers are exploring how the power of multiple AI models working together, known as ensemble learning, can revolutionize code security. Imagine a team of specialized code detectives, each with unique skills. One focuses on the meaning of the code (semantics), another on its structure, and a third on how different parts interact. Individually, they're good, but together, they're a force to be reckoned with. This is the idea behind EnStack, a new framework that combines the strengths of leading AI models like CodeBERT, GraphCodeBERT, and UniXcoder. Each model analyzes the code from its unique perspective, and their insights are then combined using a 'meta-classifier' – a judge that weighs the evidence and makes the final verdict on whether a vulnerability exists. Experiments show that EnStack outperforms individual models, catching subtle vulnerabilities that others miss. This suggests that ensemble learning may be the key to more secure software in the future. However, challenges remain. These AI models are computationally intensive, and the research relied on a specific dataset, raising questions about its applicability to other types of code. Future research will focus on broadening the dataset and exploring even more powerful generative AI models like LLaMA and Mistral to further enhance code security.