Modulos vs Lumenova: AI Governance Comparison (2026)

Compliance and audit-evidence is the primary requirement

Where the primary AI governance requirement is regulatory compliance and audit-ready evidence across multiple frameworks, and the system of record needs to be the controls-and-evidence layer rather than the model-testing layer, Modulos is the natural fit. The binding question is whether the organisation can produce audit-ready evidence of compliance, not the model-level fairness, robustness, and explainability evaluation of a single model.

Enterprises pursuing ISO/IEC 42001 product conformity

Modulos is the first AI governance platform to have completed ISO/IEC 42001 product conformity assessment, audited by CertX. For organisations whose AI governance procurement is anchored on ISO/IEC 42001, where the assessment scope is the governance programme rather than individual models, the vendor-level product conformity signal is procurement-relevant in a way it is not for platforms that have not made an equivalent public disclosure.

Multi-framework compliance teams managing several regimes at once

If your obligations stack EU AI Act, ISO/IEC 42001, DORA, NIS2, and NIST AI RMF simultaneously, the Governance Graph’s cross-framework deduplication maps a single control against several frameworks with shared evidence. One implementation, multiple regulatory artefacts, one audit-ready evidence chain across the regulatory stack, where cross-framework reuse is a material procurement criterion.

Boards and supervisors requiring programme-level monetary risk

Modulos quantifies AI risk in EUR, GBP, and USD using Fermi estimation at the programme level. Board audit committees and prudential supervisors that read AI risk in defensible financial decision units, rather than model-level performance metrics, get the same reporting frame for AI risk as they get for operational and market risk across the programme as a whole.

Regulated industries where audit-ready evidence is the binding test

In financial services, defense, aerospace, healthcare, telecommunications, and critical infrastructure, the primary AI scrutiny is often whether you can produce audit-ready evidence of compliance with the framework, rather than the model-level fairness, robustness, and explainability evaluation of a specific model. Where the AI governance buying decision is owned by GRC, compliance, or AI Centre of Excellence leadership, Modulos maps to that buyer and budget centre.

ML and data science teams owning model evaluation and explainability

Where the primary AI governance requirement is model evaluation, testing, and explainability, and the buying decision is owned by ML or data science leadership rather than by compliance or GRC functions, Lumenova is the natural fit. The platform validates and stress-tests models with custom metrics for performance, fairness, bias, robustness, and explainability across both traditional ML and GenAI.

Bias and fairness testing for specific high-stakes models

For organisations focused on bias and fairness testing as a core capability for specific high-stakes models, such as hiring, credit, healthcare diagnostic, or insurance underwriting models, Lumenova provides native testing of protected attributes. Where rigorous model-level fairness assessment is the binding requirement, this depth is one of the stronger model-evaluation stories in the category.

Enterprises building extensive GenAI applications

Enterprises building extensive GenAI applications that need model-specific testing tools, behavioural monitoring, and evaluation dashboards fit Lumenova’s GenAI tooling well. The platform covers performance, fairness, robustness, and hallucination detection for foundation and fine-tuned models, and evaluates retrieval-augmented generation across retriever and generator performance. GenAI tooling is one of the more developed parts of its positioning.

Buyers prioritising explainability dashboards and model performance tracking

For buyers whose analytical frame is "what is happening inside this specific model?" rather than "where does this control sit across our regulatory framework portfolio?", Lumenova’s explainability dashboards, library of risk scenarios, and continuous monitoring for drift and emerging behaviour are built directly for that question. The depth of native model analysis is the platform’s primary strength.

Governance, model-risk, and compliance teams wanting model-layer depth in one platform

Where a governance, model-risk management, or compliance function wants an AI governance platform that also carries deep native model evaluation, explainability, and monitoring, Lumenova maps to that buyer. It pairs AI inventory, guardrails, observability, and compliance mapping to the EU AI Act, NIST AI RMF, ISO/IEC 42001, and GDPR with the model-level fairness, robustness, and explainability testing that model-risk teams need, making it a strong fit where one platform must serve both the governance programme and the model layer.

Can you use Modulos and Lumenova together?

Yes. Modulos and Lumenova address different layers of the AI governance lifecycle and are more naturally complementary than substitutable. Some organisations run Lumenova at the model-evaluation layer (bias and fairness testing, GenAI behavioural assessment, explainability dashboards for specific high-stakes models) and Modulos at the compliance-and-evidence layer (audit-ready evidence, control assessment, multi-framework regulatory conformity, ISO/IEC 42001 product conformity). Model-evaluation outputs from Lumenova become evidence artefacts referenced by Modulos’s Governance Graph for control conformity, while Modulos’s compliance and evidence layer references the model-testing outputs without owning the model-testing function itself.

Is Modulos a replacement for Lumenova?

Not in a direct sense. Modulos is a compliance-and-evidence platform built for governance, risk, and compliance teams owning audit-ready evidence and regulatory conformity; Lumenova is an AI governance and model-risk platform whose distinctive depth sits in model evaluation, explainability, and monitoring, serving model quality, bias and fairness testing, and behavioural assessment of specific models alongside AI inventory, guardrails, and compliance mapping. Their distinctive depth anchors different layers of the AI governance stack, so the decision is which layer the procurement is funding rather than which platform replaces the other.

Does Lumenova hold ISO/IEC 42001 certification?

As of May 2026, Lumenova does not publicly disclose ISO/IEC 42001 certification, either as an organisational AI management system certification or as product conformity assessment. Lumenova publicly holds SOC 2 Type II and ISO/IEC 27001 certifications and offers tooling to help its own customers work toward ISO/IEC 42001 compliance. Verify directly with Lumenova before any procurement decision, since certification status can change between page refresh cycles.

Which platform has better EU AI Act coverage?

The two platforms address the EU AI Act at different layers. Modulos is built around continuous EU AI Act conformity workflows, Annex III risk classification, and Fundamental Rights Impact Assessment templates across the controls-and-evidence layer, with framework intelligence maintained against primary regulatory sources by a team contributing to the EU GPAI Code of Practice and CEN-CENELEC JTC 21. Lumenova maps its model-evaluation and risk-assessment outputs to the EU AI Act among other frameworks, which is the model-layer view of the same regulation. Which one fits depends on whether the binding requirement is audit-ready compliance evidence or model-level evaluation.

Which platform owns model-level bias and fairness testing?

Lumenova owns model-level bias and fairness testing as a native, primary capability, with metrics for fairness and bias across protected attributes, performance, robustness, and explainability for both traditional ML and GenAI models. Modulos does not natively generate model-level bias and fairness test results; instead, model-evaluation outputs from tools such as Lumenova are referenced by Modulos’s Governance Graph as evidence artefacts that map to regulatory controls. The two platforms own different functions in the same lifecycle.

How do the pricing models compare?

Neither vendor publishes standard list pricing for enterprise deployments, so both are typically quoted per engagement. Indicative ranges for dedicated AI governance and AI evaluation platforms in 2026 run from approximately 50,000 USD per year for a focused mid-market deployment to several hundred thousand USD per year for enterprise-wide programmes. Confirm current pricing and scope directly with each vendor, since the model-evaluation and compliance segments price on different units (models and evaluations versus frameworks and controls).

What is the difference between compliance-and-evidence AI governance and model-evaluation AI governance?

Compliance-and-evidence AI governance is the system of record for regulatory conformity, control assessment, and audit trails across frameworks such as the EU AI Act and ISO/IEC 42001; the binding question is whether you can produce audit-ready evidence of compliance. Model-evaluation AI governance is the system of record for testing specific models for bias, fairness, performance, robustness, and behaviour; the binding question is what is happening inside a given model. Modulos sits at the compliance-and-evidence layer and Lumenova sits at the model-evaluation layer.

How long does implementation take for each?

Implementation timelines depend on AI estate size, framework or model scope, and integration depth. As a public reference point for the compliance-and-evidence layer, Xayn reached ISO/IEC 42001 audit readiness with Modulos in four weeks. Model-evaluation deployments are scoped by the number and type of models under test and the metrics and risk scenarios in scope; confirm timelines directly with Lumenova for the model set you intend to evaluate.

Which platform is better for GenAI applications?

For model-level GenAI work, Lumenova ships native GenAI evaluation tooling covering performance, fairness, robustness, and hallucination detection, including evaluation of retrieval-augmented generation applications across retriever and generator performance. For the compliance-and-evidence view of the same GenAI applications, Modulos maps GenAI evaluation outputs and runtime telemetry to regulatory controls in the Governance Graph. The better fit depends on whether the primary requirement is model-level evaluation or audit-ready compliance evidence.

Methodology

This comparison evaluates Modulos and Lumenova based on publicly available information: vendor websites, product documentation, public marketing materials, analyst reports including the IAPP AI Governance Vendor Report January 2026, peer review platforms, press coverage, and direct product experience on the Modulos side. Capabilities reflect publicly available information as of 27 May 2026. The page frames Modulos as anchored on the compliance-and-evidence layer and Lumenova as an AI governance and model-risk platform whose distinctive depth sits at the model-evaluation, explainability, and monitoring layer, rather than as substitutable head-to-head competitors.

Disclosure

This comparison is published by Modulos AG. Modulos is one of the two vendors compared on this page. Lumenova’s capabilities are described from publicly available product information; no commercial relationship between Modulos and Lumenova is implied. Lumenova is not in Modulos’s 22-vendor buyer’s guide; it sits in an adjacent segment rather than as a direct competitor, and this page is published because buyers searching “Modulos vs Lumenova” deserve an authoritative source for the comparison, not because the two vendors compete head-to-head in the same segment. No vendor paid for inclusion or favourable treatment. The buyer profiles in “When to choose Lumenova” reflect Lumenova’s genuine strengths in the model-evaluation segment.

Refresh cadence

This page is reviewed quarterly. The next scheduled review is 27 August 2026. Material changes to either platform’s capabilities, certifications, or buyer fit should be reflected within one refresh cycle. For questions about this comparison or to flag a factual correction, contact the Modulos team.