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Zero Trust Microsegmentation Guidance

An enhanced security strategy for modern IT and OT environments

Open Until: 06/10/2026

Zero Trust

Zero Trust Microsegmentation Guidance
Microsegmentation is a foundational Zero Trust strategy that strengthens security by enforcing explicit, fine-grained communication controls between systems, reducing attack paths, and limiting lateral movement. This paper explores the evolving role of microsegmentation as a practical enabler of Zero Trust across IT, OT, IoT, and IIoT environments. With the growing complexity and interconnectivity of modern systems, particularly in critical infrastructure, the ability to apply fine-grained controls at the network, application, process, and user levels has become increasingly essential. Microsegmentation supports threat containment and breach isolation, while also enhancing regulatory compliance and operational resilience. This paper provides a technical and strategic roadmap for implementing microsegmentation using multiple enforcement models, analyzes deployment trade-offs, and outlines maturity levels to guide adoption. The discussion is grounded in real-world applications across hybrid environments, including securing industrial control systems and dynamic cloud-native architectures. The paper also considers emerging agentic AI environments, where agents, tools, APIs, models, and data services require explicit communication boundaries, governed egress, and continuous validation to avoid recreating broad implicit trust paths.

Best For IconBest For:
Primary:  Business System and Process Owners, CISOs, Security Architects, Security Engineers, Network Administrators, Network Engineers, Cloud Security Engineers, DevSecOps Engineers, OT/ICS Security Engineers.
Secondary:  Compliance Officers, Risk Managers, Security Analysts, IT Decision-Makers, Application Developers (with a security integration focus), IoT/IIoT Security Specialists and Developers, and Data Scientists (involved in security analytics or network monitoring).

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By participating in this peer review, you acknowledge and agree to the following:

  • Your name will be included as a reviewer only if you provide substantive feedback (e.g., content, clarity, accuracy). Feedback limited to grammar, syntax, or formatting will not qualify for acknowledgement.
  • CSA's authors will have final discretion over which suggestions are incorporated into the document. Not all feedback will be implemented.
  • You will not plagiarize or submit unmodified AI-generated text. If using AI-generated content, you must apply your expertise to refine, reformat, or integrate it meaningfully into the document.
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Open Until: 06/10/2026

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