Pelumi Damola Adeyinka

Enterprise adoption of artificial intelligence (AI) creates sustainability, social and security risks that firms typically govern through separate ESG and cybersecurity structures. This study asks whether, and under what conditions, large U.S. firms disclose an integrated approach to governing AI across the three domains. It uses a configurational, text-as-data design: the latest Form 10-K of each S&P 500 registrant (N = 500; analysis sample, n = 488) was converted into dictionary-based measures of AI, AI-governance, environmental, social and cybersecurity disclosure, sentence-level co-mention of AI with each risk domain, and an eleven-element score of the Form 10-K Item 1C cybersecurity-governance disclosure. Latent Dirichlet allocation of 9,444 AI-context sentences recovered six themes. Latent profile analysis identified four overlapping disclosure profiles (sustainability-led and AI-light, social-led with weak cyber governance, AI-intensive integrators, and security-anchored moderates) that differ by sector and by year-on-year growth in AI-governance language. Fuzzy-set qualitative comparative analysis showed two equifinal paths to integrated AI-governance disclosure: social-risk framing combined with intensive AI use, which was retained in every robustness specification, and a narrower path combining environmental framing, AI use and large size. Item 1C cybersecurity governance did not enter either main path, but its role is uncertain because the score varies little across firms. AI was mentioned within Item 1C by 24.8% of firms, and 8-K Item 1.05 incident disclosures did not distinguish profiles. Because all measures come from automated coding of disclosure, the findings describe reported rather than verified governance, and the cross-sectional design does not support causal claims. The findings motivate a Unified ESG–Cybersecurity Framework for AI Governance that applies the four NIST AI RMF functions jointly across the three domains through one AI inventory, one risk taxonomy, one reporting line and one disclosure narrative.

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.

This research developed a simulation-based framework for quantifying the carbon footprint and operational efficiency of machine-speed cybersecurity defenses in autonomous industrial control systems (ICS) and smart manufacturing environments. Traditional high-energy intrusion detection systems (IDS) relying on intensive machine learning models contribute significantly to carbon emissions, creating an urgent need for green cybersecurity solutions that balance robust threat detection with sustainability. Using publicly available datasets mirroring UNSW-NB15 and CIC-IDS2017, the study modeled and compared lightweight models (Random Forest and Logistic Regression) against an intensive Long Short-Term Memory network. Carbon emissions were tracked with CodeCarbon, while the proposed eco-efficiency index (EEI) integrated F1-score, carbon footprint, and latency to evaluate trade-offs. Results demonstrated that the ultra-light Logistic Regression model achieved up to 91% carbon reduction and ultra-low latency (<3 ms), yielding the highest EEI while satisfying machine-speed requirements for real-time ICS protection. The GreenShield-ICS framework offers a reproducible approach for low-carbon defenses in Industry 4.0, with implications for guiding policy standardization and accelerating industry adoption toward net-zero, energy-efficient cybersecurity practices.

This study examines how prompt injection and training data poisoning compromise artificial intelligence (AI) systems operating within PCI DSS–protected financial environments and evaluates the adequacy of existing compliance controls in addressing these emerging threats. A multi-phase quantitative research design was employed, integrating controlled convolutional neural network (CNN)–based poisoning simulation, logistic regression modeling using categorized breach variables, PCI DSS v4.0 compliance gap index construction, and Structural Equation Modeling (SEM) to validate a proposed AI security governance framework. Experimental results indicate that even minimal poisoning can produce substantial latent compromise: at a 5% poisoning rate, attack success reached 78.6% despite only a 4.6% decline in overall model accuracy. Logistic regression analysis further revealed that logging failures significantly reduce breach detection likelihood by 76% (OR = 0.24, p < .001), highlighting the central role of monitoring controls in compliance-based security architectures. Compliance coverage analysis identified a substantial governance gap, with only 33.3% of PCI DSS domains explicitly addressing prompt injection risks, producing an explicit coverage deficit of 66.7%. The largest control deficiencies were observed in domains related to transmission encryption, authentication, anti-malware protections, and secure model governance, which provide limited safeguards against AI-specific manipulation. Structural modeling results demonstrated strong explanatory power (R² = 0.74), with preventive lifecycle controls exerting the strongest influence on AI risk reduction (γ = 0.61). These findings highlight that financial institutions may remain technically compliant with PCI DSS while AI-driven systems remain vulnerable to adversarial manipulation. The study therefore recommends incorporating explicit AI lifecycle governance, continuous model integrity monitoring, and strengthened third-party AI oversight into PCI DSS revisions to improve resilience of AI-enabled financial infrastructures.

The rapid deployment of fifth-generation (5G) networks and the adoption of network slicing have significantly enhanced connectivity and service customization, but they have also expanded the cyber-attack surface of telecommunications infrastructure. At the same time, emerging quantum computing capabilities threaten the security of widely used classical cryptographic schemes such as RSA and Elliptic Curve Cryptography. Despite growing concerns, existing security frameworks for 5G slicing rarely integrate post-quantum cryptography with intelligent threat detection mechanisms. This study addresses this gap by developing a quantum-resistant security architecture for 5G network slicing that combines post-quantum cryptography and artificial intelligence–based anomaly detection. The framework incorporates NIST-standardized algorithms including CRYSTALS‑Kyber for secure key exchange and CRYSTALS‑Dilithium and FALCON for authentication. A hybrid post-quantum/classical TLS scheme was implemented to support secure key establishment with minimal latency overhead of 1.65 ms. Additionally, an AI-driven anomaly detection model using a stacked ensemble of Isolation Forest and XGBoost monitored slice orchestration behavior to detect cyber threats. Simulation results demonstrated high detection performance with a macro F1-score of 0.962 and AUC-ROC of 0.985. The proposed framework offers a practical and scalable approach for securing next-generation telecommunications infrastructure against both current cyber threats and future quantum attacks.

This study investigates security-visibility challenges in cloud-native microservices by analysing adversarial behaviours using the MITRE ATT&CK container-techniques dataset, assessing observability limitations through time-series telemetry-coverage quantification applied to the OpenTelemetry Demo dataset, and proposing an AI-driven observability model for security anomaly detection. The proposed method employs an unsupervised deep autoencoder trained on integrated telemetry from the Google Cloud Microservices Demo and Kubernetes audit logs to learn normal system behaviour and identify anomalies, defined as statistically significant deviations from established baseline telemetry patterns associated with validated security events. Model performance was evaluated using a hold-out train–test validation protocol across combined multi-source telemetry datasets. Results show that the AI-driven observability model improves detection effectiveness compared with a baseline rule-based monitoring approach, increasing recall from 54% to 84%, reducing false negatives from 690 to 240, and substantially lowering average detection latency under comparable operational conditions. These findings demonstrate that AI-enabled observability enhances detection accuracy, reduces monitoring blind spots and supports more timely operational decision-making through improved multi-layer telemetry correlation. Accordingly, the study recommends unified telemetry pipelines, consistent instrumentation across microservices, adoption of unsupervised anomaly-detection models and the development of AI-observability standards to improve the reliability and security of cloud-native systems.

This study explores how integrating blockchain technology with artificial intelligence can strengthen trust, transparency, and regulatory compliance in cross-border financial transactions. Drawing on open-source international datasets and quantitative analysis, the research shows that transaction costs rise as the number of intermediaries increases, while greater transparency significantly lowers these costs. Using transaction data from the Elliptic AML dataset, AI-based models achieved a fraud detection accuracy of 90.1%, demonstrating strong potential for identifying illicit financial activity. Structural equation modelling based on Financial Action Task Force indicators confirms that transparency and AI-driven risk detection jointly enhance compliance efficiency. To assess system-wide impact, a simulation using Bank for International Settlements cross-border statistics indicates that an integrated approach can reduce settlement times by over one-third and substantially improve data accuracy. Building on these findings, the study proposes the Blockchain–AI Trust and Transparency Framework (B-AITTF) as a practical reference model for financial institutions and regulators seeking to implement transparent, data-driven, and interoperable compliance mechanisms in cross-border finance, addressing the persistent fragmentation, opacity, and regulatory misalignment that continue to undermine trust and efficiency in existing cross-border payment infrastructures.