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All Journal The Eastasouth Journal of Information System and Computer Science
Celeste Yvonne Ramirez
BBW University of Applied Sciences, Berlin, Germany
Computer Science & IT

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Risk-Aware Federated Cyber Defense for Cloud–Edge Systems Through Privacy, Network Resilience, and Accountable Response Celeste Yvonne Ramirez
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 02 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v4i02.1267

Abstract

Cloud services, edge devices, and organizational information systems expose security teams to a shared problem: a locally accurate detector can still produce a poor operational decision when its recommendations ignore service dependencies, tenant boundaries, privacy constraints, or the consequences of interruption. This paper develops a design-science framework for coordinating these concerns within a single decision process. A purposive corpus of 55 supplied publications informs the requirements; it is treated as a bounded design resource rather than a systematic or exhaustive evidence base. The proposed architecture connects tenant-local telemetry, context-sensitive threat estimation, privacy-preserving collaborative learning, dependency-aware response selection, and verifiable governance records. Its central contribution is a constrained decision formulation that minimizes expected attack loss and response-induced disruption while enforcing explicit limits on privacy expenditure, latency, and service availability. Analytical examination establishes three conditional properties: independent event thresholds can violate shared service constraints, selective abstention can reduce decision loss when review is sufficiently informative, and federated participation does not by itself establish privacy. A transparent worked example demonstrates how a capacity constraint changes which alerts receive an automated response; the example is illustrative and does not represent measured performance. A prospective evaluation protocol specifies chronological and tenant-separated testing, rare-attack assessment, poisoning experiments, privacy accounting, recovery analysis, and paired comparisons against operational baselines. No experiments or production deployments are reported. The framework provides an implementable research specification and testable propositions for examining whether coordinated cyber defense improves operational outcomes beyond predictive accuracy alone.
From Risk Scores to Decision Value: An Evidence-Governed Business Analytics Framework Under Capacity Constraints Celeste Yvonne Ramirez
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 02 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v4i02.1312

Abstract

Organizations increasingly use machine learning to identify customers, transactions, suppliers, and projects that require attention. Yet a high predicted risk does not establish that an intervention will help, that its benefit exceeds its cost, or that the underlying evidence permits action. This paper develops an analytical framework that connects prediction, intervention value, evidence verification, and limited managerial capacity. The framework combines temporally governed data, calibrated risk estimates, bounded intervention effects, constrained optimization, and selective human review. Its central object is a decision record linking each proposed action to the information available at the decision time, the assumptions behind its expected value, and the authority required for execution. Analytical propositions establish when risk ranking agrees with value ranking, how uncertainty changes the intervention threshold, and when additional review has positive expected value. A fully specified six-case worked example demonstrates the distinction. With capacity for two interventions, risk ranking produces an assumed expected net value of −1 unit, whereas value-based selection produces 54 units. A common uncertainty allowance of 0.10 reduces the optimized lower-bound value to 34 units. These quantities are arithmetic consequences of stated assumptions, not estimates from an organizational experiment. The contribution is an auditable decision formulation and a prospective evaluation protocol that separates predictive performance from intervention effectiveness. The framework offers testable design principles for business analytics while identifying the causal, temporal, and organizational evidence required before deployment.
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