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1709782PublishedVol 5 · Issue 10

AI-Driven Risk Modeling in Enterprise Security Governance: A Framework for Proactive Threat Anticipation and Policy Alignment

Tim Abdiukov

Subject area: Science,Engineering and Technology  ·  Area of research: Artificial Intelligence

Abstract

AI is revolutionizing enterprise security governance, enhancing risk model sensitivity, proactive threat identification, and policy alignment. As cyber threats become increasingly complex, organizations are finding it more challenging to equip themselves with traditional security-based platforms. They are thus adopting AI-driven systems to fulfill their security requirements. The advantageous outcomes of AI technologies in enterprise offerings enable forward-thinking and the identification of potential risks before they occur, providing more flexible and accurate risk models. Moreover, the ability of AI to coordinate security policies in response to changing threats ensures that organizations will be highly resilient and unaffected by emerging threats. The study demonstrates that AI can make a significant contribution to ensuring accurate risk assessment, improving the overall non-representative level of threat detection, and facilitating real-time adjustments to security policy. The study presents an in-depth model for implementing AI in enterprise security governance, providing valuable insights into how AI enhances the security resilience of organizations, improves policy decision-making mechanisms, and overall security. The results support the significance of AI in revamping enterprise security practices, which will become more dynamically responsive and effective in adapting to emerging threats.

Keywords

Artificial Intelligence, enterprise security governance, risk modeling, proactive threat detection, policy alignment, cybersecurity, security policies, AI-driven solutions, risk assessment, security resilience.

How to cite this paper

Tim Abdiukov "AI-Driven Risk Modeling in Enterprise Security Governance: A Framework for Proactive Threat Anticipation and Policy Alignment" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 388-398
Tim Abdiukov "AI-Driven Risk Modeling in Enterprise Security Governance: A Framework for Proactive Threat Anticipation and Policy Alignment" Iconic Research And Engineering Journals, vol. 5, no. 10, Apr. 2022
Tim Abdiukov (2022). AI-Driven Risk Modeling in Enterprise Security Governance: A Framework for Proactive Threat Anticipation and Policy Alignment. Iconic Research And Engineering Journals, 5(10).
Tim Abdiukov "AI-Driven Risk Modeling in Enterprise Security Governance: A Framework for Proactive Threat Anticipation and Policy Alignment" Iconic Research And Engineering Journals, vol. 5, no. 10, Apr. 2022.
@article{1709782,
      author = {Tim Abdiukov},
      title = {AI-Driven Risk Modeling in Enterprise Security Governance: A Framework for Proactive Threat Anticipation and Policy Alignment},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {10},
      pages = {388-398},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709782.pdf},
      abstract = {AI is revolutionizing enterprise security governance, enhancing risk model sensitivity, proactive threat identification, and policy alignment. As cyber threats become increasingly complex, organizations are finding it more challenging to equip themselves with traditional security-based platforms. They are thus adopting AI-driven systems to fulfill their security requirements. The advantageous outcomes of AI technologies in enterprise offerings enable forward-thinking and the identification of potential risks before they occur, providing more flexible and accurate risk models. Moreover, the ability of AI to coordinate security policies in response to changing threats ensures that organizations will be highly resilient and unaffected by emerging threats. The study demonstrates that AI can make a significant contribution to ensuring accurate risk assessment, improving the overall non-representative level of threat detection, and facilitating real-time adjustments to security policy. The study presents an in-depth model for implementing AI in enterprise security governance, providing valuable insights into how AI enhances the security resilience of organizations, improves policy decision-making mechanisms, and overall security. The results support the significance of AI in revamping enterprise security practices, which will become more dynamically responsive and effective in adapting to emerging threats.},
      keywords = {Artificial Intelligence, enterprise security governance, risk modeling, proactive threat detection, policy alignment, cybersecurity, security policies, AI-driven solutions, risk assessment, security resilience.},
      month = {April},
  }