Zero Trust Architecture in Semiconductor Manufacturing

A Framework for Securing Data Management, Remote Connectivity, and Analytics Across the Supply Chain

The semiconductor manufacturing supply chain has grown increasingly distributed, multi-party, and data-intensive. Traditional perimeter-based security models are no longer adequate to protect intellectual property, process data, and equipment diagnostics as they flow across fabs, outsourced semiconductor assembly and test (OSAT) facilities, original equipment manufacturers (OEMs), and multiple tiers of sub-contractors. This blog post examines Zero Trust Architecture (ZTA) as a framework for managing these risks. Drawing on operational experience across front-end, middle-end, and back-end semiconductor environments, it discusses the motivations for ZTA adoption, practical implementation approaches, emerging challenges posed by agentic machine identities, and a recommended governance roadmap for organizations at varying levels of maturity.

Introduction

The architecture of semiconductor manufacturing has changed fundamentally over the past decade. What was once a largely self-contained operation, with engineers, equipment, and data co-located within a single facility, has evolved into a globally distributed, multi-stakeholder ecosystem. A single wafer may pass through lithography at a leading-edge logic fab, back-end test at one or more OSAT facilities on a different continent, and advanced packaging at a third site, with process data and diagnostic information flowing between all of them in near real time.

This structural change has profound implications for data security. The traditional security model of the “air-gapped” fab, in which the physical perimeter of the facility served as the principal security boundary, is no longer viable. Remote equipment diagnostics, cross-site yield analytics, AI-driven predictive models, and supply chain integration with PLM and ERP systems all require live, authenticated data flows that cross organizational and geographic boundaries.

Zero Trust Architecture (ZTA) provides a conceptual and technical framework for managing this expanded attack surface. Formalized in NIST Special Publication 800-207 [1], ZTA replaces the implicit trust granted by network membership with continuous, per-request authentication and least-privilege access control applied as close to the protected resource as possible. This article discusses what ZTA means in a semiconductor manufacturing context, how it is being adopted across the industry, and where the next generation of challenges, particularly around autonomous agentic systems, requires the framework to evolve.

What is the background and Motivation for Zero Trust Architecture in Semiconductor Industry?

What are the limitations of perimeter-based security?

Perimeter security assumes that threats originate outside a defined boundary and that traffic inside the boundary can be trusted. In a traditional single-site fab, this assumption was defensible. Network access was physically restricted; equipment vendors visited in person; data rarely left the building in raw form.

None of these conditions hold in the contemporary supply chain. Remote access for OEM diagnostics, distributed fab campuses with virtualized tool access, and multi-party data sharing for yield analysis have collectively invalidated the perimeter model. Granting access to a tool or dataset simply because a user has already authenticated to the local network, or worse, because they are physically present, is no longer an acceptable risk posture.

What is the formal definition of Zero Trust Architecture?

NIST SP 800-207 defines zero trust as: “a collection of concepts and ideas designed to minimize uncertainty in enforcing accurate, least privilege per-request access decisions in information systems and services in the face of a network viewed as compromised.” A Zero Trust Architecture is the enterprise cybersecurity plan that operationalizes these concepts across component relationships, workflow planning, and access policies [1].

The core architectural model positions the Policy Decision Point and Policy Enforcement Point (PDP/PEP) as close to the protected resource as possible, shrinking the logical distance, in time, scope, and access rights, between authentication and the resource itself. The zone between the PEP and the resource is the only region granted implicit trust; all other zones are treated as untrusted.

Why is Semiconductor Manufacturing a High-Value Target for Cybersecurity Attacks?

Semiconductor intellectual property (process recipes, device characterization data, test programs, proprietary predictive models) represents an asymmetric risk profile. The economic and competitive value of this data is enormous, and the data flows required to operate efficiently at scale create numerous potential exposure points. Industry sources estimate that third-party and sub-contractor incidents accounted for at least 30% of all reported data breaches in 2025, with the proportion increasing year on year [2].

The industry’s reliance on a tiered supply chain amplifies this risk. An OEM accessing diagnostic data at a leading-edge fab may itself engage specialist sub-contractors who need access to parts of that data. A single sub-contractor may need to access systems at multiple customer sites, each with different security postures. Without a consistent, auditable framework, each of these connections represents a potential point of compromise.

Where is Zero Trust Architecture Adopted Across the Semiconductor Supply Chain?

What is the Challenge of Moving from Two-body to Multi-body Connectivity?

Remote connectivity challenges in semiconductor manufacturing have historically been characterized as “two-body” problems: a single authorized user (User A) needing access to a specific system (System X). Common examples include:

• OEM diagnostic access: equipment vendors requiring sensor and diagnostic data from production tools during installation, bring-up, and preventive maintenance cycles, particularly for leading-edge lithography and etch equipment where on-site presence is logistically impractical.
• Fabless yield monitoring: fabless companies needing near-real-time access to wafer and chip test results at OSAT facilities, including the ability to push updated test programs and probing algorithms back to the tester.

Over the past five years, these two-body use cases have evolved into multi-party problems requiring simultaneous, governed access by three or more independent organizations:

• Multi-party process optimization: an etch tool owner, the tool OEM, a chemical slurry supplier, and an external test chip provider may each need to access and contribute data: split conditions, tool diagnostics, test structure layouts, chemical lot properties, while each party’s proprietary information must remain protected from the others.
• Supply chain integration: connecting engineering data systems to MES, ERP, and financial modelling platforms raises the stakes considerably. A breach propagating from an engineering system into a financial system represents a qualitatively different category of risk, requiring stricter segmentation at the integration boundary.
• Sub-contractor chains: a fab engaging an OEM who in turn uses specialist sub-contractors for orchestration services or software development creates chains of trust extending to fourth- and fifth-party relationships, each an additional potential attack surface.

What are the Zero Trust Architecture considerations for Front-end, Middle-end and Back-end?

ZTA adoption has not been uniform across the manufacturing sequence. Front-end fabs, which tend to have the most mature IT governance and the highest sensitivity to IP leakage, have generally been earliest adopters, particularly driven by the need for OEM remote access to advanced process equipment. Middle-end and back-end operations, including OSAT facilities, have increasingly faced ZTA requirements as fabless customers impose security standards as a condition of supply chain participation.

Advanced packaging presents a particular challenge. As back-end testing migrates across multiple facilities (sometimes at the fab, sometimes at an OSAT, sometimes at specialized on-site packaging facilities) the ability to track a specific die securely throughout its journey, maintain a verifiable chain of custody on all associated data, and deploy updated predictive models to the right tester at the right moment has become a critical path item for supply chain efficiency.

What the Architecture to implement Zero Trust Architecture?

What are the Core technology and Governance Layers for Zero Trust Architecture?

ZTA is not a single product or protocol; it is a layered architecture requiring coordinated implementation across governance, network, identity, and monitoring domains. The principal layers are:

1. Identity and access management (IAM): multi-factor authentication (MFA), single sign-on (SSO), and role-based access control (RBAC) form the foundation. Authentication must be granular: an OEM engineer authenticated to access a specific tool should be permitted to read defined diagnostic directories and execute a pre-approved set of commands, but should have no access to process recipe data on the same tool.
2. Network segmentation: micro-segmentation and software-defined perimeters (SDP) isolate systems so that a compromise of one segment does not automatically propagate to adjacent resources. A breach in an engineering data system must not grant access to a connected financial system.
3. Next-generation firewalls and traffic inspection (NGFW): all inbound traffic to managed resources should be routed through monitored, policy-enforced firewalls. Ideally, no public access to internal services is exposed: users must originate from a known, authorized network and be routed through a managed gateway before they can “knock on the front door” of a protected server.
4. Endpoint detection and response (EDR): monitoring at the device level catches threats that network-level controls miss. For sub-contractors who cannot be issued dedicated hardened hardware for each customer environment, fully controlled virtual endpoints (such as Microsoft Virtual Desktop Infrastructure (VDI)) provide an equivalent level of endpoint governance without the hardware logistics.
5. Behavioral monitoring (SIEM/UEBA): Security Information and Event Management (SIEM) and User and Entity Behavior Analytics (UEBA) provide continuous verification by analyzing patterns rather than only credentials. An authorized engineer suddenly transferring unusually large data volumes from an atypical set of tools should trigger automated escalation, up to and including immediate connection termination pending review.

What are the Zero Trust Architecture Practical Design Principles?

Several design principles have emerged from operational experience that are worth emphasizing:

• Eliminate “low-barrier” assumptions: sharing via SharePoint, commercial cloud storage (Dropbox, shared AWS S3 buckets), or remote desktop tools such as TeamViewer or AnyDesk does not satisfy ZTA principles by default. These tools may offer strong encryption, but encryption is only as effective as the authentication and access governance layered around it. A persistent TeamViewer session with no time-based expiration and password-only authentication is a significant vulnerability regardless of the tool’s technical controls.
• Standardize rather than bespoke: architecting a new security solution for each new remote connection is not scalable. The cost and complexity are prohibitive, and inconsistency across solutions creates gaps. Standardizing on a managed, auditable secure access platform, one that can be extended to new sites and new parties without rebuilding from scratch, is highly preferable.
• Apply least privilege at the resource level: access permissions should be defined at the granularity of specific directories, commands, or data fields, not at the level of the tool or the network segment. Separating process recipe access from diagnostic data access for OEM engineers is a canonical example of this principle.
• Treat sub-contractors as first-class principals: sub-contractors and partners should be subject to the same ZTA controls as direct employees, not a relaxed subset. In the past, this was operationally difficult; virtual endpoint technologies now make it tractable.

What is the impact of Data Granularity and Sensitive Field Masking on Zero Trust Architecture?

As predictive models and LLM-based analytics are incorporated into manufacturing workflows, the granularity of data access controls must increase correspondingly. Encrypting an entire STDF test data file is insufficient when an analytics pipeline needs access to test bin data but must not ingest cryptographic keys, fuse patterns, or other fields that would constitute IP exposure. The next generation of ZTA implementations in manufacturing will require field-level masking capabilities, allowing sensitive sub-fields to be redacted or obfuscated before data is passed to internal analytics teams or external partners, while preserving the utility of the non-sensitive data.

What are the Standards, Certifications and Governance Framework for Zero Trust Architecture?

Are there unified Zero Trust Architecture Audit Standard?

There is currently no internationally auditable certification standard specifically for Zero Trust Architecture. NIST SP 800-207 [1] and CISA’s Zero Trust Maturity Model Version 2.0 provide detailed guidance, but neither produces an independent third-party certificate that a buying organization can rely on as evidence of ZTA compliance. Historically, this has led each organization to develop its own security questionnaire process for evaluating vendors and partners, a process that is resource-intensive, inconsistent, and difficult to maintain as vendor risk profiles evolve.

The limitations of static, custom security questionnaires are well documented: they are point-in-time assessments that do not capture ongoing risk; they are often generic and not tuned to semiconductor-specific threat models; their criteria for pass/fail are typically opaque; and vendor responses cannot be independently verified without significant effort.

How to Leverage Existing Certification Frameworks for Zero Trust Architecture?

In the absence of a ZTA-specific standard, several established certification frameworks provide meaningful proxies. Organizations should prioritize vendors holding current certifications from independent, accredited bodies:

• ISO/IEC 27001:2022: several Annex A controls map directly to ZTA principles. A.9 (Access Control) enforces least privilege and continuous identity verification. A.10 (Cryptography) addresses data protection in transit and at rest. A.12 (Operations Security) covers monitoring and logging for continuous verification. A.15 (Supplier Relationships) addresses third-party risk management.
• AICPA SOC 2 Type 2: provides an independent controls assessment across availability, security, processing integrity, confidentiality, and privacy. The Type 2 designation confirms that controls were operating effectively over a period of time, not merely documented at a point in time.
• FIPS 140-3: a US government standard for cryptographic module validation, providing third-party certified assurance of the cryptographic chain of security for data in transit and at rest.
• NIS 2 Directive (EU): for organizations operating in European markets or working with European fabs, NIS 2 compliance signals maturity in critical infrastructure security posture and maps well to ZTA governance requirements.
• SEMI SSCA (Standardized Semiconductor Cyber Assessment): a recently released semiconductor-specific questionnaire from SEMI’s SMCC that evaluates against the NIST Cybersecurity Risk Framework and produces a maturity ranking from 1 to 5. This is notable because it is calibrated to the specific threat model of semiconductor manufacturing, not generic SaaS or enterprise IT contexts.

What is the Recommended Governance Approach for Zero Trust Architecture?

Based on operational experience, the following layered approach to governance is recommended:

6. Require independent third-party certification (ISO 27001 and/or SOC 2 Type 2) as a baseline for any vendor or partner requiring access to sensitive manufacturing data or systems.
7. Layer semiconductor-specific assessments (SEMI SSCA) on top of generic questionnaires (such as SIGLite) to capture industry-relevant risk factors not covered by generic IT security frameworks.
8. Supplement point-in-time questionnaires with continuous monitoring mechanisms, such as security ratings services, that provide ongoing visibility into a vendor’s risk posture.
9. Ensure that sub-contractors are subject to the same certification requirements as direct vendors, not a relaxed tier.

What are the Challenges for Zero Trust Architecture associated with Agentic Access and Non-Human Identities?

What is the Scale of the Machine Identity Problem?

The most significant emerging challenge for ZTA in manufacturing data ecosystems is the rapid proliferation of autonomous agents and machine identities. Industry research indicates that non-human identities (for example: service accounts, API integrations, orchestration agents, and AI inference pipelines) already outnumber human identities by a ratio of 80 to 1 or more in large enterprises [3]. In a manufacturing context, where equipment data collectors, yield analysis pipelines, ERP integration layers, and AI-driven process control loops all operate continuously and largely autonomously, the majority of access events are already machine-generated.

NIST SP 800-207 anticipated this challenge but does not provide comprehensive guidance for the current scale and sophistication of agentic systems. The ZTA frameworks developed in the years since the standard’s publication are largely optimized for human principles and must now be extended to address machine identities as first-class actors.

What are the Authentication Alternatives to MFA for Machine Principles?

Multi-factor authentication, the cornerstone of human identity verification in ZTA, is not directly applicable to machine principles. An autonomous agent does not possess a mobile device or biometric credential. Significant changes to methods and tools are required in the identity and authorization and verification stack when autonomous agents are now the primary actors:

• Authentication: mTLS (mutual certificate-based authentication), hardened by keys stored in an HSM rather than in code or config files
• Authorization: OAuth 2.0 with short-lived, scoped tokens (often encoded as JWTs) to limit what an authenticated agent can do and for how long
• Enforcement: API gateways and brokers that apply these policies consistently at the point of access
• Continuous verification: behavioral anomaly detection to flag when an authenticated agent starts behaving outside its normal pattern

What are the Risks of Guardrail Circumvention Associated with LLM Agents?

Large language models introduce a qualitatively distinct category of risk. Unlike deterministic software agents whose behavior can be formally specified and audited, LLMs are susceptible to prompt injection attacks and may be manipulated into exceeding their intended access scope or disclosing information they have been granted permission to access but should not share. If an LLM agent is granted elevated permissions as part of its operational role, for example, access to multiple data sources to support cross-system analysis, the risk of manipulation affecting a broader attack surface is correspondingly elevated.

Mitigating this risk requires a combination of:

• Behavioral monitoring applied to agent sessions, not just human sessions, with anomaly detection tuned for the usage patterns expected of specific agents.
• Context-specific authentication that verifies not only that an agent has the right credentials but that its current request is consistent with its expected operational context.
• Granular permission scoping that limits each agent to the minimum data access required for its specific task, reducing the blast radius of a successful manipulation.
• Clear separation between the agent’s “control plane,” where permissions are administered and monitored, and the data plane in which the agent operates.

For fab and fabless IT teams who have relied on MFA as the primary security control, the transition to an environment where the majority of access events are machine-generated will require a fundamental rethinking of the risk management approach. The question is not whether to extend ZTA to agentic systems but how quickly. Relatively new standards like “ISO/IEC 42001:2023 — Information technology — Artificial intelligence — Management system” compliment traditional Information Security Management Systems and help bridge some of this gap.

What is the Zero Trust Architecture Implementation Roadmap in the Semiconductor Industry?

Organizations at different stages of ZTA maturity will have different priorities. The following staged approach is suggested as a practical framework, consistent with the maturity model structure of the SEMI SSCA:

Stage 1: Foundations (Maturity Level 1–2)

• Implement MFA and SSO across all human access paths to manufacturing systems.
• Deploy RBAC with access permissions defined at the resource level, not the network level.
• Replace ad-hoc remote desktop tools with a managed, policy-governed secure access platform.
• Establish baseline logging and monitoring for all remote access sessions.
• Achieve ISO/IEC 27001 certification and require it of primary vendors and partners

Stage 2: Segmentation and Monitoring (Maturity Level 3)

• Implement network micro-segmentation to isolate engineering, operational, and financial system domains.
• Deploy SIEM and UEBA for behavioral monitoring of human access sessions.
• Extend ZTA controls to sub-contractors via virtual endpoint technology and continuous compliance checking.
• Conduct SEMI SSCA assessments for key vendors and establish ongoing monitoring.
• Implement data field-level masking for sensitive parameters in analytics pipelines.

Stage 3: Agentic Readiness (Maturity Level 4–5)

• Audit and classify all machine identities; implement credential rotation and least-privilege scoping for all service accounts and API integrations.
• Deploy mTLS and/or HSM-backed credentials for critical service-to-service communication.
• Deploy API brokers or Gateways to protect and govern high-value API keys for integrated services
• Extend behavioral monitoring to agent sessions; establish anomaly baselines for each agent type.
• Implement context-specific authentication for LLM and AI agents operating with elevated data access.
• Establish a centralized control plane for administering and revoking agent permissions in real time.

Conclusion

Zero Trust Architecture represents a necessary evolution in how the semiconductor industry manages the security of its data and connectivity infrastructure. The shift from perimeter-based models to continuous, per-request verification is not optional: the distributed, multi-party, multi-jurisdiction nature of the modern supply chain has made the old model untenable.

Implementation is neither simple nor uniform. The complexity and cost of ZTA scale with the number of parties involved and the sensitivity of the data in play. Organizations should resist the temptation to assemble bespoke solutions for each new connection, and instead standardize on auditable, scalable platforms that can be extended to new sites and new parties without rebuilding from scratch.

The governance dimension is equally important. In the absence of a ZTA-specific audit standard, the industry should rely on established independent certifications, particularly ISO 27001 and SOC 2 Type 2, as proxies for ZTA compliance, supplement them with semiconductor-specific frameworks such as the SEMI SSCA, and extend the same requirements down the sub-contractor chain.

Looking ahead, the rise of autonomous agents and machine identities will require the industry to extend ZTA frameworks beyond their current human-centric design. The organizations that invest now in classifying machine identities, implementing appropriate non-human authentication mechanisms, and applying behavioral monitoring to agent sessions will be best positioned to manage the risk that agentic AI introduces to manufacturing data ecosystems.

Zero Trust architecture is not a destination. It is a continuous operational posture built on a single principle: assume breach, minimize blast radius, and verify everything, always.

 

References

[1]  Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture. NIST Special Publication 800-207. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-207

[2]  SecurityScorecard. (2024). Global Third-Party Cybersecurity Breaches Study. Retrieved from https://securityscorecard.com/wp-content/uploads/2024/02/Global-Third-Party-Cybersecurity-Breaches-Final-1.pdf

[3]  CyberArk. (2025). Machine Identities Outnumber Humans by More Than 80 to 1. CyberArk Press Release. Retrieved from https://www.cyberark.com/press/

[4]  CISA. (2023). Zero Trust Maturity Model, Version 2.0. Cybersecurity and Infrastructure Security Agency.

[5]  SEMI SMCC. (2025). Standardized Semiconductor Cyber Assessment (SSCA) Questionnaire. SEMI International Standards.

[6]  ISO/IEC 27001:2022. Information Security, Cybersecurity and Privacy Protection — Information Security Management Systems — Requirements. International Organisation for Standardisation.