Security and Governance in AI-Powered Enterprise Systems: A Framework for Sustainable Innovation

Abstract

This article presents a framework for sustainable innovation through effective security and governance in AI-powered enterprise systems. Reviewing the intersection of security measures and governance structures in organizational AI implementations, it identifies the potential for critical gaps in trust, departing from the conventional IT security lifecycle, and provides a way to mitigate the gaps by putting forth a framework. The framework covers AI-specific threats, such as prompt injection, training data poisoning, and model theft, as well as recommending a re-imagined identity and access management controls in relation to AI systems. The article reviewed cross-disciplinary governance committees, documentation processes, and accountability frameworks to enable compliance as well as risk management practices. It also reviews the current state of regulation as it relates to AI operations, with a specific focus on data lineage, consent management, and privacy impact assessments. In the end, it identified potential technical approaches to enable oversight of the allowed use, including monitoring models used for chatbots or large language model APIs, explainability tools, fair assessment capabilities, and version control systems that facilitate a responsible approach to AI and a build in system of checks and balances that justified the means of innovation. This holistic framework will empower organizations to navigate the emerging encumbrance of AI implementation better and address the complexity of immediate security problems as well as longer-term governance issues. The proposed framework is a practical resource for businesses with differing levels of readiness for the integration of AI into their systems, as it provides incremental options that can be tailored to their technical capacities and regulatory obligations over time, creating an ecosystem of innovation and responsibility.