AI-Powered Business Transformation: The Evolving Landscape of Analytics and Security Trends Albin Shaji* Abel Jopaul V P
Fuente:
Zenodo
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Recurso digital |
| Publié: |
Zenodo
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866902236979789824 |
|---|---|
| author | ALBIN SHAJI AND ABEL JOPAUL V P |
| author_facet | ALBIN SHAJI AND ABEL JOPAUL V P |
| contents | <table style="border-collapse: collapse; margin-left: 6.75pt; margin-right: 6.75pt;"> <tbody> <tr style="height: 11.05pt;"> <td style="width: 363.25pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 11.05pt;"> <p><strong><span>ABSTRACT</span></strong></p> </td> <td style="padding: 0cm 0cm 0cm 0cm;"> <p> </p> </td> </tr> <tr style="height: 53.6pt;"> <td style="width: 368.2pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 53.6pt;" colspan="2"> <p><strong><em><span><span> </span></span></em></strong><em><span><span> </span></span></em><span><span> </span></span><span><span> </span>This study examines artificial intelligence's transformative impact on business analytics and cybersecurity paradigms within enterprise environments. Employing a systematic review methodology, we analyzed 127 peer-reviewed articles (2020-2025) and evaluated anonymized case studies from Fortune 500 organizations through the PRISMA framework. Key findings reveal that AI-driven analytics implementations achieved median efficiency gains of 47% and ROI improvements of 40% across 70% of analyzed enterprises. Concurrently, AI-specific security incidents increased by 28%, with adversarial attacks and model poisoning representing 62% of reported breaches. Emerging trends include federated learning architectures for privacy-preserving analytics and explainable AI frameworks bridging operational transparency with threat detection. The research identifies critical gaps in integrated governance models that simultaneously optimize analytical performance and security resilience. Implications suggest that sustainable AI transformation requires hybrid frameworks combining technical safeguards, organizational change management, and executive-level risk literacy to navigate the dual imperatives of innovation and protection.</span></p> <div> <p><strong><span>Key words: </span></strong><span><span> </span></span><span>Artificial Intelligence (AI) Transformation, Business Analytics, Cybersecurity, Federated Learning, Explainable AI (XAI)</span></p> </div> <p><span> </span></p> </td> </tr> </tbody> </table> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17544642 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Powered Business Transformation: The Evolving Landscape of Analytics and Security Trends Albin Shaji* Abel Jopaul V P ALBIN SHAJI AND ABEL JOPAUL V P Artificial Intelligence (AI) Transformation, Business Analytics, Cybersecurity, Federated Learning, Explainable AI (XAI) <table style="border-collapse: collapse; margin-left: 6.75pt; margin-right: 6.75pt;"> <tbody> <tr style="height: 11.05pt;"> <td style="width: 363.25pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 11.05pt;"> <p><strong><span>ABSTRACT</span></strong></p> </td> <td style="padding: 0cm 0cm 0cm 0cm;"> <p> </p> </td> </tr> <tr style="height: 53.6pt;"> <td style="width: 368.2pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 53.6pt;" colspan="2"> <p><strong><em><span><span> </span></span></em></strong><em><span><span> </span></span></em><span><span> </span></span><span><span> </span>This study examines artificial intelligence's transformative impact on business analytics and cybersecurity paradigms within enterprise environments. Employing a systematic review methodology, we analyzed 127 peer-reviewed articles (2020-2025) and evaluated anonymized case studies from Fortune 500 organizations through the PRISMA framework. Key findings reveal that AI-driven analytics implementations achieved median efficiency gains of 47% and ROI improvements of 40% across 70% of analyzed enterprises. Concurrently, AI-specific security incidents increased by 28%, with adversarial attacks and model poisoning representing 62% of reported breaches. Emerging trends include federated learning architectures for privacy-preserving analytics and explainable AI frameworks bridging operational transparency with threat detection. The research identifies critical gaps in integrated governance models that simultaneously optimize analytical performance and security resilience. Implications suggest that sustainable AI transformation requires hybrid frameworks combining technical safeguards, organizational change management, and executive-level risk literacy to navigate the dual imperatives of innovation and protection.</span></p> <div> <p><strong><span>Key words: </span></strong><span><span> </span></span><span>Artificial Intelligence (AI) Transformation, Business Analytics, Cybersecurity, Federated Learning, Explainable AI (XAI)</span></p> </div> <p><span> </span></p> </td> </tr> </tbody> </table> |
| title | AI-Powered Business Transformation: The Evolving Landscape of Analytics and Security Trends Albin Shaji* Abel Jopaul V P |
| topic | Artificial Intelligence (AI) Transformation, Business Analytics, Cybersecurity, Federated Learning, Explainable AI (XAI) |
| url | https://doi.org/10.5281/zenodo.17544642 |