Blockchain-Integrated Zero Trust Framework

• Blockchain-integrated zero trust framework is a security model that ensures no entity is trusted by default and continuously verifies access using blockchain for tamper-resistant record keeping.
• It employs an immutable, decentralized ledger to securely store identity states, policy evidence, and audit logs across diverse applications such as vendor management, FinTech, IoT, and vehicular networks.
• Architectural variations range from smart contract-enabled permissioned blockchains to localized trust engines, addressing performance trade-offs, scalability, and integration challenges.

A blockchain-integrated zero trust framework is a security architecture in which no entity is trusted by default and trust is continuously re-evaluated, while blockchain provides an immutable, decentralized substrate for identity state, policy state, auditability, reputation, or consensus. Recent formulations apply this pattern to vendor ecosystems, FinTech, vehicular fog computing, IoT, edge-supported smart city systems, decentralized clinical trials, UAV networks, DApps, and distributed energy trading. Across these settings, blockchain is not used as a replacement for access control or monitoring; rather, it appears as a tamper-resistant control plane, an evidence layer, a policy traceability layer, or a distributed trust record, and in some systems it is only a comparison point or an architectural extension rather than part of the deployed authentication path (Gupta et al., 2024, Singh et al., 26 Jul 2025, Ahmad, 21 May 2025).

1. Conceptual definition and scope

Zero trust is consistently framed as “Never Trust, Always Verify” or “never trust, always verify”, with continuous verification, least privilege, explicit policy enforcement, and the assumption that compromise may already exist (Singh et al., 26 Jul 2025, Bicer et al., 2023). In blockchain-integrated variants, the distinctive addition is that trust-relevant events are made immutable, distributed, and auditable. One formulation states that blockchain becomes a tamper-resistant control plane for trust decisions, while another maps blockchain directly to the Policy Engine (PE), Policy Enforcement Point (PEP), and policy storage (Gupta et al., 2024, Singh et al., 26 Jul 2025).

The term does not denote a single protocol family. In some systems, blockchain stores vendor identity, submitted documents, compliance checks, vulnerability scan results, and incident response actions; in others it stores request histories, UAV identity and reputation, or device identifiers, public keys, PUF challenge values, authentication records, and transaction state (Gupta et al., 2024, Xie et al., 2024, Commey et al., 2024). In oracle and DApp settings, blockchain coordinates selection, truth inference, incentives, or decentralized trust middleware rather than endpoint login alone (Gigli et al., 2024, Truong et al., 2021).

A recurrent boundary condition is that blockchain alone is insufficient. One DApp trust study explicitly argues that blockchain and smart contracts ensure on-chain data security, autonomy, and integrity of business logic, but do not guarantee the quality of service of DApps, which depends on off-chain performance. That work therefore introduces a decentralized trust framework on top of blockchain. A related review of endpoint-centric ZTA similarly argues that blockchain strengthens the “verify” part of zero trust by making intrusion evidence, endpoint context, and shared alerts tamper-resistant, immutable, and consensus-verified (Truong et al., 2021, Alevizos et al., 2021).

2. Architectural forms

Recent work exhibits several recurring architectural forms. A vendor-risk architecture combines a Baseline governance layer, a Blockchain trust layer, and an Operational security layer. The governance layer is grounded in NIST Cybersecurity Framework (CSF), SP 800-53 Rev. 5, and SP 800-161 Rev. 1; the blockchain trust layer records vendor identity and compliance evidence; the operational layer applies MFA, role-based access control, patch management, encryption, continuous monitoring, AWS IAM, AWS Shield, and CloudTrail (Gupta et al., 2024).

FinTech work presents a stronger blockchain coupling. Ethereum smart contracts, a DApp frontend, and Web3.js / Ethers.js communication are used so that access requests are mediated through smart contracts and DApp logic, with blockchain functioning simultaneously as PE, PEP, and policy storage. The stated novelty is not merely that blockchain is present, but that it is used as a Zero Trust policy engine, enforcement point, and policy store (Singh et al., 26 Jul 2025).

Permissioned blockchain architectures are prominent in IoT, edge, and clinical settings. Hyperledger Fabric is used as a permissioned blockchain backbone, with MSP support, certificate authorities, peers, orderers, chaincode, and an immutable, tamper-evident ledger (Commey et al., 2024). In edge-supported IoT, the zero-trust chain includes PEP, Policy Administrator, multiple Policy Engines, Authentication Service, Operating System Vulnerability service, Parameter Checker, Blockchain Peer Monitoring, Persistence Managers, and Blockchain Peer Logging, with only dedicated bridge components interacting with Hyperledger Fabric (Bicer et al., 2023). In decentralized clinical trials, z-TAB uses a permissioned blockchain with private channels, chaincode, ABAC, PEP, PDP, PE, PESC, PDPSC, and TESC, so that every transaction is authenticated, policy-checked, and consensus-validated before admission to the shared ledger (Peepliwall et al., 2024).

Not all architectures that inform blockchain-integrated zero trust actually deploy blockchain in the critical path. The Zero-Trust Mobility-Aware Authentication Framework (ZTMAF) for vehicular fog computing is built as a three-tier model of vehicles, fog nodes, and a decentralized trust ledger / trust layer, but the paper explicitly states that the actual mechanism is a local trust engine rather than a full blockchain consensus system. This distinction is central to the framework’s interpretation (Ahmad, 21 May 2025).

3. Trust, identity, and verification primitives

The dominant technical motif is continuous trust computation from identity, context, behavior, and cryptographic proof. In vehicular fog computing, trust is mobility-aware and context-driven. Each vehicle has a context vector

$\mathbf{c}_i(t) = [s_i(t), l_i(t), b_i(t)],$

where $s_i(t)$ is speed, $l_i(t)$ is location, and $b_i(t)$ is recent behavior score. Trust is updated through a context-sensitive exponential forgetting filter,

$$\mathcal{T}_i(t+1) = \alpha \cdot \mathcal{T}_i(t) + (1 - \alpha) \cdot \psi(\mathbf{c}_i(t)),$$

and authentication is allowed only if trust exceeds a threshold $\theta$, expressed as

$$P_{accept} = \mathbb{P}(\mathcal{T}_i(t) > \theta).$$

This is a canonical example of continuous, context-aware zero-trust admission control (Ahmad, 21 May 2025).

Other systems root trust in hardware or proof systems. In a Hyperledger Fabric IoT framework, PUFs provide strong, hardware-rooted device identity via $PUF: C \rightarrow R$, and the device identifier is derived as

$ID_i \leftarrow \text{Hash}(pk_i || R_i).$

Authentication and transaction validation are then carried by Groth16 zk-SNARKs, so that identity and transaction integrity can be proven without revealing sensitive data (Commey et al., 2024). In zk-IoT, trust is attached to computation rather than to device identity alone: zk-Devices generate proofs that intended firmware executed correctly on intended inputs, service contracts verify those proofs, and blockchain stores ZKPs and contract logic as the trust foundation (Ramezan et al., 2024).

Blockchain-integrated zero trust in FinTech relies on a different primitive stack: Ethereum smart contracts enforce MFA, RBAC, and JIT access. MFA includes email, password, device information, MAC address, browser details, OS type/version, IP address, geolocation, and a SHA-256 checksum of device information; RBAC is assigned on-chain; and JIT access constrains execution within a time window (Singh et al., 26 Jul 2025). In DApp trust middleware, pairwise trust is made explicit rather than implicit:

$$Trust(A, B) = w_1 Rep(B) + w_2 Exp(A, B), \qquad w_1 + w_2 = 1.$$

That formulation is notable because it treats trust as a continuously updated function of direct experience and community reputation rather than as a static credential (Truong et al., 2021).

Open participation systems push the same logic further. ZONIA assumes that No actor is trusted by default; oracles, indexers, and producers are all assumed potentially faulty or malicious, and trust emerges from open participation with staking, randomized selection, multi-source redundancy, deterministic truth inference, reputation updates, and blacklisting when needed (Gigli et al., 2024).

4. Control flows and enforcement logic

Blockchain-integrated zero trust is operationalized through explicit, staged workflows. In vendor assurance, the sequence is: vendor onboarding and identity verification; submission of security policies, certifications, audit reports, and compliance documents; hashing and blockchain storage of those documents; smart contract-based validation against predefined standards such as NIST 800-53 controls; vulnerability scans, penetration testing, and security posture evaluation; access control verification for MFA and RBAC; continuous monitoring; and incident response with auditable actions and timestamps (Gupta et al., 2024).

In FinTech, the DApp workflow is similarly explicit. Registration stores user data and a SHA-256 checksum of device information on-chain; role assignment is performed through AssignRole; login recomputes device data and checks password, device information, MAC, and sender address; and sensitive actions are gated by startExecution, isOvertime, and terminateExecution for JIT access. The architecture substitutes blockchain into a NIST-style request path in which requests go to the PDP, policy logic is held by smart contracts, policies are immutable on-chain, and results are auditable (Singh et al., 26 Jul 2025).

Vehicular zero trust emphasizes a low-latency admission path. A vehicle forms an authentication request

$s_i(t)$0

signs it, sends it to a fog node, and the fog node verifies the signature, decodes context, updates trust, checks the threshold, derives a session key $s_i(t)$1, and issues an encrypted session token; otherwise it requests a stronger challenge or denies access. The paper specifies HMAC-SHA256 as the PRF algorithm in simulation (Ahmad, 21 May 2025).

In edge IoT, each request passes through Client or Analyser, PEP, Policy Administrator, multiple Policy Engines, and blockchain-linked monitors. The Policy Administrator applies a majority consensus over Policy Engines in a PBFT-like majority voting style approach. Policy Engines execute the Trust Algorithm using identity checks, OS vulnerability checks, parameter checks, and request history for suspicious activity retrieved through BC-P-MON, while BC-P-LOG persists requests and decisions immutably (Bicer et al., 2023). In decentralized clinical trials, data from wearables are hashed, evaluated through PEP → PDP → policy evaluation, checked against private-channel permissions and T-PBFT consensus, and then appended to the ledger or offloaded to IPFS with hashes on-chain (Peepliwall et al., 2024).

A plausible implication is that blockchain-integrated zero trust often separates fast local enforcement from slower global coordination. This reading is explicit in the vehicular literature, where the suggested hybrid model uses ZTMAF-like local logic for real-time admission control and blockchain for slower, cross-domain, auditable, tamper-resistant recording of long-term reputation or policy events (Ahmad, 21 May 2025).

5. Representative domains and empirical findings

Evaluation results vary sharply by domain, but they clarify the practical trade space. In vehicular fog computing, ZTMAF keeps authentication latency below 200 ms up to 400 vehicles, session success rate remains above 95% even under high mobility, CPU overhead stays under 25k CPU cycles per authentication attempt, attack detection achieves 97% for spoofing and replay, compliant vehicles’ trust stabilizes after about 50 seconds, and performance remains consistent up to 500 vehicles. Against baselines, the paper reports 21% lower latency than blockchain-based systems and 35% lower CPU cycles than PKI (Ahmad, 21 May 2025).

In third-party vendor risk management, a case study on iHealth’s transition to AWS Cloud reports baseline vulnerabilities of 30, post-control vulnerabilities of 10, a 67% reduction, incident response time dropping from 48 hours to 12 hours, and a 75% improvement. The framework attributes these gains to immutable evidence, smart-contract workflow automation, continuous monitoring, and zero trust controls (Gupta et al., 2024).

FinTech results make the security-performance trade-off explicit. The blockchain-based DApp framework reports latency of 74.0 ms versus 49.33 ms for a traditional web application and throughput of 30.77 requests/sec versus 50.0 requests/sec, but it is presented as eliminating single points of failure and providing tamper-proof audit trails on a 200-node simulated network (Singh et al., 26 Jul 2025). In IoT proof-carrying architectures, zk-IoT reports Groth16 prover time of about 694 ms, proof reading from blockchain of about 5,078 ms, and verification time of about 19 ms, with blockchain storage and retrieval identified as the bottleneck rather than proof verification (Ramezan et al., 2024).

At the edge, the full ZTA-BC system adds only about +1.05x or roughly +5% over the non-blockchain ZTA variant, because blockchain logging is asynchronous and history queries in Hyperledger Fabric do not require consensus. The measured bottleneck is validation consensus across Policy Engines rather than blockchain itself (Bicer et al., 2023). In oracle-based IoT, ZONIA reports end-to-end latency from about 9 s under low load to about 15 s under high load, while its reputation mechanism maintains high reliability even when 40% of nodes exhibit malicious behavior (Gigli et al., 2024). In low-altitude intelligent networks, a blockchain-enabled zero-trust routing framework reduces the average E2E delay by 59\% and improves the TSR by 29\% on average compared to benchmarks, while also enabling faster and more robust identification of low-trust UAVs (Jia et al., 27 Feb 2026).

These results do not imply a single universal performance profile. They instead show that blockchain-integrated zero trust ranges from sub-100 ms policy enforcement in FinTech prototypes, to sub-200 ms vehicular admission, to multi-second oracle or consensus workflows, depending on whether blockchain participates directly in real-time control or chiefly anchors evidence and coordination.

6. Misconceptions, limitations, and research directions

A frequent misconception is that any framework compared against blockchain is itself blockchain-based. ZTMAF explicitly rejects that interpretation: blockchain is not actually implemented in the proposed ZTMAF protocol; it is used as a related-work benchmark and as a reference point for why existing decentralized trust systems are too costly for real-time vehicular fog authentication. The framework is built around local fog-node verification, trust scores, lightweight cryptographic primitives, and no consensus protocol (Ahmad, 21 May 2025).

A second misconception is that blockchain solves trust by itself. Vendor-risk, DApp, and endpoint-ZTA work all reject that view. Blockchain provides immutable auditability, consensus-backed evidence, and distributed trust recording, but policy validation, behavioral analysis, vulnerability assessment, access control, and incident handling still require higher-layer logic (Gupta et al., 2024, Truong et al., 2021, Alevizos et al., 2021). SecureBank makes the same point indirectly: its financially aware, context-adaptive zero trust architecture is not blockchain-specific, yet several of its components—continuous trust recalculation, context-aware identity scoring, deterministic policy actions, business-aligned segmentation, and audit/log provenance—map naturally to a permissioned blockchain-backed extension. This suggests that blockchain integration is often an architectural extension rather than the essence of the trust model (Biao, 29 Dec 2025).

Scalability, latency, and complexity remain the central technical limits. Ethereum-based FinTech work identifies scalability overhead, gas fees, and a partial encryption gap, and proposes Layer-2 optimizations, alternative platforms such as Hyperledger Fabric and Polkadot, stronger communication security through ZKPs and TLS, and AI-driven anomaly detection as future work (Singh et al., 26 Jul 2025). Hyperledger- and Fabric-based IoT systems note that experiments were off-chain or prototype-level and still need live deployment, embedded-device validation, or fuller performance studies (Commey et al., 2024, Peepliwall et al., 2024). Survey work on Zero-Trust Foundation Models identifies scalability, secure orchestration, dynamic trust calibration, interoperable policy federation, and interpretable threat attribution as open research challenges for blockchain-based identity management, micro-segmentation, TEEs, and federated learning in adversarial IoT (Li et al., 26 May 2025). A robust decentralized ZTA based on blockchain and federated learning adds future directions in differential privacy, post-quantum cryptographic methods, Dirichlet process anomaly detection, and quantum-resilient consensus (Pokhrel et al., 2024).

Taken together, the literature suggests a mature design principle rather than a closed blueprint: keep real-time verification lightweight and contextual, use blockchain where immutable evidence, decentralized policy state, or cross-domain trust anchoring are required, and avoid forcing latency-sensitive admission control to pay unnecessary consensus cost.