Secure AI adoption framework for a multi-tenant SaaS Product Companies - Chachar, Santosh (Journal of Advanced Research in Electronics Engineering and Technology; Vol 12 No 1&2 (2025): Journal of Advanced Research in Electronics Engineering and Technology, 2025)
Software as a service (SaaS) has become popular in the last decade due to its cost-efficiency, scalability, and convenience. It eliminates the need for businesses to invest in on-premises infrastructure by offering cloud-hosted, subscription-based solutions. Artificial Intelligence (AI)’s influence on Software as a Service (SaaS) product development has grown rapidly in the last five years. SaaS solutions leverage artificial intelligence to furnish personalised services, augment functionality, and enhance performance, promising an en riched user experience grounded in intelligent data-driven decisions. However, the multi-tenant architecture of SaaS platforms introduces unique challenges, particularly in ensuring security, tenant data privacy, and compliance. This paper introduces a Secure AI Adoption Framework designed to guide multi-tenant SaaS product organisations through the systematic, secure, and ethical implementation of AI capabilities. The proposed framework addresses critical aspects of AI adoption, including readiness assessment, AI solution design, secure deployment, and continuous, monitoring, while prioritising tenant data protection and regulatory compliance. By analysing current practices and identi fying gaps, this review develops a structured roadmap that balances technical feasibility with organisational goals. Through an extensive literature review, case studies, and expert analysis, this study provides a practical and scalable framework to empower SaaS organisations in leveraging AI responsibly and securely.