Rethinking Multi-Tenant Cloud Security: A Zero-Trust Framework for Eliminating Lateral Movement and Identity Abuse

Abstract

Background: The rapid adoption of cloud computing has driven architectures that support multi-tenancy, elasticity, and heterogeneous workload placement. However, multi-tenant clouds introduce distinctive security, isolation, and placement challenges that affect confidentiality, integrity, and availability for hosted services. Existing literature addresses discrete elements — from placement controls in OpenStack to hypervisor-based intrusion detection and broader zero-trust prescriptions — but there is a need for an integrative framework that aligns placement mechanisms, scheduling policies, storage backends, configuration automation, and adaptive security controls under a single theoretical model. (Karataş et al., 2017; Bogorodskiy, 2019; OpenStack Documentation, 2019; Nikolai & Wang, 2014; Hariharan, 2025).

Objectives: This article develops a comprehensive, publication-ready theoretical research article that synthesizes technical controls and administrative practices into a placement-aware zero-trust model for multi-tenant cloud environments. The aim is to ground each claim in the supplied literature, to elaborate mechanisms in fine-grained detail, and to provide a roadmap for future empirical evaluation and operationalization. (Jackson et al., 2015; Bentley, 2016; Rajesh & Kumar, 2017; Manikyam & Kumar, 2017).

Methods: A conceptual-methodological synthesis is used: (1) systematic mapping of cited multi-tenancy literature; (2) functional decomposition of OpenStack placement primitives and storage backends; (3) threat surface mapping using hypervisor-focused detection techniques; and (4) construction of a layered zero-trust placement control model integrating automation and operational policies. Each methodological step draws directly on provided references and extrapolates logically, avoiding empirical claims beyond cited work. (Karataş et al., 2017; OpenStack Documentation, 2019; Nikolai & Wang, 2014; Bogorodskiy, 2019).

Results: The synthesis yields a placement-aware zero-trust architecture that defines: tenant-aware host aggregates and AZ tagging strategies; scheduler extensions for affinity/anti-affinity informed by security labels; Cinder multi-backend policies aligned with tenant isolation goals; hypervisor-based telemetry hooks for detection; and an operational automation blueprint using Ansible for policy enforcement. The result is a coherent conceptual model that maps threat vectors to placement and orchestration controls. (OpenStack Documentation, 2019; Bogorodskiy, 2019; Bentley, 2016; Jackson et al., 2015; Nikolai & Wang, 2014).

Conclusions: Integrating placement control with zero-trust principles materially strengthens isolation guarantees in multi-tenant clouds and reduces attack surface for lateral movement, co-residency attacks, and storage-based leakage. The article outlines practical implementation steps and identifies measurable research directions—particularly empirical validation of scheduler policy impact and hypervisor-detection efficacy—while acknowledging limitations due to the conceptual nature of the work. (Hariharan, 2025; Karataş et al., 2017; Nikolai & Wang, 2014).