Christopher Ugbong Akeke

Critical infrastructure operators increasingly deploy artificial intelligence (AI) to detect degradation, predict failures, optimise maintenance, identify cyber anomalies and support operational decisions. These capabilities create a plausible route to higher availability, reliability and quality, yet they also couple asset-performance decisions to data integrity, model uncertainty, software supply chains, human oversight and operational technology security. This critical narrative review examines how secure AI can contribute to asset performance without weakening safety or resilience. Literature spanning predictive maintenance and prognostics, industrial AI, digital twins, industrial cyber-physical security, adversarial machine learning, explainability, uncertainty, machine-learning operations and human factors was critically synthesised. The evidence is strongest for AI as a decision-support layer for condition monitoring, fault diagnosis and targeted maintenance where data provenance is controlled, failure modes are sufficiently represented and recommendations remain bounded by engineering constraints. Evidence for fully autonomous optimisation in high-consequence infrastructure is less mature because benchmark accuracy does not directly establish operational utility, transferability or safe behaviour under distribution shift and hostile manipulation. Digital twins can improve contextual diagnosis and testing, but their value depends on fidelity, synchronisation, governance and protection of the data-model-actuation pathway. Cybersecurity and safety therefore cannot be treated as independent assurance domains: poisoned training data, adversarial inputs, compromised updates or unavailable models can become physical reliability and quality risks. Explainability alone is also insufficient for assurance; uncertainty calibration, abstention, independent validation, auditability and meaningful human authority are required. The synthesis proposes a secure asset-performance loop in which AI recommendations are evaluated against safety, security, service and quality constraints, with outcomes fed back into monitored model and process improvement. The central implication is that asset performance should be maximised as a constrained socio-technical objective rather than as an unconstrained prediction or utilisation metric.

Information privacy and data security governance increasingly depend on sustained human attention: employees must interpret warnings, follow changing policies, report anomalies, manage credentials, make disclosure decisions and repeatedly consent to data practices. Yet governance arrangements commonly treat attention, motivation and self-control as effectively unlimited. This critical narrative review examines how security fatigue, privacy fatigue, habituation, burnout, cynicism, organisational silence and institutional decoupling interact to weaken protective behaviour and governance legitimacy. Literature published from 1 January 2000 to 20 May 2026 was identified through accessible scholarly indexes, bibliographic databases, disciplinary digital libraries, DOI metadata services, institutional repositories and citation chaining, with foundational earlier works retained where conceptually necessary. Evidence was appraised for design quality, behavioural measurement, temporal ordering, ecological validity, theoretical coherence and relevance to organisational governance. The synthesis indicates that fatigue is not adequately explained as individual carelessness. Repeated low-value warnings, work-impeding controls, opaque privacy choices, excessive policy demands and punitive reporting climates create cumulative cognitive and emotional costs. These costs can produce attentional habituation, rational workarounds, reduced self-efficacy, resignation and silence. At institutional level, audit-oriented programmes may become decoupled from operational risk reduction, allowing training completion, policy acknowledgement and nominal consent to substitute for observed protective outcomes. Evidence is strongest for warning habituation, compliance-cost reasoning, privacy concern–behaviour discrepancies and associations between exhaustion and silence. Confidence is lower regarding long-term causal pathways and the effectiveness of organisation-wide interventions because much of the literature is cross-sectional, self-reported and based on behavioural intention. An integrated burden–efficacy–legitimacy cycle is proposed to explain how security demands, perceived control, organisational credibility and voice climate jointly shape behaviour. Sustainable governance should reduce unnecessary security work, prioritise high-consequence actions, automate where safe, design adaptive warnings, preserve meaningful choice, support non-punitive reporting and evaluate real behaviour and risk outcomes rather than ceremonial indicators.

Artificial intelligence systems increasingly mediate consequential decisions in credit allocation, healthcare triage, employment screening and public administration, yet the data underpinning these decisions is frequently incomplete, mislabelled, stale or quietly altered as it moves through long and opaque pipelines. This review examines governance frameworks intended to preserve the integrity of algorithmic decision data and to align organisational practice with an increasingly dense regulatory landscape spanning the European Union, the United States and international standard-setting bodies. It synthesises literature on data quality theory, documentation artefacts such as datasheets and model cards, blockchain-based provenance mechanisms, algorithmic auditing regimes and sector-specific compliance obligations in finance and healthcare. The review finds that technical solutions for data quality monitoring have matured considerably faster than the institutional arrangements needed to make such monitoring auditable, contestable and legally enforceable, producing a persistent gap between what is technically feasible and what is organisationally practised. It further finds that regulatory instruments, notably the General Data Protection Regulation and the Artificial Intelligence Act, converge on transparency and documentation obligations but diverge on enforcement mechanics, creating compliance friction for organisations operating across jurisdictions. The review proposes a layered governance model integrating data-level controls, documentation practices, human oversight and independent auditing, and identifies future research priorities around interoperable provenance standards, cross-border regulatory harmonisation and the measurement of data integrity as a continuous rather than a point-in-time property.

The accelerating adoption of artificial intelligence across United States healthcare systems has intensified concerns over cyber resilience, data privacy, and regulatory compliance, yet existing governance frameworks remain fragmented and difficult to compare. This study addressed that gap by developing a governance-centred multi-criteria evaluation model (a structured governance-scoring instrument rather than a machine-learning system) that evaluates and ranks established frameworks on a single quantitative scale. Twelve governance constructs were derived from framework analysis and a control coverage mapping against adversary tactics, then weighted by combining the analytic hierarchy process with the entropy weight method to balance expert judgement and data-driven objectivity. The integrated weights produced composite readiness indices, dimensional resilience, privacy, and compliance scores, and a five-level maturity classification. Robustness was confirmed through bootstrap resampling, sensitivity perturbation, and consistency testing, returning a consistency ratio of 0.058 and rank stability of 0.958, while principal component analysis retained 86.3 percent of variance (interpreted descriptively given the small number of frameworks, n = 6). All six frameworks exceeded the readiness threshold, achieving a mean readiness of 0.782, with ISO/IEC 27001 ranked highest at 0.869 and the only Adaptive classification. The analysis isolated transparency and human oversight as the weakest constructs. The resulting instrument offers healthcare decision makers a reproducible, transparent, and internally consistent basis for strengthening accountability and prioritising critical governance improvements.

This study examines the ethical and secure deployment of generative artificial intelligence in enterprise environments, with emphasis on innovation, data privacy, and risk governance. It addresses the gap between the rapid organisational adoption of generative AI systems and the slower development of institutional mechanisms for managing ethical, privacy, security, and regulatory risks. The study identifies the absence of a validated, integrated governance instrument that combines ethical, privacy, security, and enterprise risk controls for the specific characteristics of generative systems. A desk-based mixed-methods approach was used, combining systematic literature review, document analysis, thematic synthesis, comparative evaluation of governance frameworks, and Analytic Hierarchy Process weighting. Evidence was drawn from peer-reviewed literature and authoritative international governance instruments. Eight governance dimensions were assessed: scope and coverage, risk classification, data privacy, ethics and accountability, security controls, legal enforceability, enterprise applicability, and adaptability to generative AI. The findings show that existing governance instruments provide useful but fragmented coverage when applied independently. Ethics and accountability emerged as the highest-weighted dimension, followed by data privacy, security controls, and enterprise applicability. The proposed framework integrates five pillars: ethics and accountability, privacy and data governance, security governance, enterprise risk management, and innovation and compliance alignment. The study concludes that responsible enterprise deployment of generative AI requires coordinated, multi-layered governance rather than reliance on isolated ethical, technical, or legal controls.

School psychologist shortages remain a critical challenge in high-need districts, where ratios often exceed 1:1,200, limiting timely mental health support for students. This desk-based study developed hybrid AI and telepsychology workforce models to address these shortages. A systematic literature review synthesized best practices for telepsychology delivery and artificial intelligence tools in school mental health, identifying effective triage, consultation, and service expansion strategies. Agent-based modeling and Monte Carlo simulations, parameterised from NASP and NCES data, projected a 52.6% improvement in caseload ratios, reducing the average from 1:1,211 to 1:574 while enhancing equity and maintaining strong accuracy (AUC = 0.96) and cost-effectiveness. The models integrate AI triage with telepsychology to augment limited personnel and improve access in rural districts. Findings support scalable, technology-driven frameworks aligned with the NASP Practice Model. Policy recommendations emphasise licensure portability, infrastructure investment, and phased implementation for sustainable adoption. This interdisciplinary approach offers practical, replicable solutions for mitigating workforce shortages and advancing equitable mental health services in under-resourced schools.

Autonomous AI agents powered by large language models increasingly operate with unrestricted communication and publishing capabilities, creating critical security vulnerabilities through memory poisoning attacks that corrupt long-term agent memory and propagate falsified outputs to public channels, eroding institutional trust and compromising data integrity. This study investigated how dynamic authorization and immutable audit trails can mitigate these risks, focusing on public trust and data integrity in agentic AI security and governance. A quantitative, simulation-based research design was adopted, utilizing Agent Security Bench scenarios across 1,000 simulation runs and 13 large language model backbones within a controlled cybersecurity environment. Results revealed that a 19.6% environmental poison rate amplified to a 46.5% agent-level compromise, generating a 15.7x trust degradation multiplier. Dynamic authorization achieved a 96.8% denial rate, reducing attack success rates by 85%, while Merkle hash chain immutable audit trails recovered 91.9% of poisoning incidents with complete hash integrity. The combined architecture produced a Balanced Security-Utility Index (BSUI) of 0.8673, confirming operational viability. The study recommends integrating context-aware authorization with blockchain-anchored audit logging as minimum governance standards for autonomous AI deployments in high-stakes publishing environments.

This study examines how school psychologist workforce capacity influences the timeliness of Autism Spectrum Disorder (ASD) diagnosis and access to intervention services within Title I schools in the United States. Using secondary data obtained from the National Center for Education Statistics (NCES), including the Common Core of Data (CCD) and EDFacts databases, the study adopts a quantitative design to investigate the direct, indirect, and contextual effects of workforce capacity on ASD service delivery outcomes. Workforce capacity was operationalized using the student-to-psychologist ratio, while early identification, intervention access, and assessment capacity were measured using standardized proportional indicators derived from school-level reporting data. The analysis integrated multivariate linear regression, binary logistic regression, mediation analysis, and hierarchical linear modeling to evaluate the relationships among workforce capacity, assessment processes, and intervention access across Title I school contexts. The findings indicate that higher psychologist-to-student ratios significantly reduce early ASD identification rates (β = -0.000185, p < 0.001) and decrease the likelihood of access to intervention services (β = -0.00112, p < 0.001). Mediation analysis further demonstrates that assessment capacity partially mediates the relationship between workforce capacity and service access, while multilevel modeling reveals that the negative effects of workforce shortages are significantly amplified in high Title I contexts (\gamma = -0.000057, p = 0.002). The study concludes that workforce shortages and structural socioeconomic disadvantage jointly constrain ASD diagnostic and intervention pathways within underserved schools. The findings highlight the importance of strengthening workforce distribution, improving assessment infrastructure, enhancing inter-agency coordination, and expanding alternative service delivery models to improve equitable access to ASD-related services.

This study presents a hybrid federated learning and explainable artificial intelligence framework, termed RobustFL, designed to mitigate adversarial attacks in medical imaging while incorporating privacy-preserving mechanisms. Focusing on chest X-ray analysis, the research systematically examined adversarial vulnerabilities and data privacy challenges in distributed healthcare systems. A simulation-based methodology was implemented using a 500-sample dataset partitioned across multiple clients in a non-independent and identically distributed setting. The framework extends federated averaging by integrating projected gradient descent-based adversarial training, differential privacy stochastic gradient descent, and SHAP-based interpretability. Experimental results demonstrate that RobustFL maintains stable performance under adversarial conditions, achieving 70% accuracy against FGSM and PGD attacks, with an adversarial success rate of approximately 30–31%. A balanced privacy budget of ε = 1.0 produced an F1-score of 0.412, indicating a trade-off between privacy and model utility. Interpretability consistency, measured via SHAP outputs, served as a proxy for trust assessment, while privacy risk indicators were used to infer potential user concern. The framework provides a practical pathway toward secure, transparent, and privacy-aware healthcare AI systems.