Segmentation of the pancreas in CT images is crucial in multiple pancreatic diagnostic tasks, such as the detection, classification, and prognosis of pancreatic cancer. We present a segmentation model to find pancreatic tissue accurately in abdominal CT images. We utilize the Segment-Anything Model (SAM), a prompt-based 2D segmentation transformer model, and adapt it to 3D CT images to build a model that can segment the pancreas automatically without any prompts. To our knowledge, this is the first prompt-free work to segment the pancreas on a CT image based on the generalizable SAM model. We achieve a DICE score of 87.01% and a Jaccard score of 81.42% on the NIH dataset. We also performed zero-shot segmentation on the Abdominal-1K dataset. We achieved a DICE score of 83.20%, which shows the generalizability and applicability of our method to new unseen samples. Our study put together the zero-shot performance of SAM and the 3D nature of CT images to provide an automatic, real-time model that provides consistent segmentation throughout CT slices without the need for expert intervention.