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1713349 Vol 8 · Issue 5 Download Paper

The SAIS-GRC Framework: Engineering Trust and Secure, Agile Systems for Proactive AI Governance and Compliance

Adetunji Oludele Adebayo

Subject area: Science,Engineering and Technology  ·  Area of research: GenAI GRC, AI

DOI: https://doi.org/10.64388/IREV7I5-1713349

Abstract

Organisations urgently require a unified model to manage the escalating technical risks and regulatory demands of Artificial Intelligence (AI). Current governance methods fail because they address security and compliance in a reactive manner. This paper introduces the Secure, Agile, Integrated System for Governance, Risk, and Compliance (SAIS-GRC) model. SAIS-GRC integrates adversarial robustness controls directly into the enterprise operating system, ensuring compliance velocity and organisational agility. The model uses technical defence mechanisms, such as Differential Privacy, to proactively mitigate supply chain risks, including data poisoning and model manipulation. Structurally, SAIS-GRC mandates the cross-functional integration of engineering and legal expertise, aligning directly with global mandates such as the NIST AI Risk Management Model and the EU AI Act. Validation demonstrates tangible operational benefits. Enterprise implementations based on SAIS-GRC principles achieve operational cost reductions of up to 25% and deliver platform modernisation 2X faster than legacy methods (Siana Capital Management, 2024). This integrated structure transforms fragmented risk management into an immediate source of competitive advantage.

How to cite this paper

Adetunji Oludele Adebayo "The SAIS-GRC Framework: Engineering Trust and Secure, Agile Systems for Proactive AI Governance and Compliance" Iconic Research And Engineering Journals, vol. 8, no. 5, Nov. 2024, doi: https://doi.org/10.64388/IREV7I5-1713349
Adetunji Oludele Adebayo (2024). The SAIS-GRC Framework: Engineering Trust and Secure, Agile Systems for Proactive AI Governance and Compliance. Iconic Research And Engineering Journals, 8(5). doi: https://doi.org/10.64388/IREV7I5-1713349
Adetunji Oludele Adebayo "The SAIS-GRC Framework: Engineering Trust and Secure, Agile Systems for Proactive AI Governance and Compliance" Iconic Research And Engineering Journals, vol. 8, no. 5, Nov. 2024. Crossref, https://doi.org/10.64388/IREV7I5-1713349
@article{1713349,
      author = {Adetunji Oludele Adebayo},
      title = {The SAIS-GRC Framework: Engineering Trust and Secure, Agile Systems for Proactive AI Governance and Compliance},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {1475-1480},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713349.pdf},
      abstract = {Organisations urgently require a unified model to manage the escalating technical risks and regulatory demands of Artificial Intelligence (AI). Current governance methods fail because they address security and compliance in a reactive manner. This paper introduces the Secure, Agile, Integrated System for Governance, Risk, and Compliance (SAIS-GRC) model. SAIS-GRC integrates adversarial robustness controls directly into the enterprise operating system, ensuring compliance velocity and organisational agility. The model uses technical defence mechanisms, such as Differential Privacy, to proactively mitigate supply chain risks, including data poisoning and model manipulation. Structurally, SAIS-GRC mandates the cross-functional integration of engineering and legal expertise, aligning directly with global mandates such as the NIST AI Risk Management Model and the EU AI Act. Validation demonstrates tangible operational benefits. Enterprise implementations based on SAIS-GRC principles achieve operational cost reductions of up to 25% and deliver platform modernisation 2X faster than legacy methods (Siana Capital Management, 2024). This integrated structure transforms fragmented risk management into an immediate source of competitive advantage.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV7I5-1713349}
  }