AI Risk Governance in High-Reliability Enterprises: Integrating CPMAI into Enterprise Risk Management
DOI:
https://doi.org/10.54489/ijbas.v6i1.606Keywords:
AI, CPMAI, Program Risk Management, AI Governance, Risk Assurance, Framework, MLOps MonitoringAbstract
This study has examined how ADNOC can enhance risk governance and AI program reliability by adopting the Cognitive Project Management for AI (CPMAI) framework all over AI lifecycles. The research addresses gaps found in the Virtual lab assessment that has shown weakness in the vendor oversight, operational tracking, data lineage and model evaluations. With the help of the mixed method approaches, quantitative survey response has been obtained from 50 employees of ADNOC and qualitative insight gains conducted interviews with four practitioners. The response has examined using thematic coding aligns with the PMI risk management domains and CPMAI phases. The findings of the study have shown robust agreement among the employees that CPMAI improves coordination, risk identification and transparency across the key stakeholder. The interview has also expressed that limited cultural readiness, governance fragmentations as well as lack of the standard process hamper consistent AI oversights.
Therefore, the study proposed AI Risk Assurance Framework (ARAF) implementing CPMAI assisted control, bias identification, governance committees as well as MLOps monitoring to address the identified gaps. The result has confirmed the adoption of the CPMAi positively impacts the AI reliability and risk mitigations, though success realized in capacity building, leadership awareness and stakeholder engagement. The study contributes to lifecycle-based governance models mainly to deal with the high-risk governance AI environments and shows how a structured framework can reinforce long-term operational sustainability, compliance and trust in
AI initiatives of ADNOC.
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