An AI-Driven Architecture for Cross-Domain Data Management in Enterprise Systems
Bibliographic record
Abstract
Enterprise ecosystems have undergone an accelerated digital transformation, which has resulted in the exponential creation, fusion and use of multi-domain data, all of which is often heterogeneous. Today's businesses rely on interdependent platforms, such as finance, health care, manufacturing, logistics, cyber security, cloud computing, and intelligent automation. But conventional data management architectures face significant challenges in delivering smooth interoperability, scalability, governance and intelligent decision-making across these distributed spheres. It has become more challenging as cloud-based systems proliferate, microservices architectures grow, Internet of Things (IoT) devices increase, edge computing systems emerge and artificial intelligence (AI) applications become more common. In this context, it becomes critical for enterprises to have the ability to incorporate structured, semi-structured, and unstructured data, along with security, compliance, observability, and real-time analytics, into a cross-domain data management architecture. This paper introduces an architecture which leverages AI technologies such as machine learning, metadata intelligence, semantic interoperability, automated governance, and adaptive orchestration mechanisms for cross-domain data management in enterprise systems, all within a single enterprise data ecosystem. The proposed architecture utilizes AI models to enable automation of data discovery, classification, quality, anomaly detection, predictive governance, and policy enforcement in various areas of enterprise. The proposed framework enables dynamic cross domain interoperability with the help of intelligent metadata catalogs, federated learning mechanisms, API orchestration and cloud-native microservices instead of traditional enterprise data warehouses and/or separate data lake solutions. There are four main layers of the architecture: Data Acquisition Layer, Intelligent Processing Layer, Governance and Security Layer, and Enterprise Intelligence Layer. The Data Acquisition Layer facilitates multi-source ingestion from enterprise resource planning, customer relationship management, IoT sensors, cloud repositories and external APIs. The Intelligent Processing Layer combines machine learning pipelines, semantic mapping engines, natural language processing models, and graph-based knowledge representation and reasoning methods, allowing for intelligent data harmonization and context-awareness. The Governance and Security Layer combines zero-trust security principles, AI-powered threat intelligence, policy-based access rules, and automatic compliance auditing capabilities to provide enterprise-grade data protection. Lastly, with the Enterprise Intelligence Layer, business stakeholders gain real-time analytics, predictive insights, decision support systems, and adaptive visualization tools. The proposed model also helps overcome enterprise-class data management problems such as data silos, inconsistent metadata standards, latency in distributed systems, security issues, lack of observability, and compliance complexity. The architecture provides intelligent orchestration and automation through AI, which increases operational efficiency, data quality, and faster delivery of analytics and minimizes governance overhead. In addition, the framework also puts into practice principles of explainable AI to guarantee transparency in automated decision making processes, a key element for enterprise trust and regulatory compliance. The analysis was done against traditional centralized architectures, federated data systems and cloud based integration models. The experimental results have shown that the proposed architecture with the integration of AI brings about significant enhancements in interoperability efficiency, data accessibility, governance automation, and analytical responsiveness. The framework proved to be more efficient at data integration by 38%, more accurate on metadata by 41% and more accurate on predictive anomaly detection by 46% than enterprise integration systems. Moreover, automated policy enforcement eliminated the compliance management overhead about 35%. In the study, the use of AI-powered observability and intelligent data catalogs is also noted for their ability to drive operational sustainability and enterprise resilience. The future extensions for the architecture also include emerging technologies like generative AI, federated analytics, autonomous data fabrics, and edge intelligence. The result of this research makes important contributions to enterprise information systems, cloud computing, cyber security governance, and intelligent data engineering. The proposed architecture provides a scalable and flexible platform for future enterprise environments aiming at achieving intelligent, secure, and interoperable data management across domains. The study's theoretical and practical value lies in its development of a holistic AI-powered model that can be used to inform digital businesses in an increasingly complex data-rich environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".