Taxonomy of AISecOps Threat Modeling for Cloud Based Medical Chatbots

• Published in: arXiv.org 2023-05
• Main Authors: Ruby, Annette J, Banu, Aisha, Sharon, Priya S, Chandran, Subash
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Chatbots; Conversational artificial intelligence; Cybersecurity; Domains; Taxonomy; Threat evaluation; Threat models
• ISSN: 2331-8422

Artificial Intelligence (AI) is playing a vital role in all aspects of technology including cyber security. Application of Conversational AI like the chatbots are also becoming very popular in the medical field to provide timely and immediate medical assistance to patients in need. As medical chatbots deal with a lot of sensitive information, the security of these chatbots is crucial. To secure the confidentiality, integrity, and availability of cloud-hosted assets like these, medical chatbots can be monitored using AISecOps (Artificial Intelligence for Secure IT Operations). AISecOPs is an emerging field that integrates three different but interrelated domains like the IT operation, AI, and security as one domain, where the expertise from all these three domains are used cohesively to secure the cyber assets. It considers cloud operations and security in a holistic framework to collect the metrics required to assess the security threats and train the AI models to take immediate actions. This work is focused on applying the STRIDE threat modeling framework to model the possible threats involved in each component of the chatbot to enable the automatic threat detection using the AISecOps techniques. This threat modeling framework is tailored to the medical chatbots that involves sensitive data sharing but could also be applied for chatbots used in other sectors like the financial services, public sector, and government sectors that are concerned with security and compliance.