2026· International Journal of Emerging Research in Engineering and Technology· Vol 7, pp. 10-16· 0 citations
TL;DR
Combining cataloguing, versioning, deprecation planning, and security enforcement leads to better compliance, faster development, and easier access to reusable assets across the entire enterprise API landscape.
Abstract
Application Programming Interface (API) governance has become a key part of enterprise digital transformation, helping organisations manage the entire lifecycle of APIs from creation and deployment to versioning, deprecation, and eventual shutdown through clear and enforceable policies. Companies that work in multi-vendor, hybrid-cloud environments often face ongoing issues like API sprawl, inconsistent security practices, fragmented cataloguing, and the lack of consistent deprecation strategies that work across the whole organisation. A well-structured governance model helps by setting up centralised control, standard design rules, automated enforcement tools, and stage-based lifecycle management, which lowers operational risks and improves the experience for developers. Proper API cataloguing makes sure that every active interface has clear ownership, specification details, and metadata about how it is used, forming a central record that supports all future governance decisions. Versioning policies help manage multiple versions of an API during change periods, keeping older versions functional while guiding users to newer, safer versions through clear depreciation schedules and migration plans. Security measures, applied consistently to all active and outdated endpoints using API gateway controls and integration with identity and access management systems, help eliminate security risks from unmonitored legacy interfaces. In large enterprises, governance must balance overall standards with the ability of different teams to work independently, often using federated models that keep product teams aligned with company goals without limiting innovation. Automation, built into continuous integration and delivery processes through policy-as-code and automated checks, turns governance from a one-time compliance check into an ongoing, automatic control process. Combining cataloguing, versioning, deprecation planning, and security enforcement leads to better compliance, faster development, and easier access to reusable assets across the entire enterprise API landscape.
Enterprise IT organizations increasingly face challenges in delivering scalable, efficient, and
governed digital services across complex environments. ServiceNow, as a leading cloud-based IT
Service Management (ITSM) platform, offers extensive capabilities for automating workflows,
consolidating enterprise processes, and enabling data-driven decision-making. This study
proposes a conceptual and applied framework for the design, governance, and scalable delivery
of ServiceNow programs across organizations. The framework integrates strategic program
planning, modular architecture design, and best-practice governance structures to ensure
alignment with organizational objectives, regulatory compliance, and operational efficiency. Key
elements include the standardization of process workflows, role-based access controls, and
continuous performance monitoring to support iterative improvement and value realization.
Applied methodologies focus on phased program deployment, stakeholder engagement, and
change management strategies that mitigate operational risks and enhance adoption. The
framework further emphasizes scalability through reusable configuration patterns, integration
with enterprise systems, and automated orchestration of cross-functional processes. Case-based
scenarios illustrate how the framework addresses common challenges such as service delivery
bottlenecks, inconsistent process adoption, and governance gaps, while enabling measurable
performance improvements. This research contributes to both theory and practice by providing a
structured approach to ServiceNow program management, demonstrating how conceptual design
principles can be translated into practical, enterprise-scale implementations. Ultimately, the
proposed framework supports organizations in achieving resilient, adaptive, and high-performing
IT service operations, fostering sustainable growth and digital transformation. Future studies may
explore the integration of artificial intelligence, predictive analytics, and real-time operational
dashboards within the framework to further optimize service delivery and strategic decisionmaking.
Joseph Edivri· INTERNATIONAL JOURNAL OF SOC...· 0 citations
Enterprise front-end development at scale is characterized by a persistent tension between the need for organizational consistency and the demand for product-team autonomy. Left unresolved, this tension produces fragmented codebases, duplicated infrastructure decisions, inconsistent security postures, and compounding onboarding costs. Application generators address this problem by encoding architectural standards, security defaults, and delivery contracts directly into the project creation process. Rather than relying on documentation and developer discipline to propagate platform conventions, generators make correct behavior the automatic outcome of project initialization. This article examines application generators as first-class platform infrastructure across their full operational lifecycle. The scope covers scaffold design and baseline contract definition, governance and security enforcement through generated defaults, extensibility architecture that preserves product-team autonomy without sacrificing platform coherence, pipeline and observability integration, developer experience as a technical requirement, shared asset distribution, and the governance models required to prevent generator sprawl. The article further addresses the implications of AI-assisted development for generator design, arguing that embedded static analysis and dependency governance become especially critical as AI-generated code enters enterprise codebases at an increasing rate. The article establishes that generator value is not realized at project creation alone. It builds up over time through consistent delivery pipelines, less time spent on compliance remediation, faster onboarding, and measuring at the portfolio level. Sustaining that value requires treating the generator as a versioned, owned, and actively maintained platform product, governed by the same rigor applied to the applications it produces.
Anilraj Chennuru· International journal of com...· 0 citations
Purpose: Large-scale enterprise transformation programs depend on stable, well-governed environments to support development, testing, and production activities. Environment management is frequently treated as a reactive infrastructure function, resulting in scheduling conflicts, environment-related defects, and avoidable delays across program phases. This paper addresses that operational gap by presenting the Enterprise Environment Management Framework (EEMF) — a governance-driven model that repositions environment management as a strategic enterprise operations capability.Design/methodology/approach: The EEMF is derived from enterprise-scale transformation practice across multi-release programs involving Enterprise Resource Planning (ERP) systems, Software as a Service (SaaS) platforms, and hybrid cloud integration services. The framework integrates eight interconnected layers: environment strategy, landscape architecture, multi-release environment management, lifecycle governance, governance operating model, scheduling and maintenance governance, risk management, and Key Performance Indicator (KPI)-driven observability. A structured framework derivation method is used, drawing on cross-program patterns, quantitative operational outcomes, and governance maturity assessment. Findings: Application of the EEMF across large-scale ERP transformation programs yielded a 30% reduction in environment-related defects, measurable improvements in release predictability, and a significant decrease in refresh scheduling conflicts. Programs adopting the governance operating model demonstrated higher environmental stability during critical phases such as mock cutover cycles and Production Integration Testing (PIT). KPI-driven observability enabled proactive issue detection and continuous improvement across the environment estate.Originality/value: The EEMF is the first structured framework to integrate environment strategy, landscape architecture, lifecycle management, multi-release coordination, governance operating model, and KPI-driven observability into a unified enterprise operations governance model. It addresses a demonstrable gap in formal delivery methodologies, which do not provide dedicated environment management frameworks suited to complex hybrid cloud ecosystems.
Ashok Gopalakrishnan· International journal of com...· 0 citations
A lifecycle model for LLM systems is proposed that supports security analysis by structuring it around security-relevant boundaries rather than workflow optimisation, and is supported by a 12-stage LLMOps pillar and a 9-category governance pillar.
Eleftherios Batzolis, George Drosatos, V. Katsouros et al.· 0 citations
The findings are that companies that implement organized governance systems record dramatic advancement in the quality of data, compliance rates, and the accuracy in analytics, and the need to incorporate governance frameworks in enterprise analytics strategies to promote sustainable data-driven change is highlighted.
Jessica Rachel Brown· International Journal of App...· 0 citations
Digitalization and regulatory compliance pose substantial challenges to companies, requiring adjustments to operations and business processes. Smooth transitions can be facilitated by analyzing discrepancies between current and target processes, enabling the identification of necessary organizational changes. Based on these insights, change managers can develop action plans to support effective implementation and ensure return on investment. Although scholars emphasize the importance of data-driven evaluation in change management (CM) and recognize the value of information embedded in business process models, the literature lacks systematic methods for extracting and integrating such information, particularly from text-based sources. In collaboration with industrial partners, we developed a method to address this gap. Our approach integrates semantic business process management, text analytics, and CM to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations. The method also resolves terminological inconsistencies across heterogeneous sources. This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers. To demonstrate applicability, we implemented a proof of concept in an industrial environment to validate process adherence against natural language documents such as industry standards
Domonkos Gáspár, Ildikó Szabó, Katalin Ternai et al.· Journal of Industrial Integr...· 0 citations