Cloud-scale security platforms generate a continuous stream of vulnerability, misconfiguration, and threat-detection findings that vary sharply in urgency, yet many delivery pipelines still process every finding through the same stages, at the same speed, regardless of severity. This uniform treatment produces head-of-line blocking, in which a small number of genuinely urgent findings sit behind a much larger volume of routine ones, delaying delivery precisely when delay is least tolerable. This article develops a priority-aware data delivery architecture for security-critical distributed platforms, drawing on real-time systems scheduling theory, quality-of-service networking, and risk-based vulnerability management to design a system that routes findings through differentiated processing paths matched to their assessed risk. The architecture combines a multi-signal classification engine, which integrates intrinsic severity scoring, exploitation-probability modelling, confirmed-exploitation catalogues, and asset context, with a multi-lane delivery layer that provisions isolated compute, queue, and storage resources for each priority tier, a progressive-enrichment mechanism that trades initial completeness for delivery speed on the most urgent findings, and priority-aware anti-entropy reconciliation that preserves eventual delivery completeness across every tier. The analysis situates this design against published evidence on exploitation timing, regulatory remediation mandates, and the operational cost of undifferentiated alerting, and proposes tier-specific service level objectives spanning delivery latency, delivery completeness, and enrichment coverage. The discussion examines the central trade-off the architecture introduces between enrichment depth and delivery latency, and the conditions under which differentiated delivery reduces exposure windows without weakening completeness guarantees for lower-priority findings. The article concludes that priority-aware delivery is a structural requirement for security platforms at cloud scale, particularly as regulation moves toward risk-tiered remediation deadlines and exploitation timelines continue to compress
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Rahul Kizhakkepachilamakol (2026) studied this question.
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