International Journal of Sustainability and Innovation in Engineering (IJSIE)
2025
Authors
Prassanna Rao Rajgopal
Shilpi Yadav
Abstract
Artificial Intelligence (AI) has rapidly evolved into a cornerstone of digital transformation, revolutionizing decision-making, operational efficiency, and innovation across industries. Yet, as enterprises accelerate adoption, risks related to data privacy, integrity, and security are escalating. AI systems rely on vast volumes of sensitive data often personal, regulated, or business-critical that must be managed responsibly to prevent breaches, misuse, and ethical violations. At the same time, regulatory frameworks such as GDPR, HIPAA, and CCPA impose strict requirements around lawful processing, data minimization, and accountability. This dual challenge underscores the urgent need for robust data governance as an enabler of secure AI adoption. Data governance establishes the policies, processes, and standards for managing data across its lifecycle. When applied to AI ecosystems, it ensures the quality, provenance, and lawful use of training data, while embedding security and compliance at every stage of the model lifecycle. Unlike purely technical cybersecurity controls, governance provides a socio-technical framework that aligns people, processes, and technology to build trust in AI outcomes. It enables organizations to mitigate risks such as adversarial data poisoning, model bias, or unauthorized access to sensitive datasets. This paper examines how data governance frameworks integrate with cybersecurity to secure AI adoption. We review existing literature, highlight governance gaps, and propose a Secure AI Governance Model (SAIGM) consisting of four pillars: data integrity, privacy and compliance, access and control, and continuous oversight. Case studies demonstrate how effective governance translates into trusted AI outcomes, regulatory compliance, and business resilience.
Keywords:
Data governance, Secure AI adoption, AI risk management, Privacy and compliance (GDPR/HIPAA/CCPA), AI ethics and fairness, Data integrity and lineage, Access control and continuous oversight
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