GRAILS - A Framework for Embedding Ethical Safeguards in Software Applications for Responsible AI

Software systems increasingly mediate critical societal functions involving large-scale use of data. The use of personal and sensitive data introduces ethical and legal concerns, necessitating architectures that support Responsible AI and enforce open access safeguards grounded in ethical principles. These principles are conventionally derived through laws, regulations, codes of conduct, and frameworks. Much of the focus in responsible AI has been on privacy alone. However, there can be other sensitive information in the enterprise and public domain that needs to be handled in a responsible and ethical manner as well. Ethical compliance traditionally has been achieved through systematic manual adherence to guidelines, regulations, and other documentation. However, there is a lack of concrete software architectures, frameworks, or tools that can provide automation to enable compliance directly in software systems. This paper introduces Guardrail Framework, a modular, reusable software framework that operationalizes responsible AI principles by decoupling ethical constraints from the functional requirements of software applications. The proposed novel framework addresses the issue of ethical compliance in application software by creating a robust software framework that can be plugged into multiple application software development environments. The framework allows ethical filtering of information on the basis of data sensitivity (Low to High), the trust score of the user (Low to High), and granularity of the data (Cell, Row, Column, or Table). The proposed Guardrail framework is implemented and evaluated using Open Government Dataset (OGD), demonstrating high cohesion, low coupling, and long-term maintainability, in line with fundamental software engineering architectural design principles. Notably, the proposed framework is equally effective for ensuring ethical use of personal, enterprise, and public data.