Concerns in information and data security are addressed based on implementing practices to enhance data security mechanisms and feedbacks. Recent medical data breaches across the globe and rising cybersecurity threats from cyber warfare make research into data privacy more crucial in the system’s overall design. The advent of telemedicine and research in the applications of artificial intelligence in the medical domain makes a case for integrating enhanced image analysis inside a secure framework. The proposed modality implements critical principles of “authentication, authorization data integrity, data confidentiality, and the Principle of Least Privilege.” The use case chosen for this paper is on a retinal image DRIVE dataset. Retinal images have several applications in the field of medicine, which include the early detection of various diseases such as glaucoma. Characteristics intrinsic to retinal images make the vessel detection process difficult. Our proposed framework EyeEncrypt, aids in effective medical image segmentation on a secure integrated framework.