AI-Driven continuous compliance and threat intelligence model for adaptive GRC in complex digital ecosystems
Bibliographic record
Abstract
The rapid evolution of digital ecosystems—characterized by multi-cloud infrastructures, IoT proliferation, and distributed data flows—has fundamentally altered the governance, risk, and compliance (GRC) landscape. Traditional GRC frameworks, rooted in periodic audits and reactive controls, are increasingly inadequate for addressing the scale, speed, and sophistication of modern cyber threats. This review paper examines the emergence of AI-driven continuous compliance and threat intelligence models as adaptive solutions for managing GRC in complex digital environments. It synthesizes existing literature on regulatory mapping, continuous auditing, and real-time threat intelligence integration, while identifying key limitations of siloed, manual approaches. The study highlights how artificial intelligence and machine learning can enable proactive risk identification, predictive analytics, and automated remediation, transforming GRC into a continuous and intelligent function. Furthermore, the paper explores technical methodologies such as natural language processing for regulatory interpretation, anomaly detection algorithms for compliance monitoring, and predictive modeling for risk forecasting. By analyzing current advancements, challenges, and research gaps, this review proposes a conceptual framework that positions AI as a catalyst for adaptive, resilient, and future-ready GRC architectures. The findings underscore the critical need for intelligent, real-time governance models to ensure organizational sustainability in the face of regulatory volatility and cyber risk escalation. Keywords: Continuous Compliance, Threat Intelligence, Adaptive GRC, Artificial Intelligence in Governance, Predictive Risk Analysis, Automated Remediation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".