Journal of Engineering Sciences

The rapid adoption of cloud-native applications, microservices, hybrid cloud platforms, mobile systems, Internet of Things environments, artificial intelligence services, and distributed enterprise applications has significantly increased organizational dependence on application programming interfaces. APIs provide standardized mechanisms for communication among internal applications, external partners, cloud services, data platforms, machine learning systems, and customer-facing applications. However, conventional perimeterbased security models are increasingly inadequate because modern enterprise services operate across dynamic network boundaries, multiple identity domains, heterogeneous infrastructure, and continuously changing workloads. Attackers can exploit compromised credentials, excessive privileges, insecure tokens, weak authorization, vulnerable endpoints, service impersonation, lateral movement, API abuse, data leakage, and communication manipulation. This paper proposes a Zero-Trust API Security Architecture for protecting distributed enterprise services. The proposed framework applies the principle of never trust implicitly and continuously verify every request, identity, device, workload, service, token, and access context. The architecture integrates identity-centric authentication, workload identity, mutual transport authentication, token validation, attribute-based authorization, least-privilege access, micro-segmentation, API gateways, service mesh controls, secrets management, behavioral analytics, risk scoring, continuous verification, policy enforcement, anomaly detection, secure API lifecycle management, and automated incident response. A layered methodology is introduced to discover APIs, classify service sensitivity, establish trusted identities, validate contextual attributes, evaluate request risk, enforce dynamic access decisions, inspect communication behavior, detect anomalies, and continuously update security posture. Machine learning models including Random Forest, Gradient Boosting, Artificial Neural Networks, and temporal analytics are incorporated for API abuse detection and adaptive risk assessment. A representative prototype-oriented evaluation indicates that the proposed architecture can improve unauthorized request detection, reduce lateral movement opportunities, strengthen credential protection, decrease excessive access, and provide faster response to anomalous API behavior compared with static perimeter-oriented security. The architecture also incorporates production-scale machine learning governance, secure service integration, cyber-risk modeling, cloud-native sustainability, and enterprise observability. The study demonstrates that Zero Trust provides a scalable security foundation for protecting distributed enterprise API ecosystems where network location alone cannot be treated as evidence of trust.

Keywords :

Author : B. Bhanu Sree

Title : ZERO-TRUST API SECURITY ARCHITECTURE FOR PROTECTING DISTRIBUTED ENTERPRISE SERVICES

Volume/Issue : 2026;01(01)

Page No : 1-13

The rapid adoption of cloud-native architectures, microservices, and API-driven applications has significantly increased the exposure of sensitive credentials, tokens, and cryptographic keys within enterprise environments. Traditional approaches to secrets management often rely on static credentials and manual rotation processes, creating security vulnerabilities that can be exploited by malicious actors. This paper presents an adaptive secrets management and runtime security enforcement framework designed to enhance the protection of modern enterprise APIs. The proposed framework integrates automated secret discovery, dynamic credential rotation, policy-driven access control, and real-time runtime monitoring to mitigate risks associated with secret leakage, unauthorized access, and API abuse. By leveraging continuous security validation and behavioral analytics, the system detects anomalous activities and enforces security policies during application execution. The framework also supports seamless integration with cloud platforms, containerized workloads, and DevSecOps pipelines, enabling organizations to maintain security without compromising operational efficiency. A comprehensive analysis of contemporary API security challenges demonstrates that adaptive secrets management combined with runtime protection significantly improves resilience against credential compromise, insider threats, and sophisticated cyberattacks. The proposed approach contributes to the development of secure, scalable, and compliant API ecosystems capable of addressing evolving enterprise security requirements

Keywords :

Author : P. Madhuri

Title : ADAPTIVE SECRETS MANAGEMENT AND RUNTIME SECURITY ENFORCEMENT FOR MODERN ENTERPRISE APIS

Volume/Issue : 2026;01(01)

Page No : 14-26

Contrast-induced nephropathy (CIN) is a significant cause of acute kidney injury following percutaneous coronary intervention (PCI) and remains a major concern in cardiovascular care. The occurrence of CIN is associated with prolonged hospitalization, increased healthcare costs, and adverse clinical outcomes. This prospective study was conducted to evaluate the incidence of CIN and identify the major risk factors contributing to its development among patients undergoing PCI. A total of [sample size] patients scheduled for PCI were enrolled and monitored throughout the study period. Baseline demographic data, clinical characteristics, renal function parameters, comorbidities, and procedural details were recorded. CIN was defined as an increase in serum creatinine of at least 25% or 0.5 mg/dL from baseline within 48–72 hours following contrast administration. The incidence of CIN was determined, and potential risk factors were analyzed using appropriate statistical methods. The study found that CIN occurred in [percentage]% of patients undergoing PCI. Advanced age, diabetes mellitus, pre-existing renal dysfunction, hypertension, and higher volumes of contrast media were identified as significant predictors of CIN. Patients with reduced estimated glomerular filtration rate (eGFR) demonstrated a markedly higher susceptibility to renal impairment after contrast exposure. Furthermore, preventive strategies such as adequate hydration and minimization of contrast volume were associated with a lower incidence of CIN. The findings emphasize the importance of early risk stratification and implementation of preventive measures in high-risk patients. Timely identification and management of modifiable risk factors can significantly reduce the occurrence of CIN and improve clinical outcomes in patients undergoing PCI. Therefore, careful monitoring of renal function and adherence to evidence-based preventive protocols are essential components of patient care in interventional cardiology.

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Author : B. Anusha

Title : A PROSPECTIVE STUDY ON THE INCIDENCE AND RISK FACTORS OF CONTRAST-INDUCED NEPHROPATHY IN PATIENTS UNDERGOING PCI

Volume/Issue : 2026;01(01)

Page No : 27-34