Abayomi Titilola Olutimehin

Enterprise cybersecurity has shifted from perimeter defence toward continuously verified architectures, yet zero trust, layered defence, and formal risk governance are typically adopted in isolation, producing fragmented estates in which expenditure rises while measurable resilience does not. Existing literature examines each paradigm separately, leaving their interaction at enterprise scale unexamined. This study developed the Integrated Architecture Resilience Model, expressing available control surface as a weighted composite of governance, layering, and verification, and relating it to observed resilience. Two open datasets were analysed, comprising a versioned adversary technique catalogue and a community breach repository, yielding 638 qualifying incidents across financial services, healthcare, government, and critical infrastructure. Regression, configurational analysis, and stratified cross-validation were applied, with coding reliability assessed independently. Threat profiles differed significantly by sector, and 111 of 697 active techniques, representing 15.9 per cent, carried no mapped mitigation, concentrating in discovery. Layering dominated coverage while verification remained weakest. Chance-corrected coding agreement reached 0.683, indicating substantial reliability. The composite explained 35.9 per cent of variance in-sample but failed under cross-validation, establishing that documented coverage and enacted protection are distinct. The study contributes a reproducible diagnostic framework and evidence that control breadth alone does not secure resilience, redirecting attention toward verification depth.

Agentic AI has strong potential to boost productivity and growth, but current evidence suggests it is more likely reinforcing existing core–periphery inequalities than fundamentally reshaping the global economic order, due to uneven access, capabilities, and institutional readiness across countries. This study examines the economic value of Agentic Artificial Intelligence (AI), defined as advanced AI systems capable of autonomous decision-making and task execution, by analysing its impact on firm-level productivity, financial performance, and macroeconomic growth across developed and emerging economies. While existing literature has extensively examined general AI adoption, limited empirical evidence exists on how more autonomous, agent-like systems contribute to economic outcomes and whether their benefits are evenly distributed across different economic contexts. To address this gap, the study employs a quantitative panel data approach using data from the World Bank (World Development Indicators and Enterprise Surveys) and OECD AI indicators for the period 2015 to 2024. An AI Adoption Index is constructed using indicators of AI investment, business adoption, and innovation output, serving as a proxy for the diffusion of advanced AI capabilities, including agentic features. Fixed effects regression, mediation analysis, and quantile regression are used to estimate firm-level and macroeconomic relationships. The results show that AI adoption significantly improves firm-level productivity (β = 0.18, p < 0.01) and influences economic growth primarily through a productivity channel (β = 0.35, p < 0.01), with a comparatively weaker direct effect (β = 0.09). However, the magnitude of these effects varies across economic contexts, with developed economies experiencing substantially stronger growth impacts (approximately 0.33) than emerging economies (approximately 0.15). These findings suggest that while Agentic AI enhances economic performance, its benefits are mediated by structural conditions and are unevenly distributed across countries. The study contributes to the literature by providing an integrated micro to macro empirical framework and highlighting the role of advanced AI capabilities in reinforcing global economic disparities. Policy recommendations emphasise the need for investment in digital infrastructure, human capital development, and inclusive technology diffusion strategies to ensure more equitable distribution of AI-driven economic value.

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.

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.

Predictive maintenance has emerged as a cornerstone of Industry 4.0, enabling manufacturers to proactively identify and address equipment failures, minimize unplanned downtime, and optimize operational costs. However, realizing effective predictive maintenance in smart manufacturing environments requires overcoming persistent challenges related to real-time data communication, cybersecurity vulnerabilities, and system scalability. This study addresses these gaps by investigating the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies for predictive maintenance using the NASA C-MAPSS dataset. A quantitative methodology was employed, including transmission protocol analysis, cybersecurity assessment with an Isolation Forest-based Intrusion Detection System, scalability evaluation on edge and cloud infrastructures, and predictive modeling with Long Short-Term Memory (LSTM) networks. This research develops and empirically validates an integrated AI-IoT framework that unifies communication efficiency, cybersecurity resilience, and predictive modeling, representing a novel contribution to the state of the art. Results show MQTT achieved the lowest latency (50.21 ms), the IDS attained a Precision of 92.31%, edge systems supported up to 3352 MB before degradation, and the LSTM model outperformed linear regression with an RMSE of 14.25 and R² of 0.92. The study recommends that manufacturers adopt MQTT for efficient real-time communication, deploy AI-driven intrusion detection to safeguard predictive analytics, invest in scalable edge computing infrastructures, and implement deep learning models within hybrid edge-cloud architectures to enhance predictive maintenance reliability and support immediate, practical deployment in Industry 4.0 manufacturing systems.

The increasing adoption of cloud-based patient information systems in healthcare enhances efficiency and accessibility but introduces significant cybersecurity risks. Traditional encryption methods, such as RSA and ECC, are becoming vulnerable due to rapid advancements in quantum computing, necessitating the transition to quantum-resistant encryption. This study evaluates the effectiveness of post-quantum cryptographic (PQC) solutions in securing cloud-stored healthcare data. Using publicly available datasets, including HHS breach records, NIST PQC benchmarks, and HIMSS cybersecurity reports, this research identifies trends in healthcare data breaches, encryption adoption, and regulatory penalties. Findings indicate a strong correlation between increased cloud adoption and cybersecurity breaches, highlighting the urgent need for enhanced encryption. Post-quantum cryptographic algorithms, particularly CRYSTALS-Kyber and CRYSTALS-Dilithium, outperform traditional encryption methods in terms of security and computational efficiency, making them viable for healthcare applications. Institutions that have adopted PQC show a marked decline in regulatory fines, reinforcing its role in compliance and risk mitigation. This study underscores the need for early PQC adoption, increased investment in cybersecurity training, and regulatory collaboration to ensure seamless integration of quantum-resistant encryption. The research provides critical insights into the evolving cybersecurity landscape, emphasizing the necessity of transitioning to PQC to safeguard patient data against future quantum threats. These findings serve as a strategic guide for healthcare institutions and policymakers, advocating for proactive encryption strategies that align with regulatory standards and ensure long-term data security.

The increasing reliance on artificial intelligence (AI) in credit scoring has raised concerns about algorithmic bias and data privacy, necessitating robust cybersecurity risk assessment frameworks. This study investigates the role of cybersecurity risk assessment in mitigating these risks, utilizing multiple datasets, including the Home Mortgage Disclosure Act (HMDA) dataset, the Equifax Data Breach Report, the Financial Cybersecurity Incidents Database, and the MITRE ATT&CK Financial Sector Threat Intelligence Dataset. We employ statistical fairness metrics, Bayesian Probability Modeling, Markov Chain Analysis, and Monte Carlo Simulations to evaluate the extent of bias, privacy risks, and cybersecurity vulnerabilities. Findings reveal significant disparities in loan approvals, with Black applicants receiving approval rates 28% lower than White applicants (χ² = 59.83, p < 0.001), highlighting systemic bias in AI-driven credit scoring. Data privacy remains a pressing issue, as financial sector breaches affect an average of 5,069,760 individuals per incident. Insider threats pose the greatest risk, with a probability of 0.81 of leading to financial fraud. These findings underscore the urgency of integrating fairness-aware machine learning, enhancing regulatory compliance with AI governance policies, and deploying AI-driven cybersecurity tools to fortify financial AI applications against emerging threats. This research contributes to the broader discourse on ethical AI by providing a structured cybersecurity risk assessment approach to mitigate algorithmic bias and strengthen data privacy protections. Implementing these recommendations will enhance fairness, security, and transparency in AI-driven financial decision-making, ensuring compliance with evolving regulatory frameworks and fostering trust in automated credit scoring systems.

This study evaluates the feasibility of post-quantum cryptographic (PQC) algorithms in mitigating "Harvest Now, Decrypt Later" (HNDL) attacks as quantum computing advances threaten classical encryption. Using datasets from the NIST Post-Quantum Cryptography Project, Deloitte’s PQC Adoption Survey, and IBM & Google Quantum Roadmaps, the study applied statistical modeling, Monte Carlo simulations, and ARIMA forecasting to assess PQC security resilience, adoption readiness, and quantum decryption feasibility. The findings indicate that CRYSTALS-Kyber and CRYSTALS-Dilithium outperform RSA-2048 and ECC-256 in quantum resistance, with attack cost thresholds exceeding 1.91×10101.91 \times 10^{10}1.91×1010 compared to 2.24×1082.24 \times 10^82.24×108 for RSA-2048. Industry adoption varies, with government PQC implementation at 79%, finance at 67%, and healthcare lagging at 48%. Quantum decryption probability remains negligible until 2029 but rises to 78.6% by 2033. This study addresses the critical challenge of post-quantum cryptographic (PQC) adoption amid growing quantum threats, particularly "Harvest Now, Decrypt Later" (HNDL) attacks. By evaluating PQC security resilience, industry adoption, and quantum decryption feasibility, the findings provide strategic insights for policymakers and cybersecurity experts. Highlighting the vulnerabilities of CRYSTALS-Kyber to side-channel attacks, this research underscores the need for continuous cryptanalysis, hybrid encryption models, and adaptive security frameworks to future-proof data.Organizations must accelerate PQC adoption, integrate hybrid cryptographic models, enforce regulatory policies, and sustain cryptanalysis efforts to ensure long-term security.

This study examines the ethical and legal implications of Shadow AI in healthcare, finance, and education by analyzing unauthorized AI deployments and their impact on data privacy, cybersecurity, and regulatory compliance. Using a quantitative research approach, descriptive statistics, ordinal regression modeling, and network analysis were employed to assess AI violations using the MITRE ATLAS AI Incident Database, EU AI Act Public Database, and IBM X-Force Threat Intelligence Report. Findings reveal that privacy breaches are most prevalent in education (22 cases), bias-related issues dominate finance (20 cases), and cybersecurity risks are highest in healthcare (19 cases). Legal risk analysis shows a 20% probability of regulatory intervention, with breach type as the strongest determinant. Anomaly detection identified healthcare as the most vulnerable to AI-driven cyber threats (8 anomalies). This study contributes to AI governance literature by quantifying the impact of regulatory interventions on Shadow AI risks, demonstrating how enforcement actions influence unauthorized AI adoption trends. It also underscores the limitations of current frameworks (e.g., GDPR, HIPAA, SEC regulations) in mitigating AI-related violations. The findings emphasize the urgent need for sector-specific AI compliance frameworks, AI ethics committees, and real-time cybersecurity monitoring systems to mitigate risks. Strengthening legal accountability and regulatory enforcement is critical to preventing the unchecked proliferation of Shadow AI in sensitive industries. Recommendations include sector-specific AI compliance frameworks, AI ethics committees, cybersecurity policies, and stricter regulatory enforcement.

This study examines security risks, emerging technologies, and cryptographic techniques in cloud-based digital currency transactions using a quantitative research approach. Data was sourced from the REKT Database, Web3 Security Report, and Elliptic Open Dataset, employing descriptive statistical analysis, regression modeling, and time-series analysis to assess security vulnerabilities, fraud reduction trends, and regulatory compliance effectiveness. Findings reveal that AI-driven security measures reduced fraud cases by 55% from 2022 to 2025, while illicit transactions declined from 12.5% in 2019 to 6.1% in 2023, demonstrating the impact of cryptographic advancements and regulatory interventions. However, cybercriminals are shifting toward high-value, precision-based attacks, necessitating an integrated security framework. This study contributes to AI-driven security, cryptographic resilience, and regulatory compliance in cloud-based digital transactions. By analyzing emerging threats and security frameworks, it offers valuable insights for researchers, policymakers, and financial institutions. The findings aid in developing robust fraud detection, post-quantum cryptography, and regulatory strategies to enhance digital financial security. This research bridges technological advancements with compliance measures, ensuring a more resilient and secure cloud-based cryptocurrency ecosystem. The study recommends enhancing AI fraud detection with cryptographic security models, adopting post-quantum cryptography, strengthening regulatory compliance, and implementing zero-trust security models to ensure long-term resilience in cloud-based financial ecosystems.