Vendor comparison

Modulos vs Trustible: AI Governance Comparison (2026)

Two dedicated AI governance platforms for two different buyer profiles within the same category: engineering-system evidence integration with ISO/IEC 42001 product conformity, and governance-professional workflow with automated intake and routing.

May 2026 · 13 min read · Updated for the EU AI Act Omnibus deal (December 2027 deadline)

Last reviewed: Next review: Companion to the 2026 buyer’s guide, Modulos vs Credo AI, and Modulos vs OneTrust.

Modulos and Trustible are both purpose-built, dedicated AI governance platforms. This is not a depth-versus-breadth comparison; both vendors cover the EU AI Act, NIST AI RMF, and ISO/IEC 42001 alongside additional frameworks, and both serve governance teams. The contrast is buyer profile within the organisation: a governance-professional workflow with an intake-and-routing focus, against broader engineering-system integration with model-level assessment depth.

Modulos and Trustible are both purpose-built AI governance platforms with multi-framework compliance coverage, but they target different buyer profiles: Modulos is the default choice for organisations that need engineering-system evidence integration alongside governance-team workflows and are pursuing ISO/IEC 42001 product conformity; Trustible is the default choice for governance teams (legal, risk, and compliance) that need automated AI use-case intake and routing, AI-assisted vendor documentation analysis, and Colorado SB 205 mapping alongside EU AI Act and NIST AI RMF coverage.

At a glance: Modulos vs Trustible

Seventeen dimensions buyers weigh in 2026 procurement, with the canonical positioning of each platform on each. The deeper analysis follows below, organised around the buyer-profile contrast.

DimensionModulosTrustible
HeadquartersZurich, SwitzerlandWashington, DC metro
Founded2018 (ETH Zurich spin-out)2023
Funding stageEstablished, multi-round4.6M USD Series Seed (June 2025)
Product scopeDedicated AI governance platformDedicated AI governance platform
Core approachAI-native compliance automation built on the Governance Graph (connected-object data model spanning frameworks, requirements, controls, and evidence)Governance-professional workflow platform orchestrating intake, risk assessments, vendor evaluations, and policy management
Primary buyer profileGovernance team plus engineering systems (data science, MLOps, security)AI governance professionals (legal, risk, compliance)
ISO/IEC 42001First platform to achieve product conformity (assessed by CertX)Maps ISO/IEC 42001 as a customer-compliance framework; does not publicly disclose ISO/IEC 42001 product or organisational certification as of May 2026
Risk quantificationMonetary, using Fermi estimation to assign defensible EUR, GBP, USD exposure rangesAttributes-based risk scoring with expert-curated taxonomies; recommends governance next steps
Regulatory framework coverageEU AI Act, ISO/IEC 42001, NIST AI RMF, OWASP, GDPR, NIS2, DORA, 10+EU AI Act, NIST AI RMF, ISO/IEC 42001, Colorado SB 205, 10+
AI use-case intake and routingAvailable within the Governance Graph workflow surfaceAutomated intake-and-routing is a primary product pillar (reference customer reduced intake time from three weeks to three hours)
Vendor and third-party AI documentationEvidence and control assessment via Scout and the Governance GraphAI-assisted vendor documentation analysis is a primary product pillar
Agentic automationScout investigative AI agent with deep-agent reasoning across GitHub, Bitbucket, Google Drive, Confluence, Jira, AWS, Azure, and the Governance Graph itselfAI-assisted analysis across intake, vendor documentation, and policy mapping; designed for the agentic AI readiness future
Engineering-system integrationsGitHub, Bitbucket, Google Drive, Confluence, Jira, AWS, Azure; partner telemetry from Vijil and ZenityConfigurable workflows with governance-team integrations; engineering-system evidence integration is more focused on the governance workflow itself
DeploymentSaaS, private cloud, on-premise, including sovereign-AI and air-gap optionsSaaS; does not publicly disclose private cloud or on-premise options as of May 2026
Public customer referencesPwC, Armasuisse, Beyond Gravity, ETH AI Center, Xayn, JobCloud, SCSK, Serai38% Fortune 500, 62% publicly traded, 87% global (segment-level disclosure; does not publicly name individual reference customers as of May 2026)
Regulatory authorshipTeam contributes to the EU GPAI Code of Practice, NIST AI Safety Institute Consortium, and CEN-CENELEC JTC 21Published thought leadership on Colorado SB 205, agentic governance, and AI governance committees
Strongest fitISO/IEC 42001 plus EU AI Act plus multi-framework compliance for organisations needing engineering-system evidence integration and model-level assessment depthGovernance-team-led programmes prioritising intake-and-routing workflow, vendor documentation analysis, and US state-law coverage (Colorado SB 205) alongside the EU and federal frameworks

Table reflects publicly available product information as of 27 May 2026. Verify current status with each vendor before procurement.

Why this comparison matters now

The EU AI Act Omnibus political agreement sets the Annex III high-risk deadline at 2 December 2027, and ISO/IEC 42001 has become the structured way to demonstrate AI governance maturity to a regulator, a customer, or a board. Against that backdrop, the dedicated AI governance platform market has matured to the point where the buyer’s choice is increasingly between two dedicated platforms rather than between a dedicated platform and a privacy-suite extension.

Dedicated AI governance platforms now divide along buyer-profile lines rather than category lines. Many enterprises evaluating AI governance in 2026 are deciding not between a dedicated AIGP and a privacy-suite extension, but between two dedicated AIGPs that target different teams within the same organisation. The first question is no longer whether the organisation needs a dedicated AI governance platform, but which team owns the programme: governance professionals running intake and policy, or a cross-functional team integrating with engineering systems. This comparison addresses that question, and it sits alongside the broader 2026 buyer’s guide and the other comparison pages in the series.

How each vendor positions itself

Modulos

Modulos positions itself as an AI-native compliance automation platform for regulated enterprises. The product is built around the Governance Graph, a connected data model that links frameworks, requirements, controls, and evidence as first-class objects rather than flat lists. Scout, the platform’s investigative AI agent, conducts multi-step research across the customer’s engineering and governance estate (code repositories, cloud accounts, document stores, and the Governance Graph itself), returning structured findings with file paths, line references, and relevance and confidence scores, and continuously checking AI systems against published policies. Dedicated evidence-processing and control-assessment agents propose evidence attachments and control state changes for human review. Modulos is the first AI governance platform to complete ISO/IEC 42001 product conformity assessment, audited by CertX, and quantifies AI risk in monetary terms using Fermi estimation. The team contributes to the EU GPAI Code of Practice, the NIST AI Safety Institute Consortium, and CEN-CENELEC JTC 21.

Trustible

Trustible positions itself as purpose-built for AI governance professionals: legal, risk, and compliance practitioners rather than data science or MLOps teams. The platform orchestrates AI use-case intake, risk and impact assessments, vendor and model evaluations, and policy management with configurable, audit-ready workflows. A centralised AI Inventory gives portfolio visibility; automated workflows handle intake reviews and approvals (a publicly referenced Fortune 500 consumer-goods customer reduced intake time from three weeks to three hours); an attributes-based risk scoring engine recommends governance next steps from expert-curated taxonomies for AI risks and mitigations; and AI-assisted vendor documentation analysis surfaces risk signals in third-party AI materials. Compliance mappings span 10+ frameworks including the EU AI Act, NIST AI RMF, ISO/IEC 42001, and Colorado SB 205, with board-ready reporting and dashboards. Trustible reports a customer base that is 38% Fortune 500, 62% publicly traded, and 87% global, and raised a 4.6M USD Series Seed round in June 2025 as it builds toward what it frames as the agentic AI readiness future.

Capability deep dive

Five capabilities where the two platforms diverge in design rather than in marketing language. Each subsection describes the underlying mechanic and frames the two as complementary buyer fits rather than a ranking.

01

Buyer profile and workflow surface

Trustible is built around the governance professional. The workflow surface follows the legal, risk, and compliance team’s mental model: intake, routing, assessment, policy, and reporting. For teams whose work begins and ends with the governance workflow itself, that focus is a close fit, and the platform orchestrates the team’s process end to end without requiring data-science or MLOps involvement.

Modulos spans two surfaces: the governance team’s workflow and the engineering-system evidence surface. Scout, the investigative AI agent, pulls evidence from code repositories, cloud accounts, ticketing systems, and document stores into the Governance Graph, so controls connect to evidence living in engineering systems. The two are complementary buyer fits. Governance teams whose programme is self-contained within the governance workflow are served well by Trustible’s focus; programmes that need to connect controls to evidence held in engineering systems benefit from the Modulos integration surface. The deciding question is where the evidence lives and who owns the integration with the systems that hold it.

02

Regulatory framework coverage and depth

Both platforms cover the EU AI Act, NIST AI RMF, and ISO/IEC 42001, alongside additional frameworks. Trustible’s published distinctive is explicit Colorado SB 205 mapping, a piece of US state-law coverage that buyers with Colorado exposure should weigh, and it organises its compliance content through an AI Inventory, an attributes-based risk scoring engine, and expert-curated taxonomies for AI risks and mitigations across 10+ frameworks.

Modulos’s distinctive is ISO/IEC 42001 product conformity assessed by CertX, plus the EU-leaning stack of EU AI Act, ISO/IEC 42001, DORA, NIS2, and the EU GPAI Code of Practice. The differentiating mechanic is cross-framework deduplication in the Governance Graph: one control mapped against multiple frameworks shares evidence, so a single implementation produces multiple regulatory artefacts from one audit-ready evidence chain. The two approaches answer different needs: structured compliance content tuned to the governance professional versus a connected-object data model with deduplication as a technical primitive.

03

Risk quantification approach

Modulos quantifies AI risk in monetary terms using Fermi estimation, a structured method for arriving at defensible numeric exposure ranges in EUR, GBP, or USD even where direct historical loss data is sparse. The output is an expected loss per AI system, comparable across the AI estate and reportable in the same financial units as operational and market risk. Board audit committees and prudential supervisors are the two audiences this serves directly.

Trustible uses an attributes-based risk scoring engine that draws on expert-curated taxonomies and recommends governance next steps. The output is a structured set of governance-decision-grade signals oriented to the AI governance committee’s workflow. The two are different valid approaches: monetary expected-loss in financial-decision-grade units for boards and supervisors, versus structured attribute scoring that maps directly onto governance next steps. Each fits a different reporting hierarchy, and the right one depends on whether AI risk has to land in the financial risk frame or in the governance committee’s decision process.

04

AI use-case intake, routing, and vendor documentation analysis

This is Trustible’s strongest workflow capability surface. Its intake-and-routing automation standardises submission, automates triage, and tracks AI use cases from intake through ongoing monitoring; a publicly referenced Fortune 500 consumer-goods customer reduced intake time from three weeks to three hours after adopting it. Its AI-assisted vendor documentation analysis surfaces risk signals and governance gaps in third-party AI vendor materials, and the AI Inventory and attributes-based risk scoring engine give the governance team portfolio visibility. Trustible’s intake-and-routing workflow is purpose-built and is one of the deeper executions of that specific surface in the category.

On the Modulos side, the complementary primary capability is Scout’s deep-agent reasoning across the engineering-system evidence surface. Where Trustible accelerates the governance team’s intake and review work, Scout conducts multi-step research across engineering systems and returns structured evidence into the Governance Graph. The two target different parts of the same programme: the governance-professional front door and the engineering-system evidence back end.

05

Evidence sourcing and engineering-system integration

Modulos’s Scout is an investigative AI agent built on a deep-agent reasoning architecture. It conducts multi-step research across GitHub, Bitbucket, Google Drive, Confluence, Jira, AWS, Azure, and the Governance Graph itself, returning structured findings with file paths, line references, and relevance and confidence scores, streaming its reasoning, and continuously checking AI systems against published policies. Dedicated evidence-processing and control-assessment agents propose evidence attachments and control state changes for human review. This is the layer where buyers with engineering-team-owned AI estates tend to differentiate.

Trustible’s integration surface is currently more focused on the governance-team workflow itself, with configurable workflows and structured submission rather than autonomous evidence extraction from engineering systems. For governance-team-led programmes that gather evidence through structured submission, that focus matches the operating model. For programmes where evidence has to be pulled from engineering systems, the Modulos integration surface is the closer fit. This is a buyer-profile-fit consideration about where the evidence lives, not a verdict on either platform.

When to choose Modulos

Five buyer profiles where Modulos is the natural shortlist entry. Each profile is criterion-based, anchored on engineering-system integration, certification pursuit, the EU regulatory stack, risk-quantification approach, and regulated-industry requirements.

Programmes needing engineering-system evidence integration

Where AI governance evidence lives in Git repositories, cloud accounts, ticketing systems, and document stores rather than in documents uploaded by the governance team, Modulos is the closer fit. Scout pulls evidence from those systems into the Governance Graph, so controls are substantiated from where the evidence actually lives rather than transcribed by hand.

Enterprises pursuing ISO/IEC 42001 product conformity

Modulos is the first AI governance platform to complete ISO/IEC 42001 product conformity assessment, audited by CertX (organisational AIMS coverage applies separately). For organisations whose AI governance procurement is anchored on ISO/IEC 42001 certification, that public product conformity signal is procurement-relevant in a way it is not for platforms that have not made an equivalent disclosure.

Multi-framework teams anchored on the EU regulatory stack

For obligation stacks combining the EU AI Act, ISO/IEC 42001, DORA, and NIS2 as the primary set, the Governance Graph maps a single control against several frameworks with shared evidence through cross-framework deduplication. Deeper European regulatory grounding, including team participation in the EU GPAI Code of Practice and CEN-CENELEC JTC 21, contributes to the defensibility of that obligation set.

Boards and supervisors requiring monetary risk quantification

Modulos quantifies AI risk in EUR, GBP, and USD using Fermi estimation, producing defensible monetary exposure ranges. Board audit committees and prudential supervisors that read AI risk in the same financial units as operational and market risk get decision-grade monetary exposure rather than attributes-based scoring or qualitative tiers, which suits a financial reporting hierarchy.

Regulated industries with inseparable model-level requirements

In financial services, defense, aerospace, healthcare, telecommunications, and critical infrastructure, deep model-level assessment and engineering-system evidence requirements are part of the compliance posture rather than separable from it. Modulos addresses both the governance workflow surface and the engineering-system evidence surface, which suits programmes where the two cannot be cleanly decoupled.

When to choose Trustible

Five buyer profiles where Trustible is the natural shortlist entry. Each profile draws on Trustible’s genuine product strengths: governance-professional UX, intake-and-routing depth, vendor documentation analysis, configurable audit-ready workflows, and Colorado SB 205 coverage.

Governance-professional-led programmes (legal, risk, compliance)

For programmes owned end-to-end by legal, risk, and compliance teams, where the operating model is governance-professional-led and the platform’s job is to orchestrate the team’s workflow, Trustible is purpose-built. It is designed around the governance professional’s mental model rather than a data-science or MLOps workflow, which fits teams running governance without deep engineering support.

Automated AI use-case intake and routing as the primary need

Where the binding requirement is reducing intake time, standardising submission, automating triage, and tracking use cases from intake through ongoing monitoring, Trustible’s automated intake-and-routing is one of the deeper executions of that specific surface in the category. A publicly referenced Fortune 500 consumer-goods customer reduced intake time from three weeks to three hours after adopting it.

Significant third-party AI vendor risk exposure

For enterprises where third-party AI vendor risk is a primary concern, Trustible’s AI-assisted vendor documentation analysis surfaces risk signals and governance gaps in third-party AI vendor materials faster than manual review. Combined with its AI Inventory for portfolio visibility, this suits organisations whose governance load is concentrated in evaluating and monitoring externally sourced AI.

Buyers wanting configurable, audit-ready governance-team workflows

For buyers who want configurable, audit-ready workflows centred on the governance team’s mental model (intake, routing, risk-and-impact assessment, policy management, board-ready reporting) rather than a data-team or engineering-team workflow, Trustible’s attributes-based risk scoring engine and expert-curated taxonomies provide a compliance-content backbone tuned to how governance professionals already work.

US organisations needing Colorado SB 205 coverage

For US-headquartered organisations needing Colorado SB 205 coverage alongside the EU AI Act and NIST AI RMF, Trustible maps Colorado SB 205 directly in its framework library and has published Colorado SB 205 materials and cross-framework mapping. Buyers with material Colorado exposure should weigh this state-law coverage, which Trustible surfaces as a published distinctive.

What if neither is right

A handful of adjacent options that come up in the same shortlists, and the buyer profile each fits best. For the full landscape, see the 2026 buyer’s guide.

Credo AI

Closer fit for US enterprise scale, MLOps-stack-centric programmes, and autonomous agent management at runtime.

OneTrust AI Governance

Closer fit if you already run OneTrust for GDPR or CCPA and AI governance is extending that existing privacy and trust platform.

IBM watsonx.governance

Closer fit if you already run IBM Cloud Pak for Data, OpenPages, or adjacent IBM systems and the integration economics favour extending the IBM stack.

Holistic AI

Closer fit if your AI risk concentration is bias and fairness rather than multi-framework compliance.

ServiceNow AI Control Tower

Closer fit if ServiceNow is your workflow and ITSM platform of record and agent governance is the primary requirement.

Fiddler AI or Lumenova

Closer fit if your primary need is model evaluation, explainability, or observability rather than compliance.

Zenity

Closer fit if your problem is agent-layer security and shadow-agent discovery rather than the policy and compliance layer.

Frequently asked questions

Ten questions that come up in Modulos vs Trustible procurement conversations, with direct answers.

Are Modulos and Trustible direct competitors?

Yes. Modulos and Trustible are both purpose-built, dedicated AI governance platforms with multi-framework compliance coverage, and they compete for the same category of buyer. The difference is buyer profile within the organisation. Trustible is built around the governance professional, where the operating model is owned end-to-end by legal, risk, and compliance teams running automated use-case intake, routing, risk assessments, vendor evaluations, and policy management. Modulos spans the governance-team workflow surface and the engineering-system evidence surface, where controls are substantiated by pulling evidence from code repositories, cloud accounts, ticketing systems, and document stores. They overlap on category and diverge on which team owns the programme.

Does Trustible hold ISO/IEC 42001 certification?

Trustible maps ISO/IEC 42001 as one of the frameworks its product helps customers comply against, which is distinct from Trustible itself holding ISO/IEC 42001 certification. As of May 2026, Trustible does not publicly disclose ISO/IEC 42001 certification, either as organisational AI management system certification or as product conformity assessment. Modulos is the first AI governance platform to achieve ISO/IEC 42001 product conformity, assessed by CertX. Verify current certification status directly with Trustible before any procurement decision, since this can change between page refresh cycles.

Which platform has better EU AI Act coverage?

Both platforms cover the EU AI Act as a primary framework. Trustible maps the EU AI Act inside a library of 10+ frameworks alongside NIST AI RMF, ISO/IEC 42001, and Colorado SB 205, with configurable, audit-ready workflows. Modulos is built around continuous EU AI Act conformity workflows with cross-framework deduplication in the Governance Graph, and framework intelligence is maintained against primary regulatory sources by the team, which contributes to the EU GPAI Code of Practice, the NIST AI Safety Institute Consortium, and CEN-CENELEC JTC 21. The stronger fit depends on whether your obligation stack is EU-leaning and engineering-integrated or governance-team-led with US state-law exposure.

Does Modulos map Colorado SB 205?

Modulos’s framework library focuses on the EU AI Act, ISO/IEC 42001, NIST AI RMF, OWASP, GDPR, NIS2, DORA, and related frameworks, with cross-framework deduplication in the Governance Graph. Trustible maps Colorado SB 205 directly in its framework library and has published Colorado SB 205 materials and cross-framework mapping. Buyers with material Colorado exposure who need Colorado SB 205 mapped today should confirm the current state of each vendor’s framework library directly, since coverage evolves between refresh cycles.

How do the pricing models compare?

Neither Modulos nor Trustible publishes standard list pricing; both quote per engagement based on AI estate size, framework scope, and deployment model. As an indicative reference point for dedicated AI governance platforms in 2026, engagements 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 packaging directly with each vendor, since neither publishes a public price list as of May 2026.

Can you use Modulos and Trustible together?

Yes, but uncommonly. Both platforms target the AI governance policy, compliance, and risk layer, so running both creates two systems of record at the same layer. The more typical pattern is to pick one as the AI governance system of record. Where both are present, one team might use Trustible’s intake-and-routing workflow as the governance-professional front door while another owns engineering-system evidence integration in Modulos, but most organisations consolidate on one platform to avoid duplicate inventories and reconciliation overhead.

What is the difference between Modulos Scout and Trustible’s AI-assisted analysis features?

Scout is Modulos’s investigative AI agent built on a deep-agent reasoning architecture. It conducts multi-step research across the engineering and governance estate (GitHub, Bitbucket, Google Drive, Confluence, Jira, AWS, Azure, and the Governance Graph itself), returns structured findings with file paths, line references, and relevance and confidence scores, streams its reasoning, and continuously checks AI systems against published policies. Trustible’s AI-assisted features focus on the governance-team workflow surface, most notably AI-assisted vendor documentation analysis that surfaces risk signals in third-party AI materials, plus AI assistance across intake and policy mapping. Scout extracts evidence from engineering systems; Trustible’s analysis accelerates the governance team’s review work.

How does cross-framework deduplication work in each?

Modulos models frameworks, requirements, controls, and evidence as connected objects in the Governance Graph. A single control mapped against both EU AI Act Article 9 and ISO/IEC 42001 Annex A satisfies both obligations with one implementation and one evidence chain, so the deduplication is a technical primitive of the data model. Trustible provides the compliance-content backbone through its AI Inventory, attributes-based risk scoring engine, and expert-curated taxonomies across 10+ frameworks; the reuse mechanism is organised around its governance-workflow surface rather than a connected-object graph. Ask each vendor for a worked example mapping one control against two frameworks with shared evidence.

Which platform is better for governance-team-led programmes versus engineering-integrated programmes?

For programmes owned end-to-end by legal, risk, and compliance teams whose work begins and ends with the governance workflow itself, Trustible’s governance-professional UX and intake-and-routing depth are a close fit. For programmes that need to connect controls to evidence living in engineering systems (Git repositories, cloud accounts, ticketing systems, document stores), Modulos’s Scout-driven evidence integration and Governance Graph are the closer fit. The deciding criterion is where the evidence lives and which team owns the integration with the systems that hold it.

How long does implementation take for each?

Implementation timelines depend on AI estate size, framework scope, deployment model, and integration depth. As a public reference point on the Modulos side, Xayn reached ISO/IEC 42001 audit readiness with Modulos in four weeks. On the Trustible side, one publicly referenced Fortune 500 consumer-goods customer reduced AI use-case intake time from three weeks to three hours after adopting its automated intake-and-routing workflow. Both vendors scope implementation per engagement; confirm current timelines directly with each vendor.

Evaluating Modulos and Trustible side by side?

If Modulos is on your shortlist after this comparison, we can walk through how the Governance Graph, Scout-driven engineering-system evidence integration, Fermi-style monetary risk quantification, and ISO/IEC 42001 product conformity map onto your specific framework scope, AI estate, and which team owns the programme. Book a 30-minute working session with a Modulos solutions engineer.

Book a working session →

Methodology and disclosures

Methodology

This comparison evaluates Modulos and Trustible based on publicly available information: vendor websites, the Trustible blog (including its published Colorado SB 205 materials and cross-framework mapping), Trustible’s June 2025 Series Seed funding announcement, Gartner Peer Insights reviews, the IAPP AI Governance Vendor Report (January 2026), and direct product experience on the Modulos side. Capabilities reflect publicly available information as of 27 May 2026.

Disclosure

This comparison is published by Modulos AG. Modulos is one of the two vendors compared on this page. Trustible’s capabilities are described from publicly available product information; no commercial relationship between Modulos and Trustible is implied. No vendor paid for inclusion or favourable treatment. Inclusion does not constitute endorsement, and the buyer profiles in the “When to choose Trustible” section reflect Trustible’s genuine strengths.

Refresh cadence

This page is reviewed quarterly. The next scheduled review is . 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.


Published by Modulos AG. Last updated: 27 May 2026. Next refresh: 27 August 2026.

Related reading: Modulos vs Credo AI · Modulos vs OneTrust AI Governance · Modulos vs IBM watsonx.governance · 2026 AI governance tools buyer’s guide · EU AI Act compliance · ISO/IEC 42001 · NIST AI RMF · Modulos AI governance platform · Xayn ISO 42001 case study