Sunday Abayomi Joseph

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.

The rapid evolution of Industry 4.0 and emerging Industry 5.0 paradigms has accelerated the adoption of autonomous cyber-physical systems (CPS) in smart manufacturing, creating a need for robust runtime policy orchestration to ensure governance, regulatory compliance, and resilience under dynamic disruptions and cyber threats. This study addresses the lack of unified frameworks by proposing the Policy Resilient Orchestrator (PRO), a simulation-driven architecture integrating machine-readable policy cards, multi-agent reinforcement learning using Proximal Policy Optimization (PPO), multi-objective optimization via NSGA-II, runtime verification, and digital twin-based validation. A quantitative, desk-based methodology utilizing synthetic and benchmark datasets was employed to model CPS environments without physical hardware. NSGA-II generated Pareto-optimal policy configurations, while PPO enabled adaptive runtime control supported by simplex switching for resilience. Monte Carlo simulations across normal, attack, and failure scenarios showed significant improvements, with Governance Efficiency (0.961), Resilience Index (0.993), and Downtime Reduction (0.988), all statistically outperforming baselines (p < 0.0001). PRO demonstrates a scalable, reliable solution for trustworthy smart manufacturing systems.

This study critically evaluates WhatsApp’s security ecosystem through a shared responsibility framework, examining the interaction between platform safeguards, usability, and user accountability. A mixed-methods approach was employed, integrating four open-access cybersecurity datasets with primary data from a structured questionnaire completed by 835 respondents and semi-structured interviews with five members of an alumni WhatsApp group affected by an account breach. Analytical techniques included sentiment–feature mapping using logistic regression, risk distribution profiling with Relative Risk Ratios, exploratory factor analysis with Cronbach’s Alpha, multivariate time-series correlation with Granger causality, and thematic analysis of interview transcripts. Results indicate that two-step verification recorded the strongest positive sentiment and highest odds ratio (OR = 2.34), yet adoption remained low, with fewer than 40 percent of users enabling the feature. Investment fraud accounted for 61 percent of high-loss scam cases, while questionnaire responses revealed persistent engagement in risky behaviours such as joining unverified groups. Qualitative findings further demonstrate that trust dynamics, misunderstanding of security prompts, and limited practical awareness significantly contributed to the observed account breach. Behavioural accountability constructs explained 80 percent of variance in security outcomes; however, only 24 percent of users consistently exhibited high accountability. Platform-level interventions such as key transparency were associated with reductions in banned accounts (r = −0.62, p = 0.04), while spikes in abuse incidents triggered reactive increases in two-step verification adoption. These findings underscore the limitations of encryption-centric security models and highlight the need for improved usability, targeted awareness strategies, and strengthened user accountability. The study provides actionable insights for platform designers, policymakers, and users seeking to enhance digital resilience in encrypted communication environments.

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.

Artificial intelligence (AI) is revolutionizing cybersecurity in digital currency transactions by enhancing fraud detection and risk mitigation. This study leverages machine learning techniques, including logistic regression and random forest classifiers, to evaluate AI’s efficacy in detecting fraudulent transactions using datasets such as the REKT Database, Elliptic Crypto Transaction Dataset, Cipher Trace AML Reports, and IEEE DataPort Financial Transactions Dataset. The analysis employs confusion matrix evaluation, fairness-aware machine learning techniques, and regression modeling to assess AI’s impact on fraud prevention. Findings reveal that AI-driven security measures reduced fraudulent activities by up to 76.86%, underscoring their effectiveness. However, the models exhibited a high false-negative rate of 89.54%, signaling a significant risk of undetected illicit transactions. Algorithmic bias is also evident, with a disparate impact ratio of 0.7793, indicating fairness concerns in AI fraud detection. These limitations highlight the need for adversarial training, fairness-aware optimization techniques, and anomaly detection refinements to improve model reliability. Additionally, regulatory frameworks, such as the EU AI Act, must be considered to ensure compliance, ethical fairness, and transparency in AI-powered cybersecurity applications. This study emphasizes the dual role of AI in strengthening fraud detection while presenting new challenges, such as algorithmic bias and adversarial vulnerabilities. To enhance AI’s effectiveness, quantum-proof encryption, fairness-aware fraud detection models, and transparent governance frameworks are recommended. These findings contribute to the ongoing discourse on AI-driven cybersecurity in digital finance, offering insights into mitigating risks while harnessing AI’s potential for securing digital currency ecosystems.

Artificial intelligence (AI) is revolutionizing cybersecurity in digital currency transactions by enhancing fraud detection and risk mitigation. This study leverages machine learning techniques, including logistic regression and random forest classifiers, to evaluate AI's efficacy in detecting fraudulent transactions using datasets such as the REKT Database, Elliptic Crypto Transaction Dataset, Cipher Trace AML Reports, and IEEE DataPort Financial Transactions Dataset. The analysis employs confusion matrix evaluation, fairness-aware machine learning techniques, and regression modeling to assess AI's impact on fraud prevention. Findings reveal that AI-driven security measures reduced fraudulent activities by up to 76.86%, underscoring their effectiveness. However, the models exhibited a high false-negative rate of 89.54%, signaling a significant risk of undetected illicit transactions. Algorithmic bias is also evident, with a disparate impact ratio of 0.7793, indicating fairness concerns in AI fraud detection. These limitations highlight the need for adversarial training, fairness-aware optimization techniques, and anomaly detection refinements to improve model reliability. Additionally, regulatory frameworks, such as the EU AI Act, must be considered to ensure compliance, ethical fairness, and transparency in AI-powered cybersecurity applications. This study emphasizes the dual role of AI in strengthening fraud detection while presenting new challenges, such as algorithmic bias and adversarial vulnerabilities. To enhance AI's effectiveness, quantum-proof encryption, fairness-aware fraud detection models, and transparent governance frameworks are recommended. These findings contribute to the ongoing discourse on AI-driven cybersecurity in digital finance, offering insights into mitigating risks while harnessing AI's potential for securing digital currency ecosystems.

This study explores the impact of AI-driven technological change on workforce management, focusing on job displacement, employee satisfaction, and productivity. A mixed-method approach was employed, including logistic regression, K-means clustering, and multivariate regression analysis, to evaluate the effectiveness of technocultural interventions (upskilling programs, ethical AI frameworks, and innovation culture). Data was sourced from the U.S. Bureau of Labor Statistics (BLS) and survey questionnaires. Logistic regression revealed that routine AI adoption showed a weak positive relationship with job displacement (B = 0.013, p = 0.111), but the overall model was not statistically significant. The K-means clustering identified three distinct organizational patterns in adopting technocultural interventions. Multivariate regression highlighted the substantial role of leadership commitment in increasing employee satisfaction (B = -0.067, p = 0.039) but found limited direct effects of upskilling programs and AI frameworks on productivity. Based on the findings, the study highlights the importance of customized, sector-specific interventions and recommends that organizations integrate leadership and ethical considerations to manage AI-driven changes effectively.

This study employs a mixed-methods approach to examine the optimal balance between AI-powered automation and human oversight in information governance frameworks, aiming to enhance organizational productivity, efficiency, and compliance. Quantitative data collected from 384 respondents were analyzed using Pearson correlation, regression models, and Structural Equation Modeling (SEM). The results reveal strong positive correlations between AI automation levels and both organization size (r = 0.55, p < .01) and AI adoption duration (r = 0.62, p < .01). Regression analysis indicates that higher levels of AI automation significantly improve error reduction (β = 1.12, p < .001) and compliance (β = 1.05, p < .001), especially in larger organizations with longer AI adoption periods. SEM findings highlight that human oversight positively impacts error reduction (β = 0.65, p < .001) and compliance improvement (β = 0.72, p < .001), and the interaction between human oversight and AI automation further enhances these outcomes (error reduction: β = 0.32, p < .001; compliance improvement: β = 0.35, p < .001). The qualitative analysis, involving thematic extraction from industry reports, reveals ethical challenges such as data quality issues, algorithmic bias, and privacy concerns. Hence, it is necessary to integrate human oversight to ensure ethical standards and build stakeholder trust in AI-driven systems. The study concludes with practical recommendations for organizations: establishing transparent AI governance frameworks, investing in continuous training for employees, and regularly auditing AI processes to mitigate risks. By addressing both the technological and ethical dimensions, organizations can implement AI-powered information governance that not only boosts productivity and efficiency but also ensures compliance and ethical integrity.

This study explores the impact of blockchain technology on aerospace supply chains, focusing on mitigating risks such as supply chain disruptions, counterfeit parts, and regulatory compliance challenges. A mixed-methods approach was employed, combining quantitative and qualitative analysis using data sourced from publicly available databases, including the Federal Aviation Administration (FAA), the Aerospace Industries Association (AIA), the International Air Transport Association (IATA), and reports from blockchain technology providers. Descriptive analysis identified supply chain disruptions (M = 3.74, SD = 1.17) as the most significant risk, followed by counterfeit parts (M = 3.34, SD = 1.20). Structural Equation Modeling (SEM) demonstrated that part authentication and provenance tracking had the strongest impact on improving traceability (β = 0.40, p < 0.001) and transparency (β = 0.38, p < 0.001). A Cost-Benefit Analysis (CBA) revealed a positive return on investment, with cost-benefit ratios up to 2.31. Blockchain adoption was also found to reduce CO₂ emissions by up to 25%, while improving operational efficiency through shorter procurement lead times and lower administrative costs. However, regulatory misalignment and interoperability challenges remain barriers to full adoption. The study concludes that blockchain is a transformative tool for enhancing traceability, security, and sustainability in aerospace supply chains.

This study explores the balance between data privacy and regulatory compliance in blockchain-based financial systems, focusing on privacy-enhancing technologies (PETs) such as Zero-Knowledge Proofs (ZKPs) and multiparty computations (MPCs). Through a comprehensive methodology combining literature review, comparative analysis, and empirical testing on the Ethereum test network, the research reveals significant trade-offs. Implementing ZKPs increased transaction times from 5 seconds to 12 seconds and gas fees from 0.02 ETH to 0.05 ETH, while computational load rose by 60%, highlighting the impact on scalability and efficiency. Chi-Square tests and regression analysis uncovered notable algorithmic biases, with low-value accounts experiencing 15% fewer transaction approvals and small mining pools receiving 20% fewer rewards than larger counterparts. Additionally, MPCs, while offering robust privacy, increased communication overhead by 35%, posing scalability challenges. The study recommends adopting a tiered privacy approach, implementing basic privacy measures for low-sensitivity transactions, and advanced technologies like ZKPs for high-sensitivity transactions while optimizing ZKPs to reduce their computational burden and enhance transaction speeds, and integrating artificial intelligence to detect and mitigate algorithmic biases in blockchain systems. Future research should also explore hybrid privacy solutions that combine the strengths of different PETs, such as ZKPs and MPCs, to achieve both robust privacy and high efficiency. Furthermore, investigating quantum-resistant cryptographic methods is crucial to safeguarding blockchain systems against emerging threats. These insights provide valuable guidance for financial institutions, blockchain developers, and policymakers, promoting the development of blockchain-based financial systems that optimize data privacy while maintaining system performance and regulatory compliance.