What is Identity-Based Microsegmentation?

In the contemporary digital landscape, the traditional network perimeter has effectively dissolved. With the rise of hybrid cloud environments, remote workforces, and the proliferation of ephemeral workloads, the concept of a “trusted internal network” is no longer a viable security posture.

Cloud migrations created a world where the attack surface is fluid, and the threat of lateral movement is constant. Identity-based microsegmentation emerges as the necessary evolution of network segmentation, shifting the focus from location-based infrastructure to the intrinsic attributes of users, devices, and applications.

As organizations add more Internet of Things (IoT), operational technology (OT), and Internet of Medical Things (IoMT) devices to their networks, identity-based microsegmentation enables a zero trust architecture that minimizes data breaches and security risk in complex, distributed environments. 

What Is Identity-Based Microsegmentation?

Identity-based microsegmentation uses an asset’s definition rather than its network location to control its network access. In traditional environments, security policies are linked to IP addresses, VLANS, and subnets – constructs that break down in dynamic environments. Identity-based microsegmentation replaces these with dynamic, attribute-based policies. 

Some examples of IoT, OT, and IoMT assets that can complicate identity-based microsegmentation  include:

• A network-connected camera that streams around the clock with no one watching
• A PLC that runs its process with no user context at all
• An infusion pump that communicates with its back-end server whether or not a clinician is logged in

Generally, identity-based microsegmentation treats identity as the primary perimeter, mandating verification between any two connected entities using credentials rather than physical network location. When managing user access, these security policies look like requiring multi-factor authentication. However, connected devices are challenging as they are non-person entities often operating without any authenticated user session. 

Many IoT, OT, and IoMT devices are unable to run an authentication agent or hold a certificate. Often, the ones that can produce an identity to confirm themselves as trusted endpoints can’t describe what they are or how they should behave. 

When devices are unable to assert an identity, organizations typically use information about them, like:

• Make
• Model
• Firmware version
• Network location
• Operational or clinical function
• Data sensitivity
• Ownership
• Established communication behavior

What Is the Difference Between Identity-Based Microsegmentation and Legacy Methods?

Legacy security methods, such as hardware firewalls and VLAN-based zoning, were designed for static, on-premises data centers. Often, legacy perimeter security can lead to lateral movement once breached, while identity-based microsegmentation helps contain threats by enforcing controls at the individual asset level. 

Identity-based microsegmentation decides access using asset identity and behavior with per-device east-west traffic granularity, whose policy adapts as assets change, enabling IoT, OT, and IoMT network security since the process requires no agent. In comparison, the following legacy approaches lack either the comprehensive coverage, flexibility, or granularity:

• VLANS and subnets: Decide access by network location with zone-level granularity, creating flat trust inside the perimeter that requires manual re-architecture as devices move, and is a poor fit for IoT, OT, and IoMT devices that sprawl across zones if a compromise exposes a zone. 
• Firewall rules and Access Control Lists (ACLs): Decide access using IP addresses and ports, with primarily north-south traffic and limited east-west granularity, with rules often growing faster than the network, making it a poor fit for IoT, OT, and IoMT, as rules break when IPs change. 
• Network Access Control (NAC): Decide access using endpoint authentication with admission controls at connection for granularity, using certificate and supplicant life cycle for maintenance, making it limited in fitness for IoT, OT, and IoMT as most legacy devices are unable to authenticate. 

What Is The Difference Between Identity Segmentation and Identity-Based Microsegmentation?

While people often use identity segmentation and identity-based microsegmentation, the differences between these two terms are important to understand:

• Granularity: While identity segmentation uses zone-level grouping based on device type or location, identity-based microsegmentation takes a per-device or per-workload approach. 
• Trust model: While identity segmentation trust remains flat inside each zone so peers can communicate freely, identity-based microsegmentation eliminates implicit trust between any two assets, regardless of grouping.
• Blast radius: While identity segmentation means a compromised device can impact every peer in its zone, identity-based segmentation means that lateral movement stops at the individual asset. 
• Policy basis: While identity segmentation applies to an asset category or classification, identity-based microsegmentation applies to shared observed communication behaviors, the specific connections that a device actually needs. 

How Does Identity-Based Microsegmentation Work?

Identity-based microsegmentation relies on a continuous loop of verification, contextualization, and enforcement that can be explained using four foundational stages:

• Identity verification: Using identity management, like Active Directory or Entra ID for humans, and assigning machine identities to non-human identities to manage internal east-west traffic. 
• Contextual access policies: Incorporating real-time data points, like the devices’ health status, the time of day, the geographical location, or the sensitivity level of the data being requested to anchor policies in context and adapt to changing threat landscapes. 
• Dynamic trust calculation: Constantly re-evaluating trust to prompt termination of existing sessions or re-authentication when an identity’s behavior shifts.
• Microsegmentation based on identity: Using an abstraction layer to trigger the identity-based policy and enforce isolation at the network interface, ensuring that attackers remain trapped within a small, isolated segment and are unable to use lateral movement.

Best Practices for Implementing Identity-Based Microsegmentation with Connected Devices

Identity-based microsegmentation projects often fail when organizations lack the technologies needed to maintain the policies. By following some best practices and identifying solutions to help enforce policies, organizations can improve their security with identity-based microsegmentation that supports zero trust frameworks. 

Build a Classified Inventory Before Writing a Single Policy

Catalog every connected asset with its make, model, firmware, function, and operational role rather than just its IP and MAC address. Policies written against shallow inventory data are guesswork, and guesswork is what takes devices offline.

A solution supporting this step should provide:

• Passive, protocol-based discovery that doesn’t disrupt sensitive equipment through active scanning.
• Classification depth that captures device function and operational role, not just device type.
• Continuous inventory updates as assets are added, changed, or retired.

Map Device Communication Flows

Document each device’s real traffic, including what it connects to, which ports it uses, and which protocols and services it uses. After understanding the traffic, establish a list of expected connections. Policies built on assumed rather than observed behavior commonly cause production outages.

Look for a platform that delivers:

• Full communication mapping at the port, protocol, and service level.
• Behavioral analysis that separates normal traffic from anomalous traffic.
• East-west visibility, not just north-south.

Prioritize by Risk, Not by Subnet

Rank assets by exploitability, vulnerability severity, and operational criticality, then focus on the riskiest first. Projects stall when organizations attempt to segment an entire network at once, while risk-ordered work reduces attack surface faster, showing the project’s value sooner.

Evaluate whether a solution offers:

• Risk scoring informed by threat intelligence on active exploitation, not raw CVSS.
• Attack path visibility showing how a compromised device could reach critical systems.
• The ability to model risk-reduction impact before committing resources.

Simulate Policy Impact Before Enforcing Anything

Model each proposed policy against observed device behavior to see what it would block, then push enforcement through the firewalls, NAC, and switches already in place. Simulation catches the policy that would disrupt operations, and existing-infrastructure enforcement avoids a parallel rollout.

Key capabilities to look for:

• Policy simulation showing communication impact prior to deployment.
• Automated policy and segmentation-group recommendations rather than manual rule building.
• Integrations that enforce policy at the network layer through firewalls, NAC, and switching already deployed.

Treat Verification as Continuous, Not a Project Milestone

Monitor deployed policies for drift as devices are added, updated, or reconfigured, then re-validate regularly. Every environment change can quietly invalidate a policy, so organizations need to continuously verify segmentation policies to ensure ongoing security.

A capable solution should include:

• Ongoing policy monitoring with drift and gap alerting.
• Policy that adapts automatically as new assets appear.
• Reporting that maps segmentation posture to Zero Trust frameworks, HIPAA, NIST CSF 2.0, and other regulatory compliance requirements.

Asimily AI-Driven Segmentation for AI-Driven Threats

As attackers increasingly adopt AI-driven techniques to accelerate reconnaissance, exploitation, and lateral movement, organizations need security controls that can adapt just as quickly. Asimily’s purpose-built solution provides deep discovery and classification, vulnerability prioritization, and orchestration of segmentation, complete with Policy Auto-Recommendation, Policy Creation, Policy Simulation, Continuous Segmentation, and Policy Audit.  All of these serve the goal of creating continuous microsegmentation to improve security and performance. By using Asimily, organizations can ensure that policies remain consistent with the original security intent while being able to easily modify them over time so they become more effective as new devices are added to the network, keeping risk to a minimum.

Learn about Segmentation Orchestration from Asimily to thwart AI-driven attacks in the modern era.