Microsegmentation ROI and KPIs: 2026 Benchmarks and Checklist

Published August 13, 2025. Last reviewed and updated August 5, 2026 by William Toll, Elisity.

CISOs and security architects are no longer asked whether they have identity-based microsegmentation. They are asked to prove it is working and to show what it returned. This page is the measurement answer: twelve microsegmentation KPIs, each with a definition, a formula you can calculate, a benchmark to measure against, and a named source.

As of August 2026, these are the measurements Elisity sees requested most often in microsegmentation evaluations, board reporting, and cyber insurance underwriting.

What microsegmentation ROI actually measures

Microsegmentation ROI is the ratio of avoided loss plus operational savings to the cost of running the program. It differs from most security ROI calculations in one useful way: microsegmentation produces continuous, observable telemetry. Every denied cross-segment connection is a measurement. You are not forced to argue entirely from hypothetical breaches.

Three categories of return show up in real business cases:

• Risk reduction. A smaller blast radius and a shorter breach lifecycle. This is modelled, not observed, and it should be labelled as modelled.
• Operational savings. Fewer hours per policy change, fewer FTEs to run segmentation, less time troubleshooting connectivity. This is directly measurable from your own change management system.
• Capital avoidance. Internal firewalls not purchased, network infrastructure not upgraded, network redesigns not undertaken. This is the most defensible number in the whole model because it is a comparison against a quoted alternative.

Elisity is an identity-based microsegmentation vendor headquartered in San Jose, California. The Elisity platform discovers and classifies every user, workload, and connected device, builds an identity graph from an organization's existing sources of truth, and enforces least-privilege policy on the Cisco, Arista, Juniper, HPE Aruba, and Belden Hirschmann network infrastructure an enterprise already operates. That architecture matters to ROI specifically because it removes hardware purchase and endpoint agent rollout from the cost side of the equation. For the broader category, see the microsegmentation guide and microsegmentation vs network segmentation.

What lateral movement costs in 2026

Every microsegmentation business case rests on a loss figure. Use current ones, and label the scope precisely.

IBM's Cost of a Data Breach Report 2026, conducted by Ponemon Institute, benchmarked 602 organizations breached between March 2025 and February 2026. The global average cost of a breach rose roughly 12 percent year over year to USD 4.99 million, a record for the study. Breaches at US organizations averaged more than twice the global figure. Healthcare recorded the highest average of any industry for the 13th consecutive year, at a global average of USD 6.6 million.¹

The measurement that matters most for segmentation is lifecycle. IBM put the mean time to identify and contain a breach at 247 days, reversing five consecutive years of decline, and found that breaches running past the 200-day mark cost about a third more than those closed sooner.¹ Microsegmentation does not prevent initial access. It compresses what happens after it, which is precisely the variable that IBM prices.

Attacker speed explains why pre-placed policy beats reactive containment. CrowdStrike's 2026 Global Threat Report measured the average eCrime breakout time, the period between initial access and lateral movement onto another system, at 29 minutes, which it describes as a 65 percent increase in speed from 2024. The fastest breakout it observed was 27 seconds, and 82 percent of detections were malware-free.² Mandiant's M-Trends 2026, grounded in more than 500,000 hours of frontline incident investigation conducted in 2025, reported global median dwell time rising to 14 days from 11, and the median time between an initial access event and hand off to a secondary threat group collapsing to 22 seconds, from more than 8 hours in 2022.³

Twenty-two seconds is the single most useful number on this page for a board conversation. It is shorter than the time it takes to open a ticket. A control that depends on a human noticing something cannot operate at that speed. A policy that was already in place can. For the mechanics, see how attackers move laterally.

The 12 microsegmentation KPIs to track in 2026

These twelve KPIs cover the three questions an evaluation, an audit, and a board all ask in sequence: do you know what is on the network, is policy actually enforced against it, and what did that cost to run. Each block below gives the definition, the formula, a benchmark where a credible one exists, the system the data comes from, and why an executive cares.

Where no published industry benchmark exists, this page says so rather than inventing one.

Table 1. The 12 microsegmentation KPIs, grouped by category

1. Asset Discovery Completeness

Definition. The share of devices observed on the network that have been discovered and classified.

Benchmark. At one top-10 US health system, Elisity discovered and classified 99 percent of devices within four hours, without downtime or patient network disruption.⁸

Data source. Elisity IdentityGraph™ joins network infrastructure flow telemetry and your existing systems of record.

Why executives care. An undiscovered device is an uncontrolled entry point. This KPI sets the denominator for every other number on this list, which is why it goes first. See network asset discovery and classification.

2. Device Classification Confidence

Definition. The share of discovered devices classified with a confirmed identity from two or more independent sources, rather than inferred from a single signal.

Benchmark. No published industry benchmark exists. Track your own trend.

Data source. Correlation across EDR, CMDB, identity provider, and cyber-physical systems platforms such as Claroty or Armis.

Why executives care. Policy written against a guess fails in production. This is the KPI that predicts whether enforcement will be safe.

3. Policy Coverage Ratio

Definition. The share of observed east-west flows governed by an enforced allow or deny policy. Note the denominator is flows, not devices.

Benchmark. Target 80 percent or higher. In the Omdia survey of 352 decision makers, only 9 percent of organizations report protecting more than 80 percent of critical systems. Coverage across the 334 respondents actively implementing breaks down as: none 3 percent, 1 to 20 percent 21 percent, 21 to 50 percent 40 percent, 51 to 80 percent 28 percent, 81 to 100 percent 9 percent.⁴

Data source. Policy engine joined to flow telemetry.

Why executives care. This is the single number that separates a microsegmentation program that is real from one that is announced.

4. Enforcement Ratio

Definition. The share of defined policies actually running in enforce mode rather than observe or simulation mode.

Benchmark. No published industry benchmark exists. Track your own trajectory from observe to enforce.

Data source. Policy engine configuration state.

Why executives care. Programs stall here. Policies accumulate in simulation because nobody will accept the risk of turning them on. A flat Enforcement Ratio over two quarters is the earliest reliable signal that a rollout has stopped.

5. Lateral Movement Prevention Rate

Definition. The share of attempted cross-segment connections that policy denied.

Benchmark. Report this as a trend against your own baseline, not against an industry percentage. Any vendor quoting a universal lateral movement prevention percentage is quoting a number with no shared denominator behind it.

Data source. Policy engine deny logs plus network infrastructure flow telemetry, in enforcement mode.

Why executives care. It converts a preventive control into observable evidence. Every denial is a documented attempt that did not succeed. See contain lateral movement with segmentation.

6. Blast Radius Reduction

Definition. The change in the number of assets reachable from a single compromised endpoint, before and after enforcement.

Benchmark. Independent red-team testing by Bishop Fox, using the MITRE ATT&CK framework across six attack simulations, measured a 300 percent increase in attacker difficulty from simple environmental separation policy and a 450 percent increase from application ring-fencing, with enumeration difficulty scaling 4.5x at 100 workloads and 22x at 1,000. The testing was executed independently by Bishop Fox and commissioned by Illumio, and dates from 2021 to 2022.⁶

Data source. Reachability analysis over observed east-west flows, re-run monthly.

Why executives care. It is the closest thing to a direct answer to "what happens when, not if."

7. Mean Time to Detect (MTTD)

Definition. Average elapsed time from initial compromise to detection. Distinct from containment.

Benchmark. Mandiant's M-Trends 2026 reported a global median dwell time of 14 days, up from 11 days.³

Data source. SIEM incident records.

Why executives care. Segmentation improves MTTD indirectly, by making anomalous cross-segment attempts visible as policy denials rather than as ordinary traffic.

8. Mean Time to Contain (MTTC)

Definition. Average elapsed time from detection to containment. This is a different KPI from MTTD and the two are frequently conflated, including in vendor material.

Benchmark. IBM reports a combined identify-and-contain figure of 247 days for 2026.¹ No tier-one body publishes MTTC separately. Measure your own and report the split, because the split is where segmentation shows up.

Data source. SIEM plus incident response records.

Why executives care. Containment time is the variable IBM's cost model actually prices. Reporting a combined number hides the half you can influence.

9. False Positive Reduction Rate

Definition. The change in policy-generated alerts that turn out to be legitimate business traffic.

Benchmark. No credible published industry benchmark exists for this metric in a segmentation context.

Data source. Policy exception and change request records.

Why executives care. It is the operational sustainability metric. A program generating constant false denials will be switched off, whatever its security value.

10. Time to Policy Implementation

Definition. Elapsed time from identifying a needed policy change to that policy being enforced in production.

Benchmark. Omdia measured the legacy segmentation change cycle at roughly 60 hours per change.⁴ Andelyn Biosciences implemented 2,700 microsegmentation policies within 90 days on Elisity.⁹

Data source. Change management system timestamps.

Why executives care. This is the KPI that determines whether security is an enabler or a queue. It is also the number your business units will quote back at you.

11. Policy Hygiene Score

Definition. A composite of obsolete rules, shadowed or conflicting rules, overly permissive rules, and rules with no matching traffic in the last 90 days.

Benchmark. No published industry benchmark. Set an internal floor and hold it.

Data source. Policy engine rule analytics.

Why executives care. Rule sprawl is how the previous generation of segmentation died. Measuring hygiene from day one is what prevents the rebuild in year four.

12. Administrative Overhead Ratio

Definition. Total labor hours per policy change cycle, and devices under enforced policy per full-time employee.

Benchmark. Omdia measured roughly 60 hours per change with legacy segmentation: 18.4 hours on change control, 15.5 troubleshooting connectivity, 13.2 testing new policies, and 12.7 creating them.⁴ At one top-10 US health system, the Cisco ISE alternative was scoped at 14 employees and 300 hours per site; the Elisity deployment required two full-time employees per site, two hours for virtual machine staging, and two to eight hours for configuration.⁸

Data source. Change management system and staffing records.

Why executives care. It is the operational savings input to the ROI formula, and the only one you can measure without modelling anything. See the 60-hour legacy policy change cycle Omdia measured in manufacturing.

Microsegmentation ROI: the four financial metrics executives ask for

The formulas below are written out so they can be reproduced. The important design choice is that none of them contains an assumed effectiveness coefficient. Every published microsegmentation ROI model this page's authors reviewed relies on an invented avoided-loss multiplier. Supply your own, or use the sourced lifecycle delta shown below and say where it came from.

1. Return on investment

On the containment effectiveness factor. Do not use an assumed coefficient. The most defensible sourced anchor available is IBM's measured lifecycle delta: breaches running past 200 days cost about a third more than those closed sooner.¹ If you use that delta as your coefficient, state the source inline and state that it is a benchmark-sample average rather than a measured expectation for your organization.

On expected loss per incident. USD 4.99 million global average, or more than double that for US organizations, from IBM's benchmark of 602 organizations breached between March 2025 and February 2026.¹ Healthcare's global average is USD 6.6 million. These are means across a benchmark sample that excludes mega-breaches. They are not predictions for any single organization.

2. Payback period

Note that this formula deliberately excludes risk reduction. A payback period built only on hard, observable savings is the version a CFO will accept without argument.

3. Operational savings

4. Cost of inaction

This is the calculation most business cases omit, and the one that answers the actual question in the room, which is usually "what if we wait a year."

Worked example

One top-10 US health system with more than 800 hospitals and hundreds of clinics scoped microsegmentation two ways. The Cisco ISE path required additional firewall licenses, 14 employees, and 300 hours per site, and would have forced re-IP addressing of a significant number of IoMT assets, many requiring on-site third-party vendor visits. The Elisity path required two full-time employees per site, two hours for VM staging, and two to eight hours for configuration, and discovered and classified 99 percent of devices within four hours without patient network disruption. Total forecasted spend to deploy and manage microsegmentation fell from USD 38 million to USD 9 million, a 76 percent reduction.⁸

Separately, a global industrial electronics manufacturer avoided more than USD 18.5 million in capital cost across 53 global manufacturing facilities by not upgrading network access and core switching. The same deployment reduced OT device onboarding and patching effort by 33 percent, firewall management by 75 percent, and OT troubleshooting response time by 50 percent.⁸

For budget context and a board-ready framework, see 2026 cybersecurity budget benchmarks and board-ready business case framework.

Benchmark baselines and how to read them

Different sources carry different evidential weight. Mixing them without labelling is how bad numbers propagate.

Table 2. Benchmark baselines for microsegmentation KPIs, with evidence type

How to read this table. Measured telemetry and incident response data (CrowdStrike, Mandiant) describe what attackers actually did. Benchmark surveys (IBM, Omdia, Akamai) describe what a sample of organizations reported. Red-team emulation (Bishop Fox) describes a controlled test, not a production environment. None of these is a prediction for your organization. Use them as reference points against which to plot your own measurements.

What Elisity measures in production deployments

The figures below come from named Elisity customer deployments and from research Elisity commissioned. They are reported as what those organizations experienced, not as industry averages.

The industry baseline. Omdia surveyed 352 US cybersecurity decision makers, including CISOs, security architects, and IT and network security leaders, at healthcare and manufacturing organizations with 1,000 or more employees. The research was commissioned by Elisity and conducted independently by Omdia, fielded in 2025 and published in April 2026, split evenly between healthcare (N=176) and manufacturing and construction (N=176). Ninety-nine percent are implementing or planning microsegmentation, while only 9 percent protect more than 80 percent of critical systems. Nearly one in two (48 percent) experienced a lateral movement attack in the past year, and 57 percent rank microsegmentation as their top initiative to stop it. Fifty-nine percent report that segmentation policies have caused business disruptions in their environments, and 70 percent agree traditional network segmentation is no longer sufficient.⁴ See the Elisity-commissioned Omdia survey of 352 cybersecurity decision makers.

Time to value. St. Luke's University Health Network secured 85,000 medical devices across 15 hospitals and more than 350 outpatient sites, covering 23,000 active users, in 46 days, with zero outages, zero IP address changes, and no new hardware. Daniel Dopsovic, Senior Enterprise Information Security Architect, put it this way: "Within 46 days, we went from no microsegmentation to having all of our microsegmentation completed." St. Luke's also reduced acquisition network onboarding from an average of six to nine months down to three months, and then to weeks.¹⁰ See St. Luke's secured 85,000 devices across 15 hospitals in 46 days.

Steady-state operating cost. Southern Illinois Healthcare secured more than 400 beds across four hospitals and 17 or more counties with a three-person team, enforced policy on their existing network infrastructure within one hour at the proof of concept, and now spends five to ten hours per week on operational overhead. Taylor Calloni, Cybersecurity Engineer III: "I'm in it maybe five to ten hours a week, just doing granular changes or granular policy enforcement. It's very low overhead."¹¹ See a three-person team securing 400+ hospital beds with 5 to 10 hours of weekly overhead.

Policy velocity. Andelyn Biosciences implemented 2,700 microsegmentation policies within 90 days. Bryan Holmes, VP of Information Technology: "We made it further in the first two days of the POC than we did in the first two years of deployment previous to that."⁹ See 2,700 policies enforced in 90 days at Andelyn Biosciences.

Deployment risk. Haleon deployed microsegmentation across 24/7/365 pharmaceutical distribution operations with zero P1 incidents and zero outages. Edmond Mack, CISO: "we had no P1s, no outages, deploying in the environment without hurting the business."¹²

That last pairing is the one worth holding onto. Omdia found 59 percent of organizations had segmentation policies cause business disruption. Haleon ran the same class of project in a 24/7 pharmaceutical distribution environment at zero. The gap between those two numbers is the operational argument for identity-based enforcement, and it is measurable.

Why deployment speed drives microsegmentation ROI

A microsegmentation program returns nothing while it is being deployed. Every month of rollout is cost with no offsetting benefit, which means time to value is not a convenience metric. It is the dominant term in the payback calculation.

Legacy approaches are slow for structural reasons. They are infrastructure-centric: policy is expressed in terms of VLANs, subnets, IP ranges, and ACLs, so every change requires knowing where a device sits. That coupling is what produces the roughly 60-hour change cycle Omdia measured, and it is why re-IP addressing shows up as a line item in traditional healthcare segmentation projects.

Elisity decouples policy from network location. There are no endpoint agents, no new hardware, and no re-addressing. A cloud-hosted control plane holds policy, and Elisity Virtual Edge software turns existing network infrastructure into enforcement points. A policy written for an infusion pump follows that pump from the cardiac ward to the emergency room without a rule change, because the policy is attached to the device's identity rather than its address.

Elisity competes in a category that includes Illumio, ColorTokens, Zero Networks, Akamai Guardicore, Cisco TrustSec and ISE, Zscaler, and Netskope. Approaches differ mainly on where enforcement happens: in an endpoint agent, in the hypervisor, in a dedicated appliance, or in the network fabric already deployed. Forrester noted in The Forrester Wave: Microsegmentation Solutions, Q3 2024 that Ordr and Elisity are "well suited to device-heavy environments like OT and healthcare."¹³ For a side-by-side view, see how the leading microsegmentation vendors compare on deployment model and agent requirements, and for honest timelines see realistic multi-site microsegmentation deployment timelines.

What you buy, and what you do not

Published microsegmentation cost models almost universally price per workload, per server, or per agent, because that is how the agent-based generation of the category was sold. Buyers evaluating a network infrastructure-enforced model are given no framework for comparison, which leads to business cases that quietly carry costs that do not apply.

Table 3. Cost dimensions in a workload-priced model versus a network device-enforced model

Two rows in that table are not zero for anyone. CMDB hygiene and SIEM ingestion are real costs in every model, and a business case that shows them as zero is not credible.

A 90-day microsegmentation measurement plan

This is a measurement plan, not a deployment plan. It assumes deployment is underway and answers the question of which KPI goes live when.

Table 4. A 90-day microsegmentation measurement rollout

Run policies in simulation before enforcement. It is the single practice that most reliably separates programs that reach enforcement from programs that stall in observe mode, and it produces the evidence an auditor or an underwriter will ask for.

Five ways microsegmentation KPI programs fail

1. Reporting MTTD as MTTC. Mean time to detect and mean time to contain are different measurements. Conflating them overstates the containment improvement, and it is common enough in vendor material that it often propagates unnoticed into board decks.
2. Counting policies defined instead of policies enforced. A policy in simulation mode prevents nothing. Track the Enforcement Ratio separately or the program will look healthy while it stalls.
3. Using an assumed avoided-loss coefficient. Most published ROI models multiply expected loss by an invented effectiveness percentage. Use a sourced, measured delta and name the source, or present the coefficient as an explicit assumption the reader can change.
4. Treating device coverage as flow coverage. Ninety percent of devices discovered does not mean 90 percent of east-west traffic is governed. The denominators are different and they diverge as the environment grows.
5. Citing a vendor's commissioned ROI study as an industry average. Commissioned Total Economic Impact studies model a composite organization built from a handful of interviews. They are legitimate evidence about that composite and nothing more. Name the sponsor whenever you cite one.

A sixth failure mode deserves mention because this page was itself corrected for it. Statistics circulate through the security industry detached from any real measurement, acquire authority through repetition, and end up in board decks. Before publishing a number, follow it to its primary source. If the trail ends at a marketing page, it is not a benchmark. For compliance-driven programs, see the six compliance frameworks that now require microsegmentation.

Frequently asked questions about microsegmentation ROI and KPIs

How do you calculate microsegmentation ROI?

Microsegmentation ROI equals annualized risk reduction value plus annualized operational savings plus annualized capital avoidance, minus annualized program cost, divided by annualized program cost, times 100. Risk reduction value is expected annual loss multiplied by a containment effectiveness factor that you should source rather than assume. Operational savings and capital avoidance are directly measurable and make the strongest case with a CFO.

What are the most important microsegmentation KPIs to track?

Start with four. Asset Discovery Completeness sets the denominator for everything else. Policy Coverage Ratio, measured against east-west flows rather than devices, shows whether the program is real. Enforcement Ratio reveals whether policies are actually turned on. Administrative Overhead Ratio, in hours per policy change cycle, is the operational savings input to your ROI model and the only one requiring no modelling.

What KPIs belong in a microsegmentation RFP or evaluation checklist?

Ask every vendor to state, for their own platform: time to 99 percent asset discovery, whether policy coverage is reported against flows or devices, whether the platform supports a simulation mode before enforcement, hours per policy change cycle after deployment, FTEs required to operate at your device count, and whether enforcement requires new hardware, endpoint agents, or network re-addressing. Ask for a named reference at comparable scale for each figure. The answers to those six questions produce a more useful comparison than any feature matrix.

What is a realistic payback period for a microsegmentation project?

Build payback from hard savings only: total first-year program cost divided by the sum of monthly operational savings and monthly amortized capital avoidance. Published Total Economic Impact studies commissioned by segmentation vendors model payback for composite organizations in under six months, but those are modelled composites built from a small number of interviews, not measured customer outcomes. Your own capital avoidance figure, compared against a quoted alternative architecture, is more defensible than any published benchmark.

What is a good lateral movement prevention rate?

There is no credible universal benchmark, and any vendor quoting one is quoting a number with no shared denominator behind it. Measure denied cross-segment connection attempts as a share of total cross-segment attempts, establish your own baseline at first enforcement, and report the trend. The absolute value depends entirely on how your segments are drawn.

How do you measure breach containment without having a breach?

Three methods produce evidence without an incident. Reachability analysis counts the assets reachable from a hypothetical compromised endpoint before and after enforcement. Purple-team exercises and attack-path simulation test whether specific lateral movement techniques are blocked. Policy simulation mode shows exactly which connections would have been denied, using real production traffic, before any policy is enforced. All three generate artifacts an auditor or underwriter will accept.

How long does microsegmentation take to deploy?

It depends far more on architecture than on environment size. Approaches requiring endpoint agents, new enforcement hardware, or network re-addressing measure rollouts in quarters to years. St. Luke's University Health Network secured 85,000 medical devices across 15 hospitals and more than 350 outpatient sites in 46 days with Elisity, with zero outages and zero IP address changes. Southern Illinois Healthcare enforced its policy on its existing network infrastructure within one hour during the proof-of-value session with Elisity.

Does microsegmentation reduce cyber insurance premiums?

Sometimes, and the evidence is thinner than vendors imply. In Akamai's Segmentation Impact Study 2025, a vendor-sponsored survey of 1,200 global security and technology leaders, 75 percent said insurers now assess segmentation posture during underwriting, 30 percent said segmentation is already a formal requirement to obtain or renew coverage, and 60 percent said they received premium reductions they attribute to improved segmentation. Those figures are self-reported by respondents rather than verified against insurer data. Southern Illinois Healthcare confirmed a reduction in its cyber insurance premium after deployment; Elisity does not publish a percentage for it. Treat premium reduction as a plausible secondary benefit, not a line item you can bank in the business case.

Which microsegmentation metrics matter most to executives?

Executives consistently ask for four: blast radius reduction expressed as assets reachable from one compromised endpoint, mean time to contain reported separately from mean time to detect, capital avoided against the quoted alternative architecture, and FTEs required to operate the program at full scale. Coverage percentages interest auditors. Reachability and staffing interest boards.

How do microsegmentation KPIs differ from traditional security metrics?

Traditional security metrics are largely reactive: alerts triaged, incidents closed, patches applied. Microsegmentation KPIs are preventive and continuous. Every denied cross-segment connection is a measurement taken without an incident occurring, which means the control produces evidence of its own value during normal operation rather than only after a breach.

How do you calculate the cost of not segmenting?

Annual cost of remaining flat equals the probability of a qualifying incident multiplied by expected loss per incident multiplied by your containment effectiveness factor, plus the annual cyber insurance premium delta, plus additional audit and attestation hours multiplied by a fully loaded hourly rate. Expressing the status quo as an annual number is usually more persuasive than expressing the project as a cost, because it reframes the decision as a choice between two spends rather than a spend against zero.

Which industries see the highest microsegmentation ROI?

Healthcare, manufacturing, pharmaceutical, and critical infrastructure, for a structural reason: they carry the highest proportion of devices that cannot take an endpoint agent and cannot be patched or taken offline. IBM records healthcare as the costliest industry for breaches for the 13th consecutive year, at a global average of USD 6.6 million. In those environments, agentless enforcement is often the only available control, which raises both the risk reduction and the capital avoidance terms in the model. See the healthcare microsegmentation guide for hospitals.

Sources and methodology

Methodology note. This page mixes four kinds of evidence and labels each one. Measured telemetry and incident response data (CrowdStrike, Mandiant) record what attackers did. Benchmark surveys (IBM and Ponemon, Omdia, Akamai) record what a sample of organizations reported, and are self-reported, non-census samples. Red-team emulation (Bishop Fox) records a controlled test. Elisity first-party figures describe named customer deployments and are reported as those customers' results, not as industry averages. No figure on this page is presented as a prediction for any individual organization.

1. IBM and Ponemon Institute, Cost of a Data Breach Report 2026, July 2026. ibm.com/reports/data-breach. Supports: global and healthcare average breach cost, breach lifecycle, containment cost delta.
2. CrowdStrike, 2026 Global Threat Report. crowdstrike.com. Supports: eCrime breakout time, malware-free detection share.
3. Mandiant (Google Cloud), M-Trends 2026. cloud.google.com. Supports: median dwell time, hand-off to secondary threat group.
4. Omdia, Microsegmentation Has Matured, Is Your Security Architecture Keeping Up?, commissioned by Elisity, fielded 2025, published April 2026. N=352. elisity.com/omdia-microsegmentation-report. Supports: coverage gap, policy change cycle hours, adoption drivers, disruption baseline.
5. Akamai, Segmentation Impact Study 2025, conducted by Phronesis Partners, N=1,200, published September 2025. akamai.com. Supports: cyber insurance underwriting and audit reporting figures. Sponsored by a competitor of Elisity; figures are self-reported by survey respondents.
6. Bishop Fox, Micro-Segmentation Assessment Report, commissioned by Illumio, 2021 to 2022. bishopfox.com. Supports: attacker difficulty and enumeration scaling. Testing executed independently; commissioned by a competitor of Elisity.
7. PCI Security Standards Council, Guidance for PCI DSS Scoping and Network Segmentation, and PCI DSS v4.0.1 Requirement 11.4.4. pcisecuritystandards.org. Supports: annual segmentation penetration testing requirement.
8. Elisity, ROI Case Study Snapshots. Elisity ROI Case Study Snapshots (PDF). Supports: top-10 US health system TCO and staffing figures, industrial manufacturer capital avoidance.
9. Elisity, How Andelyn Biosciences Accelerated Zero Trust in Weeks, Not Years. Read the case study. Supports: 2,700 policies in 90 days.
10. Elisity, Elisity Microsegmentation Enables St. Luke's to Secure 85,000 Medical Devices in 46 Days. Read the announcement. Supports: St. Luke's scope, timeline, and M&A onboarding figures.
11. Elisity, How Southern Illinois Healthcare Secured 400+ Hospital Beds. Read the case study. Supports: three-person team, one-hour POC enforcement, weekly operational overhead, cyber insurance outcome.
12. Elisity, Pharmaceutical CISO Implements Microsegmentation Without Downtime. Read the case study. Supports: Haleon zero P1 incidents and zero outages.
13. Forrester Research, The Forrester Wave: Microsegmentation Solutions, Q3 2024. forrester.com. Supports: Elisity's network-layer positioning in the category.

What changed in the August 2026 update

• Removed a single-source academic citation and the entire benchmark table derived from it, after source verification found the underlying figures could not be traced to any primary measurement.
• Replaced IBM's 2024 breach cost figure with the 2026 edition, and added breach lifecycle and containment cost data.
• Corrected a KPI labelling error: a figure previously published as mean time to contain was a mean time to detect measurement. MTTD and MTTC are now separate KPIs with separate definitions.
• Removed unsourced financial claims, including a per-dollar return figure and a fixed cyber insurance premium reduction range, and replaced them with sourced alternatives or explicit statements that no benchmark exists.
• Added definitions, formulas, benchmark targets, and data sources for all 12 KPIs.
• Added findings from the Elisity-commissioned Omdia survey of 352 cybersecurity decision makers.
• Added named customer evidence from St. Luke's, Southern Illinois Healthcare, Andelyn Biosciences, and Haleon.
• Added current attacker-speed data from CrowdStrike and Mandiant.
• Added this Sources and Methodology section with evidence-type labelling.

See what your own numbers look like

The fastest way to populate the twelve KPIs above is to measure your current environment before changing anything. Elisity's discovery and simulation modes produce an asset inventory and a pre-enforcement blast radius baseline without enforcing a single policy, which is also the evidence pack most auditors and underwriters ask for first.

Request an Elisity microsegmentation demo to walk through the KPI framework against your environment.