An integrated governance perspective that connects technical AI risks with organisational governance and practical implementation considerations is provided, and an Integrated AI Governance Framework that brings together technical assurance, organisational governance, and internationally recognised governance principles within a unified lifecycle model is proposed.
Abstract
Artificial Intelligence (AI) is increasingly embedded within supply chain and information systems, where decision-making depends on the quality, integrity and reliability of operational data. While previous studies have examined data poisoning, data drift and data integrity independently, limited research has integrated these governance risks within a single AI governance perspective. This review addresses that gap by synthesising current literature and developing a case-informed governance framework for managing interconnected AI risks in operational environments. The study adopts a structured narrative review supported by published literature and an illustrative supply chain case scenario to examine how data poisoning, data drift and data integrity failures affect AI performance, organisational resilience and governance. Rather than reporting experimental findings, the paper synthesises existing evidence and demonstrates how these risks interact throughout the AI lifecycle. The review demonstrates that effective AI governance extends beyond technical safeguards alone. Maintaining reliable and resilient AI systems requires continuous lifecycle assurance, robust data governance, organisational accountability, and appropriate regulatory oversight. Drawing on these findings, the study proposes an Integrated AI Governance Framework that brings together technical assurance, organisational governance, and internationally recognised governance principles within a unified lifecycle model. This study contributes to AI governance literature by providing an integrated governance perspective that connects technical AI risks with organisational governance and practical implementation considerations. The framework offers guidance for organisations deploying AI within complex data-driven environments.
Artificial intelligence (AI) is increasingly embedded in organisational systems, shaping decision making, resource management, and crisis response. Achieving AI-resilient continuity is not just about faster recovery or stronger systems; it involves maintaining decision integrity, governance quality, and human judgment during intelligent disruptions. While AI enhances efficiency and resilience, it also introduces risks that traditional continuity planning does not fully address. Unlike conventional disruptions, AI-enabled attacks may not cause immediate system failures, yet they can degrade decision accuracy, situational awareness, and governance even while operations appear normal. This paper examines how AI alters the nature of disruption and challenges assumptions about visibility, human oversight, and linear recovery. Through practical scenarios, it illustrates how data poisoning, adversarial inputs, compromised models, platform dependencies, and misinformation threaten organisational continuity. The paper proposes principles for AI-resilient planning that protect decision integrity, enable human override, strengthen governance alignment, and incorporate AI-specific exercises. This framework provides practitioners with actionable guidance for sustaining reliable decision making under AI-driven disruption. This article is also included in The Business & Management Collection which can be accessed at https:// hstalks.com/business/.
S. Haynes· Journal of Business Continui...· 0 citations
Artificial intelligence is increasingly embedded in critical digital infrastructure across emerging markets, supporting applications in telecommunications, financial inclusion, healthcare, agriculture, fraud detection and public service delivery. While global responsible AI frameworks have established widely accepted principles for fairness, transparency, accountability, and human oversight, translating these principles into operational safeguards remains uneven, particularly in contexts characterized by institutional fragmentation, evolving regulatory regimes, vendor dependence, and constrained governance capacity. This paper examines structural implementation gaps that arise when globally flexible responsible AI frameworks are applied without contextual integration. It advances a governance by design model that operationalizes trust through measurable proportional risk tiering, lifecycle-based oversight, and structured ecosystem accountability. Governance controls are embedded at decision points spanning design, procurement, deployment, and continuous monitoring, reducing governance debt and strengthening institutional resilience. An applied case illustration demonstrates how proportional governance can be integrated into high impact deployments while preserving scalability. Aligned with emerging international standards, including ISO/IEC 42001, the models offer a scalable, context-aware pathway for implementing trustworthy, inclusive and sustainable AI under real world constraints.
Derick Ohmar, Adil· 2026 ITU Kaleidoscope - AI a...· 0 citations
It is concluded that a holistic AI compliance framework integrating data governance, bias mitigation strategies, legal oversight, cybersecurity, and ethical accountability is essential for ensuring responsible and trustworthy AI deployment in high-stakes environments.
Joy Oluchi Nwachukwu, Thaddaeuse Odhiambo, Dorcas Akorkor Apaflo et al.· Journal of Economic, Finance...· 0 citations
This paper reviews emerging AI-GRC frameworks and regulations, including the OECD AI Principles, ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act, alongside industry standards from Microsoft, Google, and IBM, to reveal a fragmented ecosystem with overlapping principles but inconsistent enforcement and technical depth.
Abimbola Filani, J. Opoku· Magna Scientia Advanced Rese...· 0 citations
This paper argues for a transition from AI Governance as Compliance to AI Governance Engineering , a systems-oriented discipline in which governance is embedded throughout the enterprise intelligence lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.
Faruk Çelikkanat· International Journal of Res...· 0 citations
Existing data- and information-governance frameworks classify, retain and protect organisational data, but they do not govern the externally controlled artificial intelligence (AI) systems through which organisations increasingly access, interpret and reason about that data. This article names that omission epistemic dependency risk: the exposure an organisation carries when its capacity to access, interpret and reason about its own knowledge assets is contingent on AI systems it neither owns nor controls and whose continued availability it cannot guarantee. Distinguishing this risk from the operational and data lock-in already described in the cloud-computing literature, and grounding it in scholarship on epistemic dependence, the article presents a typology of disruption events from 2022 to 2026 — spanning regulatory withdrawal, commercial discontinuation and infrastructure instability — and argues that the risk is new in degree rather than in kind. It proposes a sequential professional response — mapping, classifying and planning — that extends established information-audit, custody and continuity practice rather than departing from it.
Adebowale Jeremy Adetayo· Business Information Review· 0 citations