Mitigación de fugas de datos e inyección de prompts en chatbots universitarios mediante arquitectura perimetral desacoplada

Higher education is rapidly adopting Generative Artificial Intelligence tools, a situation that has exposed universities to critical cybersecurity vulnerabilities, mainly prompt injection and data leakage. This study designs and evaluates an architecture for a decentralized conversational agent that functions as a virtual course assistant, taking into account the cybersecurity recommendations of the faculty. The methodological approach is theoretical-experimental, and the research is conducted in three phases: an assessment of faculty members’ cybersecurity knowledge via a digital questionnaire; the design of perimeter middleware that mitigates data leakage and is effective against prompt injection; and the experimental evaluation of the design. The experimental results demonstrate that the proposed architectural design significantly reduces data exposure and vulnerability to prompt injection attacks. The research provides a practical and replicable solution that strengthens privacy, information security, and the responsible adoption of AI in the university setting.