10 best AI governance and regulatory compliance software tools

These are the 10 best AI governance and regulatory compliance software tools:

1. PrivaLex
2. TrustWorks
3. Credo AI
4. IBM watsonx.governance
5. OneTrust AI Governance
6. ServiceNow AI Control Tower
7. Microsoft Purview and Compliance Manager
8. Holistic AI
9. Securiti AI Security and Governance
10. Vanta AI compliance workflows

AI governance is no longer just an internal policy about ChatGPT. In a real company, the AI landscape includes copilots, scoring systems, HR tools, product features, agents, third-party APIs, personal data, cloud vendors, enterprise customers and audit requirements. The right software should help teams make defensible decisions, not just store documents.

In practice, this decision separates three areas that should be governed together. The first is AI usage governance: who can use which tools, with which data and under which limits. The second is AI system and model governance: inventory, risk, testing, change management, documentation and oversight. The third is regulatory compliance: the EU AI Act, GDPR, ISO 42001, ISO 27001, NIS2, DORA, customer contracts and local supervisory expectations.

The Regulation (EU) 2024/1689 entered into force on 1 August 2024. AI literacy and prohibited practices have applied since 2 February 2025, and obligations for general-purpose AI models have applied since 2 August 2025. The European Commission maintains an official AI Act implementation timeline and an updated page on the European AI regulatory framework, including requirements such as data quality, logging, documentation, information for deployers, human oversight, robustness, cybersecurity and accuracy.

The practical takeaway is simple: AI governance software must help a company prove decisions. Who approved the use case, why it was classified that way, what data is involved, which vendor is used, which controls apply, which tests were performed, what training was delivered, what evidence is retained and when the system will be reviewed.

The 10 best AI governance and regulatory compliance software tools

1. PrivaLex

Before choosing a tool, a company needs a governance model that defines what counts as an AI system, who can propose a use case, who approves it, what vendor information is required, when the DPO should be involved, when an impact assessment is needed, which controls are mandatory and what evidence must be kept.

At PrivaLex, we help build that operational layer and translate it into the software the company already uses or plans to implement. A typical project starts with an AI system inventory, a taxonomy of roles and risks, an obligation matrix and an evidence model connected to AI Act and ISO 42001, GDPR, ISO 27001, NIS2 and DORA where relevant.

The critical point is not filling in an “AI governance” screen. It is making sure each field is useful for management, audit, enterprise customers or regulators. The same record can support a vendor review, a DPIA, an ISO 42001 assessment, a due diligence response and an evidence pack for a regulated customer.

PrivaLex is especially useful when a company needs to:

• Define intake and approval workflows for AI use cases.
• Separate internal AI, product AI, proprietary models, AI-enabled SaaS and agents.
• Translate AI Act obligations into practical controls.
• Connect privacy, security, suppliers, training and evidence.
• Prepare the purchase of TrustWorks, Credo AI, IBM, OneTrust, ServiceNow, Microsoft, Holistic AI, Securiti or Vanta with clear requirements.
• Check whether an existing GRC, privacy or security platform can be adapted before buying another tool.

This avoids a common procurement mistake: buying a powerful platform and then using it as a questionnaire repository. AI governance needs judgement, accountability and living evidence.

2. TrustWorks

TrustWorks is a European privacy and AI governance platform focused on discovering, registering, classifying and monitoring AI systems. Its official AI Governance page highlights AI system registration, EU AI Act risk classification, shadow AI, predefined controls, assessments, reporting and stakeholder collaboration.

It can work well for companies where privacy, legal, security and product need to work from the same inventory. It is also relevant when the problem is not only a proprietary model, but a portfolio of SaaS tools, vendors, internal initiatives and generative AI uses emerging outside the formal review process.

TrustWorks can be a good option when you need:

• A registry of AI use cases, systems, models and vendors.
• AI Act-aligned risk classification.
• Shadow AI detection and monitoring.
• Controls and mitigations linked to risk categories.
• Evidence and reporting for internal review.

For companies with an established GDPR programme, its strength is bringing AI governance close to privacy and compliance work. If the team already handles records of processing, DPIAs, vendor reviews and evidence, adoption may feel more natural. Still, define the minimum data required for each use case first, so the inventory does not become an endless form.

3. Credo AI

Credo AI is a specialist enterprise AI governance platform. Its official product page describes capabilities to discover, assess, govern, monitor and report on AI systems, including agents, models, applications and vendors, with support for frameworks such as the EU AI Act, ISO 42001 and the NIST AI RMF.

It makes sense when an organisation develops or integrates AI across multiple teams and needs a dedicated layer for policy intelligence, controls, evidence and reporting. Compared with tools focused mainly on privacy or data security, Credo AI is often a stronger fit where AI programmes are distributed across data, product, engineering, legal, compliance and risk.

Credo AI can be a good option when you need:

• Specific governance for models, agents, applications and vendors.
• Policy packs for AI frameworks.
• Review workflows by team, system and risk.
• Continuous evidence of controls, decisions and exceptions.
• Reporting for AI committees, audit and enterprise customers.

Its value is clearest where the company does not merely use AI, but embeds it into products, sensitive processes or high-impact decisions. If the immediate need is controlling prompts and data inside Microsoft 365, it may be more specialised than needed for the first phase.

4. IBM watsonx.governance

IBM watsonx.governance is aimed at organisations with production models, hybrid environments and strong risk management needs. IBM describes watsonx.governance as a visibility, control and assurance layer for AI across multi-vendor environments. Its Governance Console documentation covers risk and compliance management for generative AI assets and machine learning models, including quality, fairness, drift, alerts, workflows and links to regulatory mandates.

This option is relevant when AI governance has a serious technical dimension: proprietary models, explainability, performance monitoring, drift, testing, version changes and operational risk. It can also fit companies already working with IBM, OpenPages or complex data architectures.

IBM can be a good option when you need:

• Monitoring for generative and predictive models.
• Quality, fairness, drift, explainability and performance metrics.
• Links between models, business processes and operational risks.
• Model factsheets and technical documentation.
• Governance in large or regulated organisations.

For banking, insurance, industry, healthcare, energy or critical infrastructure, that depth can matter. Where continuity, third-party or cybersecurity obligations also apply, connect this work with NIS2 audit preparation or sector-specific controls such as DORA for fintech.

5. OneTrust AI Governance

OneTrust AI Governance is a natural option for companies already using OneTrust for privacy, third-party risk, GRC or data governance. Its official AI Governance page describes inventories for models, datasets, agents and vendors, ownership assignment, automated risk tiering, mappings to frameworks such as the EU AI Act, NIST and ISO 42001, and compliance reporting.

Its main strength is connection with broader corporate processes. Many companies do not want an isolated AI tool. They want AI integrated into privacy, vendor risk, policies, workflows and executive reporting. That is where OneTrust can make sense.

OneTrust can be a good option when you need:

• A unified AI, privacy and third-party inventory.
• Approvals, attestations, reviews and exception workflows.
• Control mapping across several regulatory frameworks.
• Reporting for internal audit and management.
• Leverage from an existing enterprise suite.

The operational warning is real: broad suites require design. If configured without a clear model for owners, evidence and escalation criteria, they can add complexity. Before implementation, decide what information is required for each type of AI use and which evidence would be mandatory in a GDPR audit or compliance review.

6. ServiceNow AI Control Tower

ServiceNow AI Control Tower is especially relevant if the company already uses ServiceNow for IT, risk, assets, incidents, security or corporate workflows. The official documentation on the AI governance life cycle explains how AI Control Tower and AI Risk and Compliance can coordinate intake, registration, deployment, monitoring and value tracking. The product page also highlights discovery, inventory, lifecycle management, risk and compliance, case management and content for NIST AI RMF and the EU AI Act.

Its approach is different from a pure compliance tool: it turns AI into a governed asset within enterprise workflows. That matters when many teams are proposing automations, agents and models, and the organisation needs to know what is in ideation, review, production, exception or retirement.

ServiceNow can be a good option when you need:

• Lifecycle management for initiatives, models, agents and datasets.
• Connections with CMDB, assets, identities, incidents and services.
• Approvals and cases inside existing workflows.
• Value, adoption and risk metrics for the AI portfolio.
• Governance for proprietary and third-party systems from one place.

For large groups, this can reduce friction because governance happens inside familiar processes. For smaller companies, it is usually more infrastructure than needed unless ServiceNow is already in place.

7. Microsoft Purview and Compliance Manager

Microsoft Purview is not a complete AI governance platform, but it is an important layer for companies using Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, Teams, SharePoint, OneDrive or generative applications connected to corporate data.

Microsoft’s documentation on Purview for generative AI apps describes capabilities for AI usage risk, audit, retention, eDiscovery, DLP, data classification and compliance controls. Compliance Manager also includes premium templates for the EU AI Act, ISO/IEC 23894, ISO/IEC 42001 and the NIST AI RMF.

Microsoft can be a good option when you need:

• Auditability for interactions with supported copilots and generative AI apps.
• DLP, classification, retention and eDiscovery for prompts and responses.
• Reduced exposure of sensitive information in Microsoft environments.
• AI regulatory assessment templates.
• AI controls connected to Microsoft 365 security and compliance.

The limitation matters: Purview does not replace a full AI system inventory, legal classification, vendor review or governance of models outside the Microsoft ecosystem. In companies with heavy Copilot adoption, it should be seen as a data and security layer within a broader AI compliance and governance programme.

8. Holistic AI

Holistic AI focuses on discovery, testing, protection and enforcement for AI systems. Its platform is presented around three pillars: identify, protect and enforce, with capabilities for shadow AI detection, inventory, bias testing, red teaming, runtime guardrails, agent governance and alignment with the EU AI Act, ISO 42001 and the NIST AI RMF.

It can be powerful for companies that need more than questionnaires. If AI systems affect candidates, employees, customers, patients, pricing, fraud, credit or sensitive decisions, governance should include technical testing and lifecycle monitoring.

Holistic AI can be a good option when you need:

• AI discovery across cloud, code, data and SaaS tools.
• Testing for bias, fairness, robustness, security, toxicity or hallucinations.
• Governance for agents and autonomous workflows.
• Guardrails, deployment gates and enforcement controls.
• Compliance and assurance reports.

The key is to keep technical testing connected to decision-making. A bias or robustness test is only useful if it is linked to a use case, owner, risk classification, acceptance criterion and documented decision.

9. Securiti AI Security and Governance

Securiti is focused on the intersection of data, security and AI governance. Its AI Security and Governance solution includes model discovery and cataloguing across public cloud, private cloud and SaaS, Data+AI mapping, risk assessments, controls and compliance with frameworks such as the NIST AI RMF, the EU AI Act and other regulations.

It makes sense when the main AI risk is data exposure: personal information, confidential datasets, regulated records, trade secrets, internal documents or customer data. In many programmes, this is the real starting point because AI adoption often runs ahead of the company’s data inventory.

Securiti can be a good option when you need:

• Discovery of AI models and usage across cloud and SaaS environments.
• Mapping of which data feeds each system.
• Controls over personal or confidential data exposure.
• Connections between AI governance, privacy, data intelligence and security.
• Automated controls and compliance reporting.

For companies with heavy GDPR exposure, integrate it with records of processing, DPIAs, vendor reviews and minimisation controls. This layer can be prioritised after an organisation GDPR maturity review.

10. Vanta AI compliance workflows

Vanta is not a specialist AI governance platform in the same way as Credo AI, IBM or Holistic AI, but it can be useful for companies already using it for security, audits and customer trust. Its value is in turning controls, policies, evidence and workflows into a compliance operating system, especially for startups and SaaS scaleups.

Vanta makes sense when the company needs to answer enterprise customers, maintain security evidence, organise vendors and add an initial control layer over AI usage. It should not be treated internally as a complete AI Act solution if the company has high-risk systems, proprietary models or complex technical obligations.

Vanta can be a good option when you need:

• Evidence and security controls connected with AI use.
• Acceptable AI use policies inside a compliance programme.
• Customer questionnaire and audit readiness.
• Continuity with ISO 27001, SOC 2 or similar frameworks.
• No extra tool where AI maturity is still early.

For a SaaS scaleup, it can be a practical first layer when combined with an external regulatory assessment and a well-designed AI inventory. If AI is materially embedded in the product, it should be complemented with technical governance, risk evaluation and specific documentation.

The specific frameworks that change the purchase

AI governance software should not be bought as generic compliance software. Specific frameworks change the fields, workflows and evidence the platform needs.

AI Act. For high-risk systems, Article 9 requires a documented risk management system that runs throughout the lifecycle and is regularly reviewed. The European Commission also highlights requirements such as data quality, logging, technical documentation, information for deployers, human oversight, robustness, cybersecurity and accuracy. The software therefore needs lifecycle governance, not a one-off assessment.

ISO/IEC 42001. ISO/IEC 42001:2023 is not a software checklist. It is an AI management system standard. The tool should help operate policies, objectives, responsibilities, risk assessment, controls, internal audit and continual improvement. This is what separates AI governance software from an inventory spreadsheet.

ISO/IEC 23894. ISO/IEC 23894:2023 gives AI-specific guidance for integrating risk management into organisational activities and functions. For software, that means risk taxonomies, acceptance criteria, risk treatment, reviews and traceability.

NIST AI RMF. The NIST AI Risk Management Framework is voluntary, but widely used in enterprise procurement because it structures AI risk around functions such as govern, map, measure and manage. If you sell SaaS or AI to international customers, the platform should be able to map controls to NIST as well as the AI Act and ISO 42001.

GDPR and data protection. Where personal data is involved, AI governance must connect with DPIAs, lawful basis, transparency, automated decision-making, rights, minimisation and security. This is where the DPO’s role in AI projects and EU DPO responsibilities become part of the operating model.

The AI governance architecture inside the software

A strong AI governance programme is not organised by “modules”. It is organised by decision layers. The platform should let the company view every AI system from six angles:

1. Portfolio layer. Which systems, copilots, agents, APIs, models, datasets and vendors exist.
2. Accountability layer. Who owns the business use, technical operation, privacy, security, procurement and day-to-day monitoring.
3. Regulatory layer. Which frameworks apply: AI Act, GDPR, ISO 42001, ISO 23894, NIST AI RMF, ISO 27001, NIS2 or DORA.
4. Control layer. Which controls must run before, during and after deployment.
5. Evidence layer. Which proof demonstrates approval, testing, training, oversight, changes and incidents.
6. Governance layer. What goes to management, the AI committee, DPO, CISO, product, internal audit or enterprise customers.

This architecture is specific to AI governance because risk changes with use. The same model may be low risk in an internal assistant and far more sensitive when used for hiring, healthcare prioritisation, fraud detection or financial recommendations.

The workflow a company should configure

The difference between a serious platform and a polished repository is the workflow. For AI governance, the minimum circuit should have seven gates:

1. Request. The team proposes a tool, model, agent or use case with purpose, data, users, vendor and expected value.
2. Triage. Compliance or the AI owner decides whether it is AI in scope, simple automation, AI-enabled SaaS or a material change to an existing system.
3. Classification. The company role, risk category, personal data, impact on people, vendor dependency and possible sensitive-sector use are documented.
4. Assessment. Privacy, security, procurement, legal, product and business teams enter according to objective criteria. Not every use case should go to the same committee.
5. Decision. The use case is approved, approved with conditions, blocked or returned because the evidence is incomplete.
6. Controlled deployment. Controls are activated: instructions for use, human oversight, limits, logging, testing, training and metrics.
7. Continuous review. Changes in purpose, model, dataset, vendor, autonomy, affected population and incidents are reviewed.

This workflow is closer to regulatory compliance automation than to a static checklist. The goal is for every AI decision to leave a trace from idea to retirement.

Agent governance and shadow AI

This topic deserves its own section because it is changing software selection. Companies no longer only have models that answer. They have agents that query documents, call APIs, execute actions, use connectors, access internal data or interact with other tools.

For agents, the software should record more than the model name. It should document:

• Tools and systems the agent can access.
• Identity used by the agent and permissions granted.
• Autonomy limits and prohibited actions.
• Data the agent can read, modify or transfer.
• Decision, action and error traces.
• Guardrails, prompt injection tests and stop criteria.
• The accountable owner when the agent behaves incorrectly.

Shadow AI also changes the purchase. If employees use generative tools outside the formal process, the issue is not just “missing inventory”. There may be personal data, trade secrets, contractual breaches, code leakage, unapproved vendors or automated decisions without oversight. In Microsoft 365 environments, Microsoft Purview can cover part of the data and prompt risk. In broader environments, discovery, vendor management and acceptable-use governance become essential.

The evidence each stakeholder needs

An AI governance platform should generate different evidence depending on who is asking. This is where the category is different from ordinary compliance tooling: the value is not the inventory itself, but its translation into proof for each audience.

Management. Total exposure, critical systems, accepted risks, blocked decisions, incidents, remediation cost and AI portfolio value.

DPO and privacy. Purpose, personal data, lawful basis, DPIA, transparency, vendors, security measures and links to processing records.

CISO and security. Access, sensitive data, logging, DLP, vulnerabilities, prompt injection, identity governance, cloud vendors and incidents.

Product and engineering. Requirements, acceptance criteria, tests, versioning, substantial changes, instructions for use, limitations and monitoring.

Internal audit or certification. Dated evidence, owners, reviews, controls, nonconformities, corrective actions and traceability with ISO 42001.

Enterprise customers. Clear due diligence answers: which AI is used, what data it touches, which vendors are involved, which controls exist, how incidents are managed and how training is demonstrated, especially if the supplier already goes through GDPR audits or security certifications.

Selection criteria

The buying criterion should not be “which platform has the most frameworks”. It should be whether the software can operate an AI governance system.

1. Can it design lifecycle gates? The tool should distinguish idea, pilot, approval, production, material change, incident and retirement.
2. Does it distinguish systems, models, agents, datasets and vendors? Putting everything under “use cases” is usually too shallow.
3. Does it separate provider, deployer and internal buyer roles? Legal classification should appear in the workflow.
4. Can it connect AI Act, ISO 42001, ISO 23894, NIST AI RMF and GDPR? One framework is not enough.
5. Can it generate evidence packs by audience? Management, audit, DPO, CISO and customers ask for different proof.
6. Does it control material changes? A change in purpose, model, dataset, vendor or autonomy should reopen the assessment.
7. Does it support agent governance? In 2026, a platform without traces, permissions, guardrails and agent actions can fall short.
8. Does it integrate with procurement and security? Many AI risks enter through SaaS, not the data science team.
9. Can evidence be exported? The company should not be locked in if it changes tools.
10. Does it reduce work or merely move it? If the software does not simplify decisions, owners and evidence, it will probably add bureaucracy.

Final recommendation

For AI governance and regulatory compliance, the decision does not start with “tools with more checkboxes”. It starts with a more specific question: what governance system does the company need to control AI as a living portfolio of decisions, risks, changes and evidence?

If the main problem is shadow AI, prioritise discovery, DLP, vendors and acceptable-use governance. If the problem is AI in the product, prioritise testing, documentation, lifecycle and change control. If the problem is audit or enterprise customers, prioritise exportable evidence and mappings to ISO 42001, the AI Act, GDPR and security. If the problem is agents, prioritise permissions, traces, actions, guardrails and oversight.

PrivaLex can help design that model before you invest: governance architecture, workflows, regulatory matrix, functional requirements, evidence and certification or due diligence readiness.

Request a free risk assessment and we will review what your AI programme needs to become governable, demonstrable and useful for the business.

Frequently asked questions