What is Lateral Movement?

As organizations connect more devices to their networks, they expand the number of potential access points that adversaries can exploit. For years, organizations prioritized external defenses to keep attackers out and protect internal assets. As targeted threats become more sophisticated, those perimeter-focused defenses need to be augmented.

Modern attacks often incorporate lateral movement. Once attackers gain a foothold in an environment, they navigate through the network to compromise high-value assets, sensitive data, or domain controllers. This slow, methodical process is often a stealthy progression that turns a minor endpoint compromise into a high-impact security breach. A compromise of a single Internet of Things (IoT), operational technology (OT), Internet of Medical Things (IoMT), or IT device can become the springboard for unauthorized access to other assets on the same network.

By understanding what lateral movement is and how attackers traverse networks, security practitioners can implement the specific protections that mitigate the risk.

What Is Lateral Movement?

Lateral movement refers to the techniques that cyber adversaries use to travel across an environment after gaining initial access. Once a threat actor compromises a workstation, a user account, or a vulnerable IoT, OT, IoMT, or IT device, they rarely stop there. Their primary objective resides deeper within the environment.

Attackers use existing network infrastructure, legitimate administrative tools, and stolen credentials to move from one system to another. Because they appear to be trusted users or services, their lateral movement often fails to trigger security alerts.

How Lateral Movement Differs From Other Cyberattacks

Lateral movement is distinct because it is a phase of an attack lifecycle, not a single event. Other cyberattacks are categorized by their immediate action. A DDoS attack seeks to disrupt services, while lateral movement explores the network as one step toward achieving a broader objective.

Lateral movement is often a manual, human-driven process where the attacker:

• Observes the environment
• Identifies relationships between systems
• Selects the path of least resistance

Where a typical intrusion focuses on breaking in, lateral movement focuses on blending in. As a result, traditional signature-based detection systems often fail to identify the activity. Attackers hide inside systems by using native functionality such as remote login tools or administrative command-line interfaces.

What Types of Attacks Use Lateral Movement?

Lateral movement is a step in the attack chain that occurs after the initial compromise. Attackers pivot from a single compromised device toward their ultimate target, often evading intrusion detection tools.

Ransomware

Ransomware operators rarely encrypt the first device they compromise. Instead, they spend days or weeks traversing the network, identifying high-value systems, exfiltrating data, and disabling backups before deploying the malicious payload. Across critical infrastructure and enterprise environments, this traversal increasingly runs through IoT, OT, and IoMT devices because they often lack the functionality to run detection agents.

Advanced Persistent Threats (APTs)

APT groups typically breach low-value targets, then establish persistence so they can quietly pivot device by device toward sensitive data and critical assets such as intellectual property, operational control systems, or regulated data. The lateral movement phase can last months as the attacker remains undetected by tools that only monitor traditional IT endpoints.

Supply Chain Attacks

Compromising a trusted vendor’s software or firmware gives attackers an authenticated foothold inside the perimeter. From there, they move laterally to spread from that initial trusted device across the rest of the environment.

Phishing Attacks

Phishing emails only mark the beginning of an attack. Once attackers harvest credentials or deploy a payload, they use lateral movement to turn human error into an organization-wide compromise.

Pass-the-Hash and Credential-Based Attacks

Once attackers harvest user credentials from one compromised system, they reuse or relay those credentials to authenticate across the network without needing to crack passwords. When organizations fail to change IoT, OT, IoMT, or IT device default credentials or shared service accounts, attackers can use these devices as the initial attack vector.

Worms and Self-Propagating Malware

Unlike targeted intrusions, worms automate lateral movement. WannaCry, for example, spread autonomously across connected environments. Any device that can communicate with its neighbors is a potential stepping stone. IoT, OT, IoMT, and IT devices are especially susceptible because they often:

• Run legacy firmware
• Use default credentials
• Contain unpatched vulnerabilities

Why Do Attackers Use Lateral Movement?

Initial access alone rarely allows attackers to complete their objectives, because a single compromised device almost never provides access to critical systems or contains sensitive data like intellectual property or personally identifiable information (PII).

Evade Detection

Historically, internal network traffic is less scrutinized than inbound external traffic. Attackers exploit this lack of monitoring to perform reconnaissance, credential harvesting, and file transfers without triggering alerts. Moving between systems at a slow, deliberate pace that mimics legitimate user behavior makes detection and incident response more difficult, especially when attackers gain initial access through IoT, OT, IoMT, or IT devices that cannot run Endpoint Detection and Response (EDR) agents.

Privilege Escalation

Lateral movement is almost always paired with privilege escalation. Attackers rarely compromise a system that grants them immediate domain administrator rights. Instead, they gain initial access to a low-privilege workstation and harvest credentials from memory or configuration files until they reach privileged accounts. This cycle of moving and escalating is how an attacker eventually compromises critical assets.

Access to Sensitive Remote Systems

By mapping the organization’s network and identifying service accounts with rights across multiple segments, attackers can bypass protections. Lateral movement allows attackers to gain initial access to a low-security zone, such as a guest network, then move to a high-security zone, such as a financial database or operational control system.

Exploiting Vulnerabilities

After gaining an initial foothold, attackers scan the local network to identify vulnerabilities, map active services, and identify security controls. This living-off-the-land approach lets them tailor their next move. The more time they spend moving laterally, the more they learn about internal weaknesses they can exploit.

How Network Segmentation Mitigates Lateral Movement Risk

Network segmentation is the most effective technical control against lateral movement. By dividing an enterprise network into smaller, isolated zones, an organization ensures that even if an attacker compromises one device, they cannot easily reach servers, operational systems, or other sensitive segments.

Limits Attacker Movement

Even when an attacker gains a foothold, segmentation contains the blast radius. When an infected device cannot communicate with systems outside its designated zone, attackers have no path to move from one segment to another.

Isolates the Incident

Beyond limiting where attackers can go, segmentation forces them to break through additional barriers at every step. A single compromised IoT, OT, IoMT, or IT device is no longer a launchpad for reaching the entire environment.

Removes Implicit Trust

Lateral movement works because devices on the same network tend to trust each other by default. Segmentation breaks that assumption by requiring every connection to be explicitly authorized.

Enforces Least Privilege With Microsegmentation

Rather than grouping devices into broad zones, microsegmentation restricts each device to only the communication it actually needs. By limiting every device to its minimum required communication, granular policies reduce the paths attackers can take to reach their objectives.

Best Practices for Implementing Network Segmentation

In complex environments, network segmentation can be challenging. The reason most segmentation programs stall is rarely the decision to segment. It is the gap between knowing what to isolate and safely enforcing policy at scale across a changing network. The following best practices help mitigate risk and improve security posture.

Gain Complete Visibility Into Every Connected Asset

Segmentation decisions are only as good as the inventory behind them. Without deep visibility into every device on the network, segmentation decisions rely on static assumptions rather than real network context. IP and MAC addresses are a starting point, but organizations also need to consider:

• The devices communicating across the network
• The ports that devices and applications use
• The protocols that devices use

Organizations should look for solutions that:

• Provide deep device discovery and classification across IoT, OT, IoMT, and IT environments agentlessly, capturing a complete and authoritative inventory safely and without disruption, using both passive scanning and active safe scanning where appropriate.
• Map device-to-device communication patterns across the network, capturing what each device connects to, on which ports, and using which protocols.
• Continuously update inventory as devices appear, disappear, or change behavior.
• Act as a single source of truth that feeds every downstream segmentation policy.

Prioritize Segmentation Based on Real Risk, Not Just Device Count

When organizations manage tens or hundreds of thousands of connected assets, determining where to begin is nearly impossible without risk intelligence. Segmentation efforts that do not prioritize by risk consume significant operational effort while delivering minimal security value.

Organizations should look for solutions that:

• Use risk scoring grounded in ATT&CK Analysis, which maps each vulnerability against adversary techniques to determine whether it is actually exploitable on a specific device in a specific topology. Generic CVSS scoring cannot make that determination. ATT&CK Analysis produces a prioritized queue where every item reflects genuine risk rather than theoretical severity.
• Identify the small population of devices that drive the majority of segmentation urgency.
• Map each prioritized attack vector to the specific NAC or firewall policy that contains it, expressed as DACLs, Security Groups, or Group-Based ACLs depending on the enforcement infrastructure.
• Update risk scoring as patches land, exploits emerge, or device behavior changes.

Simulate Policies Before Deployment

One of the biggest reasons segmentation projects stall is the fear of breaking critical workflows. In industrial, healthcare, and enterprise environments, a policy that disrupts device communication can have serious operational consequences, leaving teams writing policies they are afraid to enforce.

Organizations should look for solutions that:

• Simulate each proposed policy against real, observed device communication before enforcement.
• Surface every flow that would be blocked, including dependencies the team did not know existed.
• Identify operational dependencies that would break under a proposed policy.
• Allow teams to iterate on a policy in simulation until the impact is acceptable, then push it to enforcement tools.

Automate Policy Creation Across NAC and Firewall Infrastructure

Once a policy is agreed upon, network teams still have to write it in the precise format of each NAC or firewall, and every vendor is different. Manual policy creation at scale is error-prone, time-consuming, and one of the primary reasons segmentation initiatives stall in pilot.

Organizations should look for solutions that:

• Automatically generate precise, conflict-free policies in the native format of the target enforcement tool, across NAC and firewall infrastructure, including DACLs, Security Groups, and Group-Based ACLs.
• Detect every new device joining the network and propose the correct policy automatically.
• Identify conflicts and overlaps before policies are pushed to enforcement tools.
• Push continuous updates so enforcement never lags behind a changing environment.

Treat Segmentation as a Continuous Process, Not a One-Time Project

Static policies that require constant manual intervention become outdated as new devices appear, configurations change, and vulnerabilities evolve. Most segmentation initiatives fail because policies do not remain effective as the network evolves.

Organizations should look for solutions that:

• Continuously adapt segmentation policies based on device behavior, risk posture, and network topology changes.
• React to firmware changes, vulnerability disclosures, and behavior shifts with automated policy updates.
• Detect devices whose risk profile has changed enough to require policy re-tiering.
• Replace the periodic-review model with always-current enforcement.

Continuously Audit Policies To Prevent Drift and Sprawl

Even well-designed segmentation policies degrade over time. Conflicting rules, redundant policies, and drift from original intent silently erode the security value of segmentation without anyone noticing.

Organizations should look for solutions that:

• Continuously evaluate policies for errors, conflicts, and redundancy.
• Flag when policies are no longer needed or have been made redundant by changes in device or network configuration.
• Identify opportunities to consolidate multiple policies for easier maintenance.
• Automatically recommend optimizations that maintain segmentation effectiveness without overwhelming networking teams with manual management.

How Asimily Closes the Gap Between Risk Intelligence and Enforced Policy

Most security programs already know that lateral movement is the risk. Where they stall is the distance between identifying the riskiest devices and enforcing a policy that contains them without disrupting operations. Asimily, the Proactive Cyber Defense Platform, closes that gap across IoT, OT, IoMT, and IT.

Inventory and Visibility: Asimily captures a complete, authoritative, continuously updated inventory of every connected device, agentlessly, using both passive scanning and active safe scanning where appropriate. Deep packet inspection, AI and ML classification, and multi-source correlation discover every IoT, OT, IoMT, and IT asset, including services, connections, and firmware versions, without disrupting the devices or networks it monitors.

ATT&CK Analysis: Every vulnerability finding is evaluated through Asimily’s ATT&CK Analysis, our core IP. Rather than relying on generic CVSS scoring, ATT&CK Analysis maps each vulnerability against adversary techniques to determine whether it is actually exploitable on a specific device in a specific environment and network topology. The result is a prioritized queue where every item reflects real risk, and the team works on the smallest list necessary to achieve the greatest risk reduction.

Segmentation Orchestration: Asimily’s Segmentation Orchestration is the intelligence and policy orchestration layer that turns a deployed but underutilized NAC or firewall into operational segmentation. Policy Auto-Recommendation tells teams where to begin and what to prioritize. Policy Creation generates enforcement rules in the native format of the target infrastructure, across NACs and firewalls, expressed as DACLs, Security Groups, and Group-Based ACLs. Policy Simulation validates every proposed policy against real, observed traffic before anything is applied, surfacing every flow that would be blocked so teams can iterate safely. Policy Application pushes rules to enforcement infrastructure across supported platforms.

Continuous Segmentation: Networks are not static. Devices are added, patched, decommissioned, and moved, and IP addresses and OS versions change. Continuous Segmentation tracks whether policies still match the current state of the network and adapts enforcement so it never falls behind. The Intelligent Policy Engine monitors for anomalous behavior and triggers protective actions, up to enforced quarantine, when warranted.

Policy Audit: Policies accumulate over time, and policy sprawl is an operational risk, not a theoretical one. Asimily’s audit capability continuously merges, deduplicates, and optimizes policies so enforcement stays effective without overwhelming network teams.

Threat Detection and Response: Asimily remains the only connected device security platform with native packet capture for forensic incident response across IoT, OT, IoMT, and IT. When a device is implicated in a lateral movement incident, native packet capture shortens the investigation and limits the blast radius.

Discover Asimily’s Segmentation Orchestration feature and learn how to prevent lateral movement on your network.