Modulos vs Collibra: AI Governance Comparison (2026)

Organisations building AI governance as a first-class compliance programme

Where AI governance is being built as a first-class compliance programme with its own system of record (frameworks, requirements, controls, evidence, and AI assets as first-class queryable objects in the Governance Graph), Modulos was designed AI-native from the data model up around AI-specific compliance workflows. For organisations whose primary AI risk frame is regulatory obligations, model behavior, and audit-grade control evidence rather than data provenance and lineage, the AI-native data model is the closer architectural fit.

Enterprises pursuing ISO/IEC 42001 product conformity specifically

Modulos is the first AI governance platform to have completed ISO/IEC 42001 product conformity assessment, audited by CertX. Collibra has publicly announced organisational ISO/IEC 42001 certification for AI Governance, which is a different artefact: organisational AIMS certifies an organisation’s AI management system, while product conformity certifies the platform itself against the standard. For RFPs that scope the requirement to product conformity specifically, the distinction matters.

Multi-framework compliance teams anchored on EU regimes

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 EU regulatory stack.

Boards and supervisors requiring monetary risk quantification

Modulos quantifies AI risk in EUR, GBP, and USD using Fermi estimation. Board audit committees and prudential supervisors comparing AI System A against AI System B in decision-grade financial units get the same reporting frame for AI risk as they get for operational, credit, and market risk, rather than a separate qualitative scoring system or a categorical risk register adapted from a data governance framework.

Regulated industries scrutinised on model behavior and audit evidence

For regulated industries (financial services, defense, aerospace, healthcare, telecommunications, critical infrastructure) where AI risk is fundamentally about model behavior, regulatory obligations, and audit evidence rather than data lineage alone, Scout pulls evidence from where it lives (GitHub, Bitbucket, AWS, Azure, Confluence, Jira) rather than registering AI assets into a data catalog. The integration economics favour engineering-system evidence collection for that specific risk frame.

Organisations already running Collibra for data governance

Where Collibra is already in production for data catalog, metadata management, or data lineage, AI governance can extend an existing platform investment rather than standing up a new system of record. The integration economics and organisational change cost of staying within the existing Collibra programme are real and substantial: existing tooling, taxonomy, operating model, and trained users carry over to AI assets inside the same catalog.

Enterprises where AI risk is fundamentally a data-provenance problem

For regulated industries where strict data-source documentation, data quality assurance, and end-to-end data lineage are the primary regulatory and audit scrutiny (financial services for model risk management, healthcare and life sciences for clinical and research AI, telecommunications for customer-data-driven AI), Collibra has one of the deepest data lineage and metadata management products in the market. That depth is genuinely the right architectural choice when the most consequential audit question is where training data came from and how it was transformed.

Buyers prioritising a unified data and AI catalog

Where the desired operating model places AI assets alongside data assets in a single, automated documentation environment, Collibra’s unified data and AI catalog is the architecture built for that pattern. Automated documentation, lineage propagation, and metadata management extend naturally from data assets to AI assets, particularly where the same governance team owns both and a unified taxonomy is the design intent.

Data-team-led AI governance buying decisions

Where the AI governance programme is being defined and procured by the same team that owns the data governance programme, the existing Collibra tooling, taxonomy, and operating model carry over to AI with low organisational friction. The integration economics, training investment, and procurement defensibility all favour continuing inside the Collibra ecosystem rather than opening a new vendor relationship.

Organisations whose primary AI scrutiny is data quality and source documentation

For buyers whose most consequential audit and regulatory question is where the training data came from, how it was transformed, and whether data quality is documented across the pipeline, Collibra’s category-defining strengths in data catalog, automated documentation, and data lineage map directly to that primary risk frame. Broad Fortune 1000 enterprise references across regulated industries reinforce the procurement defensibility for that buyer profile.

Is Modulos a replacement for Collibra?

Not in the general case. Collibra is a category-defining data governance, data catalog, metadata management, and data lineage platform; Modulos is a dedicated AI governance platform anchored on ISO/IEC 42001 product conformity, the EU AI Act, and multi-framework AI compliance. The two address different procurement questions: Collibra is the system of record for data assets and their lineage, while Modulos is the system of record for AI-specific compliance obligations, control evidence, and audit trail. Many organisations choose to run both, with Collibra owning the data layer and Modulos owning the AI compliance layer.

Does Collibra hold ISO/IEC 42001 certification?

Yes. Collibra announced ISO/IEC 42001 certification for AI Governance in January 2025 (organisational AI management system scope). Modulos holds ISO/IEC 42001 product conformity assessment, audited by Swiss conformity-assessment body CertX, which is a different artefact: organisational AIMS certifies an organisation’s AI management system, product conformity certifies the platform itself against the standard. Both signals matter; the right weighting depends on the buyer’s procurement criteria.

Which platform has better EU AI Act coverage?

Both platforms cover the EU AI Act. Modulos is built around continuous EU AI Act conformity workflows, Annex III risk classification, and Fundamental Rights Impact Assessment templates, with framework intelligence maintained against primary regulatory sources by a team contributing to the EU GPAI Code of Practice and CEN-CENELEC JTC 21. Collibra covers the EU AI Act inside the AI Command Center (Collibra’s AI Governance product line) of its broader data and AI catalog, with the EU AI Act sitting alongside its long-running coverage of data regulations such as GDPR and CCPA.

Should I extend Collibra into AI governance or buy a dedicated AI governance platform?

The deciding criterion is the primary AI risk frame. If your most consequential AI scrutiny is data provenance, data quality, and end-to-end data lineage, extending Collibra into AI governance keeps your AI assets inside the same catalog and lineage architecture as your data assets, and the integration economics favour staying with Collibra. If your most consequential AI scrutiny is model behavior, regulatory obligations against AI-specific frameworks such as ISO/IEC 42001 and the EU AI Act, and audit-grade control evidence pulled from engineering systems, a dedicated AI governance platform such as Modulos is the closer architectural fit. Many enterprises adopt both, with Collibra owning the data layer and the dedicated platform owning the AI compliance layer.

Can you use Modulos and Collibra together?

Yes, and it is a common pattern in regulated enterprises. Collibra owns the data catalog, metadata management, and data lineage for the broader data estate; Modulos owns the AI-specific compliance system of record, control evidence, and audit trail for AI systems. The two layers are complementary rather than competing: Collibra answers data-provenance questions about AI training and operational data, and Modulos answers regulatory-obligation and model-behavior questions about the AI systems built on top.

What is the difference between data governance and AI governance?

Data governance is the discipline of managing data as an enterprise asset: data catalog, data lineage, metadata management, data quality, data access, and compliance with data regulations such as GDPR and CCPA. AI governance is the discipline of managing AI systems as regulated artefacts: AI use-case intake, model documentation, risk classification, control evidence, and compliance with AI-specific frameworks such as the EU AI Act, ISO/IEC 42001, and NIST AI RMF. The two overlap (AI systems consume data, and data quality affects model behavior), but the primary risk frame differs. Data governance frames AI risk as a data provenance and lineage problem; AI governance frames AI risk as a model-behavior and regulatory-obligation problem. Both framings are credible; the right architecture depends on which frame is the buyer’s primary scrutiny.

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. Collibra organises cross-framework coverage through the metadata model of its data and AI catalog; AI-framework coverage and the specific deduplication mechanism for AI-specific obligations vary across the AI Command Center (Collibra’s AI Governance product line) and the broader data governance product.

Which platform is better for financial services model risk management?

Both platforms appear on financial services shortlists for different reasons. Collibra is a strong fit where model risk management is anchored on strict data-source documentation, end-to-end data lineage, and data quality assurance across the training and operational pipeline, particularly where the same governance team owns both data and AI. Modulos is a strong fit where model risk management is anchored on regulatory obligations across the EU AI Act, ISO/IEC 42001, DORA, and NIST AI RMF simultaneously, with monetary risk quantification in EUR, GBP, and USD for board audit committees and prudential supervisors.

How long does implementation take for each?

Implementation timelines depend on AI estate size, framework scope, deployment model, and how deeply the platform is integrated with adjacent systems. As a public reference point, Xayn reached ISO/IEC 42001 audit readiness with Modulos in four weeks. Collibra implementations vary substantially by how much of the broader data catalog, metadata management, and data lineage programme is in scope, and by how the AI Command Center (Collibra’s AI Governance product line) is layered onto an existing Collibra deployment versus a fresh rollout.

Methodology

This comparison evaluates Modulos and Collibra based on publicly available information: vendor websites, Collibra product documentation, analyst reports including the IAPP AI Governance Vendor Report January 2026, Gartner and Forrester data governance market analyses, peer review platforms, press coverage, 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. Collibra’s capabilities are described from publicly available product information; no commercial relationship between Modulos and Collibra is implied. No vendor paid for inclusion or favourable treatment. Inclusion does not constitute endorsement; the buyer profiles in “When to choose Collibra” reflect Collibra’s genuine strengths in data governance, metadata management, and data lineage.

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.