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         article-type="Research Paper"
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  <front>
    <journal-meta>
      <journal-title-group>
        <journal-title>Journal of Artificial Intelligence and Big Data Disciplines</journal-title>
        <abbrev-journal-title abbrev-type="publisher">jaibdd</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="epub">3049-2122</issn>
      <publisher>
        <publisher-name>Dr. Aaluri Seenu</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.70179/32w32914</article-id>
      <article-id pub-id-type="publisher-id">jaibdd120014</article-id>
      <title-group>
        <article-title>Enhancing ERP Systems with Big Data Analytics and AI-Driven Cybersecurity Mechanisms</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Kaulwar</surname>
            <given-names>Pallav kumar</given-names>
          </name>
          <xref ref-type="aff" rid="aff1"/>
        </contrib>
      </contrib-group>
      <aff id="aff1">Director IT,  KPMG, Dallas</aff>
      <pub-date pub-type="epub" iso-8601-date="2026-02-24">
        <month>02</month>
        <day>24</day>
        <year>2026</year>
      </pub-date>
      <volume>2</volume>
      <issue>1</issue>
      <abstract>
        <p>This paper introduces a framework to enhance the present enterprise resource planning (ERP) systems by integrating big data analytics and state-of-the-art artificial intelligence (AI)-driven cybersecurity mechanisms. Nowadays, due to the diversity and high volume of data, the use of business intelligence and analytics solutions is paramount for ERP systems. This use results in the optimization of enterprise resource planning functions. AI can be used with cybersecurity mechanisms to predict and stop the behavior of a potential threat, thus leading to an optimal cybersecurity model. However, the biggest challenge is the integration of run-time cybersecurity solutions with the enterprise resources in the ERP systems. The proposed solutions incorporate advanced AI-driven cybersecurity techniques for intrusion detection, anomaly detection innovation, and prediction-based mechanisms to mitigate potential threats at the onset.

In particular, the paper proposes a set of measures and guidelines for IT stakeholders and business executives on how to integrate technology innovation while maintaining the ERP systems to be modern, relevant, and adaptive in a competitive business environment. We believe that our work is beneficial for both researchers and practitioners to systematically understand the significance of integrating big data analytics with the existing ERP functions and the application of AI in the cybersecurity model for enterprises. Our results emphasize that the implementation of big data and AI-based solutions within the organization will support innovation, safeguard security mechanisms, and lead to a sustainable position in the digital market.</p>
      </abstract>
      <kwd-group kwd-group-type="author">
        <kwd>enterprise resource planning</kwd>
        <kwd>ERP systems</kwd>
        <kwd>big data analytics</kwd>
        <kwd>AI-driven cybersecurity</kwd>
        <kwd>business intelligence</kwd>
        <kwd>intrusion detection</kwd>
        <kwd>anomaly detection</kwd>
        <kwd>threat prediction</kwd>
        <kwd>cybersecurity integration</kwd>
        <kwd>enterprise data optimization</kwd>
        <kwd>IT innovation</kwd>
        <kwd>adaptive ERP systems</kwd>
        <kwd>digital enterprise security</kwd>
        <kwd>AI in ERP</kwd>
        <kwd>predictive security models</kwd>
        <kwd>technology integration</kwd>
        <kwd>IT stakeholder guidelines</kwd>
        <kwd>competitive business environment</kwd>
        <kwd>data-driven decision making</kwd>
        <kwd>sustainable digital solutions</kwd>
        <kwd>organizational security</kwd>
        <kwd>advanced cybersecurity mechanisms</kwd>
      </kwd-group>
    </article-meta>
  </front>
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