Zero Standing Privilege (ZSP)

• Zero Standing Privilege (ZSP) is a security design principle that provides transient, just-in-time access rights to eliminate permanent permissions and constrain the attack surface.
• It employs techniques like peer group analysis, container partitioning, and formal verification to dynamically assign and revoke privileges based on context.
• ZSP underpins advanced architectures such as Zero Trust and cloud-native environments, enhancing auditability, privacy, and resistance to credential theft.

Zero Standing Privilege (ZSP) is a security design principle mandating that users, devices, processes, and applications are granted no permanent, static access rights. Instead, access privileges are issued just-in-time (JIT), dynamically and temporarily, in accordance with least-privilege requirements, and revoked immediately after use or when no longer necessary. ZSP aims to eliminate persistent access vectors, tightly constrain the attack surface, and simplify auditability across modern distributed systems. It is the operational realization of least-privilege in rapidly evolving environments, and has become a fundamental goal underpinning advanced cybersecurity architectures such as Zero Trust Architecture (ZTA), cloud-native data enclaves, container ecosystems, and privacy-preserving access control systems.

1. Conceptual Foundations and Evolution

ZSP originated as an evolution of the classic principle of least privilege, which mandates that every subject—user, process, or system—should have only the access necessary for its legitimate purpose. In traditional security models, authenticated users or services often retain broad “standing” privileges for the duration of their session, or even indefinitely, leading to a substantial, exploitable attack surface (Gambo et al., 7 Feb 2025). ZSP fundamentally reconfigures this paradigm by refusing all persistent access and requiring that every permission be transient and contextually justified.

ZSP gained prominence with the emergence of Zero Trust concepts, notably through publications and standards such as NIST SP 800-207 and the Federal Zero Trust Strategy. In these models, least privilege is not only statically enforced, but assessed continuously and contextually (“never trust, always verify”), catalyzing the transition to dynamic, ephemeral privilege assignment (Gambo et al., 7 Feb 2025).

2. Methodologies for Enforcing Zero Standing Privilege

a. Peer Group Analysis

ZSP enforcement in application marketplaces can be operationalized by peer group analysis (Jana et al., 2015). Applications are clustered into peer groups by apparent functionality—via developer categories, recommendation systems, or learned from textual description using supervised/unsupervised techniques. Privilege usage within each group is analyzed, and applications are scored for “unexpectedness” by quantifying the rarity of each privilege used relative to their peers. High unexpectedness scores signal over-privilege, motivating removal of unnecessary standing privileges.

Algorithmically, for each application $a$ in peer group $g_a$ with privileges $P_a$, unexpectedness is computed by summing weights $W_p$ for privileges $p$ present in $P_a$ but rare in $g_a$:

• $X_{gp} = \frac{N_{gp}}{N_g}$
• If $X_{gp} < \text{threshold}$, $W_p$ is added to unexpectedness.

Market operators may incentivize developers to remove superfluous privileges and provide user-facing privilege ratings, aligning software distribution with ZSP.

b. Container Partitioning and Minimization

In containerized environments, ZSP is enforced by dynamically identifying used resources and partitioning monolithic containers into isolated, narrowly scoped “slim containers” (Rastogi et al., 2016). Tools such as Cimplifier analyze system call traces to infer minimal file, socket, and IPC requirements for each component, then partition containers such that no component has access to resources outside its operational needs. A directed call graph guides merging of executables into containers, honoring user-defined positive/negative constraints. Partitioned containers interact via Remote Process Execution (RPE): a stub relays execution to the appropriate container with a tightly scoped privilege set. This containment ensures the absence of standing privilege outside the execution window of each micro-component.

c. Formal Verification at Binary Level

Microkernel privilege separation can be formally proven at the executable level through automated invariant inference (Nicole et al., 2020). The system abstracts hardware, OS, and user tasks as a transition system, with privilege escalation ruled out by finding an invariant $p$ such that:

• $$p(s) \implies \text{secure}(s) \equiv \neg(\text{A–controlled}(s) \wedge \text{privileged}(s))$$,

where $p$ is inferred via static analysis augmented with automated type annotation. The methodology ensures that no attacker-controlled code can ever be in a privileged state, directly achieving ZSP for kernel code and prohibiting standing privilege for user tasks.

d. Privacy-Preserving Access Policies with Zero Knowledge

In expressive access policy languages, ZSP is interpreted as data minimization at policy enforcement points. Access policies are extended with zero-knowledge predicates (NIZK, SNARKs), allowing a user to prove satisfaction of a policy (e.g., attribute ≥ threshold) without revealing their underlying privilege or sensitive attributes (More et al., 2022). This prevents the formation of “standing” access or “surplus” credential exposure: only the necessary proof-of-compliance is revealed.

3. Architectural Realizations and Application Domains

a. Zero Trust Architecture (ZTA)

ZTA embodies ZSP by dynamically issuing privileges per transaction, evaluated against a dynamic trust score $T(S) = \sum_{i=1}^{n} w_i f_i(\text{context}_i)$, and revoking them post-use (Gambo et al., 7 Feb 2025). Contextual awareness includes device health, user behavior, location, and threat intelligence, ensuring continuous conditional access. ZTA operationalizes ZSP through ephemeral privilege grants, real-time monitoring, and automated deprovisioning.

b. Data Enclaves and Just-in-Time Data Contracts

At the data plane, ZSP is achieved via on-demand data enclaves and temporary data contracts. Upon access request, a secure enclave is instantiated containing only the requested data for a specific user, permission, and timeframe as specified in a data contract $C = \{u, D, t_\text{start}, t_\text{end}, p\}$ (Bistolfi et al., 10 Oct 2025). The enclave disconnects after data loading; queries are executed and monitored; after $t_\text{end}$, data access is revoked and the enclave is dismantled. This model prevents privilege creep, simplifies auditing, and minimizes exposure risk by eliminating persistent permissions at the data level.

Domain	ZSP Mechanism	Reference
App Ecosystems	Unexpectedness Score, Clustering	(Jana et al., 2015)
Containers	Resource Minimization, Partition	(Rastogi et al., 2016)
Kernel/Embedded	Binary-Level Invariant Analysis	(Nicole et al., 2020)
Policy Enforcement	Zero-Knowledge Proofs	(More et al., 2022)
Data/Cloud	Data Enclaves, JIT Contracts	(Bistolfi et al., 10 Oct 2025)
Zero Trust Networks	Dynamic Trust, Just-in-Time Auth	(Gambo et al., 7 Feb 2025)

c. API Security

In the context of API authorization, frameworks such as BOLAZ apply ZSP by dynamically segmenting resource access (MSG intervals) and ensuring that every API request is evaluated for the minimal permissions permitted by the system’s own data-flow logic (Wu et al., 3 Jul 2025). Static taint analysis classifies APIs, traces resource ID propagation, and enforces context-sensitive authorization boundaries to prevent privilege escalation attacks in RESTful environments.

4. Benefits, Empirical Evaluation, and Challenges

Implementing ZSP has shown measurable security benefits across domains. Peer group analysis in large app markets correlates high unexpectedness scores with policy violations and real-world suspensions (Jana et al., 2015). Automated microkernel analysis achieves high efficiency (e.g., less than 10 minutes for ∼800 instruction codebases, with only 58 lines of manual annotation) and is able to uncover subtle, real-world privilege escalation vulnerabilities (Nicole et al., 2020). BOLAZ reports recall rates of 97% for API categorization and discovers numerous previously unreported BOLA vulnerabilities in empirical studies (Wu et al., 3 Jul 2025). Cloud data enclave architectures reduce attack surfaces and prevent privilege creep, crucial for large-scale, distributed, and highly regulated environments (Bistolfi et al., 10 Oct 2025).

Transitioning to ZSP may require significant refactoring of legacy systems, introduction of orchestration layers (for enclave/user session management), and sophisticated policy engines. Cultural and operational adjustments—moving from session-based to continuous verification—along with complexity management in heterogeneous environments, are also recognized barriers (Gambo et al., 7 Feb 2025).

5. Relationship to Privacy and Data Minimization

ZSP directly supports data minimization by requiring that only the necessary attributes and privilege proofs are provided at any given interaction. In access control systems extended with zero-knowledge mechanisms, it is possible to fulfill privacy-preserving policies such that users demonstrate authorization without exposing full, persistent attribute sets (More et al., 2022). This approach is particularly relevant for self-sovereign identity and data marketplace scenarios.

6. Future Directions and Broader Impact

Ongoing research investigates integrating advanced behavioral analytics, federated anomaly detection, and cryptographic protocols to enable more adaptive and scalable ZSP enforcement (Gambo et al., 7 Feb 2025). Blockchain-based ledgers, decentralized identity, and software-defined segmentation further facilitate dynamic, context-aware privilege management. The end-goal is comprehensive, cross-domain ZSP enforcement: from infrastructure to application, from data to process, and from individual device to federated systems.

The adoption of ZSP is instrumental in responding to contemporary threats—ranging from cloud data exfiltration to privilege escalation in microservices and the over-authorization in distributed APIs. Its rigorous, just-in-time nature fundamentally reduces the impact of credential theft and minimizes post-compromise risk across the digital ecosystem, reinforcing modern zero trust security postures.