With the ever-increasing effect of climate change worldwide and many problems like geopolitical issues, population boom, etc., more people are experiencing food insecurity, and many more are at risk. Food security is a very complex topic, but many researchers have tried to arrive at a composite index to measure Food security. One such widely used measure is Global Food Security Index. This research has employed a statistical approach named Data Envelopment Analysis (DEA) to analyze the Global Food Security Index in detail. The indicators used to arrive at the composite score, i.e., GFSI, are only input based, i.e., they consider the existing resources and facilities available which can help a country bolster food security in the country. In contrast to previous approaches, our study takes a different perspective by incorporating output indicators as a means to calculate the composite score for measuring food security in each country. Specifically, we employ an output-based indicator, namely the Prevalence of severe Food Insecurity in the population, expressed as a percentage of the total population. This indicator serves as a measure of the country's effectiveness in achieving food security. By focusing on output indicators, our methodology provides a unique and comprehensive assessment of food security, taking into account the actual outcomes and impacts experienced by the population. We ran a DEA analysis on the GFSI scores for the 35 countries selected for 2017, 2018, and 2019. Different weighting approaches, namely equal weight, expert-based weight, and DEA-assigned weight, were also applied to arrive at the GFSI score. The results were further analyzed and compared to infer meaningful insights.