The 8 best tools for EU AI Act compliance in companies

These are the best tools to use for EU AI Act Compliance

1. PrivaLex
2. OneTrust
3. Credo AI
4. Holistic AI
5. IBM watsonx.governance
6. Microsoft Purview
7. ServiceNow
8. Securiti

Companies searching for the best tools for EU AI Act compliance are usually dealing with practical pressure: customers requesting evidence, employees adopting generative AI, vendors embedding AI into their services or business units deploying systems without a formal approval process.

The right tool is not always the best-known platform. The European Commission’s overview of the EU AI Act shows that organisations must consider risk classification, provider and deployer responsibilities, transparency, documentation, human oversight and controls for high-risk systems.

A useful compliance tool should therefore support more than policy storage. It should help the organisation maintain an AI inventory, classify risks, assess vendors, assign responsibilities, manage approvals and produce evidence over time.

8 Tools You Can Use for EU AI Act Compliance

1. PrivaLex

PrivaLex is a compliance and information security consultancy rather than a SaaS platform. It should appear first when companies are comparing tools because technology selection depends on the compliance model behind it.

Before choosing OneTrust, Credo AI, Holistic AI, IBM, Microsoft, ServiceNow or Securiti, the organisation should understand:

• Which AI systems exist.
• Which systems are provided by vendors.
• Which systems are developed internally.
• What data each system uses.
• Which role the organisation performs.
• What risks need to be assessed.
• Which evidence must be retained.
• Who will operate the governance programme.

PrivaLex helps turn those requirements into practical purchasing and configuration criteria. Its work can connect the AI Act with GDPR, ISO 42001, information security, procurement and customer assurance.

Companies beginning with a broader AI regulatory compliance programme can receive support with:

• AI system inventories.
• Risk classification.
• Provider and deployer responsibilities.
• Internal approval workflows.
• Vendor assessments.
• Human oversight controls.
• Incident management.
• Documentation and recordkeeping.
• Management reporting.
• Customer and audit evidence.

The consultancy also supports organisations building an AI management system based on ISO 42001. This can include defining the management system scope, identifying relevant stakeholders, establishing objectives and creating a repeatable process for assessing AI risks. Companies can learn more about ISO 42001 and AI governance before deciding whether they need a dedicated platform.

The EU AI Act and ISO 42001 serve different but complementary purposes: the AI Act establishes legal obligations, while ISO 42001 helps structure responsibilities, controls and continual improvement within a management system. Together, they provide a practical framework for aligning AI Act compliance with ISO 42001. 

PrivaLex fits companies that need:

• Functional requirements before purchasing software.
• An objective comparison of AI governance tools.
• Help configuring an existing platform.
• Alignment between legal, product, security and procurement teams.
• Evidence for enterprise customers, audits or management.
• Practical implementation support without building a large internal compliance department.

The right software can accelerate compliance. However, if the organisation does not know what it must prove, which controls it needs or what data feeds its AI systems, the platform may create another layer of confusion.

2. OneTrust

OneTrust is a broad privacy, data governance, third-party risk and compliance platform. It can be suitable for organisations that already use a central compliance suite and want to connect AI governance with existing privacy and risk processes.

Its AI governance use cases may include:

• AI system cataloguing.
• Risk assessments.
• Use case reviews.
• Vendor assessments.
• Policy management.
• Approval workflows.
• Evidence collection.
• Reporting for management and audit.

OneTrust is a natural fit when AI systems process personal data, rely on external vendors or need to be reviewed alongside data protection impact assessments and records of processing activities.

It can also make sense for mature compliance teams that already have defined owners, risk criteria and internal review procedures. The main challenge is complexity. A large suite cannot compensate for missing processes or unclear accountability.

3. Credo AI

Credo AI focuses specifically on AI governance. It may be appropriate for organisations that want a specialist platform for AI policies, assessments, approvals and evidence rather than a general privacy or GRC system.

Its potential use cases include:

• Maintaining an AI systems register.
• Creating AI-specific assessments.
• Managing approval workflows.
• Recording governance decisions.
• Coordinating legal, compliance, data and product teams.
• Producing evidence for internal audits and customers.

Credo AI may be especially useful where AI use cases are distributed across product, marketing, HR, operations and customer support.

Before choosing it, organisations should review how it will integrate with existing privacy, security, GRC, MLOps and procurement systems. Without those connections, the company may create another parallel repository.

4. Holistic AI

Holistic AI can be useful when an organisation needs technical depth in model assessment and assurance. Its focus may include bias, fairness, robustness, explainability, documentation and model controls.

This type of support is particularly relevant for systems used in:

• Recruitment.
• Credit.
• Insurance.
• Education.
• Healthcare.
• Public services.
• Access to essential services.

For these systems, a policy alone is not enough. The organisation may need to demonstrate testing, human review, traceability, performance monitoring and mitigation measures.

Holistic AI fits organisations that need:

• Technical assessments of AI systems.
• Evidence about fairness and robustness.
• Explainability documentation.
• Assurance reports for customers.
• Specialist assessments connected to a broader GRC or privacy programme.

It may not be suitable as the organisation’s only compliance system. Many companies will need to connect this technical assurance layer with governance, vendor management and management system processes.

5. IBM watsonx.governance

IBM watsonx.governance is designed for larger organisations managing multiple AI assets, policies, risks, controls and reporting requirements.

It may be relevant where AI governance must operate alongside:

• Enterprise risk management.
• Operational resilience.
• Third-party risk.
• Business continuity.
• Internal audit.
• Security operations.
• Global compliance functions.

The platform may provide value through:

• AI asset visibility.
• Policy and control management.
• Risk tracking.
• Governance reporting.
• Accountability records.
• Production monitoring.
• Audit preparation.

Its trade-off is the level of maturity required. Smaller companies may not need a broad enterprise governance architecture if they are still trying to identify their AI systems and define basic approval criteria.

6. Microsoft Purview

Microsoft Purview can support companies that already use Microsoft 365, Azure, Teams, SharePoint and other Microsoft services.

Its value for AI governance is often connected to the data and security layer, including:

• Information classification.
• Sensitivity labels.
• Retention.
• eDiscovery.
• Permissions.
• Data loss prevention.
• Access controls.
• Compliance reporting.

Many AI risks begin with data. Sensitive personal information, confidential business information, prompts, logs and outputs may all require appropriate classification and access controls.

Microsoft Purview can therefore provide a useful foundation for AI governance in a Microsoft environment. However, it should not automatically be treated as a complete AI Act solution. Organisations may still need separate processes for:

• Risk classification.
• Provider and deployer responsibilities.
• Human oversight.
• AI-specific documentation.
• Model changes.
• Performance monitoring.
• ISO 42001 implementation.

7. ServiceNow

ServiceNow can be appropriate when the organisation already uses it for risk, compliance, audit, incidents, workflows and corporate operations.

Extending the existing environment to AI governance may be more efficient than introducing a separate AI platform. It can help teams:

• Assign actions.
• Manage approvals.
• Track exceptions.
• Review controls.
• Escalate incidents.
• Collect evidence.
• Report to management committees.

The main limitation is that generic GRC structures do not always capture AI-specific information well. The organisation may need a dedicated taxonomy for:

• Models.
• Datasets.
• Agents.
• Prompts.
• AI use cases.
• Human oversight.
• Material changes.
• Performance metrics.
• Provider and deployer roles.

ServiceNow can work well when AI governance is treated as part of the existing risk programme, but it requires careful configuration.

8. Securiti

Securiti may be relevant when the main AI compliance risks involve personal data, sensitive information, multi-cloud environments and external vendors.

Its potential use cases include:

• AI system discovery.
• Data flow mapping.
• Data classification.
• Privacy risk management.
• Data subject rights.
• AI governance.
• Vendor and data intelligence.
• Multi-cloud visibility.

Securiti may fit companies where AI compliance overlaps heavily with GDPR. Organisations using AI with personal data may need to connect governance with retention, lawful bases, transparency, access rights and data minimisation.

A dedicated GDPR privacy automation and operations readiness checklist can help identify the operational processes that should connect to the AI governance programme.

Securiti does not replace legal judgement or governance design. It is most useful when the organisation already understands its privacy obligations and needs technology to manage data and evidence at scale.

What Problem Does Each Type of Tool Solve?

Not every AI Act tool addresses the same problem. Before requesting demonstrations, identify the layer that needs improvement.

AI-specific governance

Tools such as Credo AI and Holistic AI focus on AI inventories, risk assessments, approvals, technical assurance and governance evidence.

They may be useful when the organisation has many AI systems and needs a consistent review process across product, operations, HR and customer-facing services.

Corporate GRC and compliance

OneTrust, ServiceNow and IBM can fit organisations that want to connect AI governance with existing risk, compliance, audit and control processes.

This approach may be efficient when the organisation already has a mature GRC structure. Companies still designing their governance model should first define what the platform must manage, including inventories, risk assessments, approvals, controls and evidence. That foundation is essential when evaluating AI governance compliance software. 

Privacy and data governance

Microsoft Purview and Securiti may be more relevant when the main risk involves personal data, information classification, retention, access or cloud data flows.

This is important because AI Act compliance often overlaps with GDPR obligations. A company may need to understand not only which AI system exists, but also which personal data it uses, where that data goes and how individuals are informed.

Programme design and implementation

PrivaLex fits before, during and after technology selection. It helps define what the organisation needs to prove, which controls are required and how the tool should connect with the AI Act, GDPR, ISO 42001 and customer requirements.

Buying Matrix for Companies

Before requesting vendor demonstrations, the company should answer the following questions precisely.

Which AI systems exist?

The inventory should include:

• Internally developed models.
• SaaS tools with embedded AI.
• Copilots.
• Autonomous or semi-autonomous agents.
• Scoring and recommendation systems.
• Workflow automations.
• AI features integrated into products.
• Tools used informally by employees.

The inventory should distinguish between systems, models, agents, datasets and vendors. Treating every item as a generic “use case” can make risk assessment too shallow.

Which data do the systems use?

Consider:

• Personal data.
• Special category data.
• Confidential business information.
• Training data.
• Prompts.
• Logs.
• Outputs.
• Customer data.
• Supplier information.
• Data transferred outside the EU.

A company’s wider SaaS environment should also be assessed because AI features are increasingly embedded in business applications. This is particularly important when reviewing the privacy risks that SaaS companies often overlook.

Which role does the organisation perform?

A company may be:

• An AI provider.
• A deployer.
• A distributor.
• An importer.
• An integrator.
• A software buyer.
• An internal user.
• Several of these at the same time.

The role affects responsibilities, documentation and the controls that need to be implemented.

Which evidence is required?

The organisation may need to retain:

• Risk assessments.
• System inventories.
• Approval records.
• Human oversight decisions.
• Technical tests.
• Vendor assessments.
• Training records.
• Meeting minutes.
• Incident reports.
• Monitoring results.
• Change approvals.
• Policies and procedures.

Where personal data is involved, evidence should connect with the organisation’s wider privacy programme. A structured GDPR audit can help identify gaps in accountability, documentation and operational controls.

Which tools already exist?

Review existing:

• Corporate GRC.
• DPO and privacy tools.
• Ticketing systems.
• MLOps platforms.
• Cloud security tools.
• Identity and access management.
• Data catalogues.
• Procurement systems.
• Microsoft services.
• Internal audit repositories.

The goal is not to create a separate system for every obligation. The better approach is to understand which system should own each type of information and how evidence will move between them.

Who will operate the programme?

Possible owners include:

• Legal.
• Compliance.
• The DPO.
• Security.
• Product.
• Data science.
• Procurement.
• Internal audit.
• Human resources.
• A cross-functional AI committee.

If the company cannot answer these questions, vendor demonstrations may look attractive but remain difficult to implement.

Where PrivaLex Fits in an AI Act Compliance Programme

PrivaLex does not replace the AI governance platform. It helps the organisation choose, configure and operate that platform with appropriate judgement.

This can include translating AI Act obligations into:

• Inventory requirements.
• Risk classification criteria.
• Approval workflows.
• Control objectives.
• Vendor assessment questions.
• Human oversight procedures.
• Evidence requirements.
• Reporting structures.

PrivaLex can also help organisations connect AI compliance with existing privacy and security frameworks. For example, an organisation may need to demonstrate that AI-related risks are included in its information security risk assessment, that controls are documented and that treatment decisions are reviewed by responsible owners.

Where companies are preparing for NIS2 or DORA as well as AI regulation, the controls should be designed together where possible. Organisations can also review how to prepare for a NIS2 audit and how NIS2 and DORA interact.

PrivaLex can support organisations that:

• Are selecting their first AI governance platform.
• Already use a GRC platform that needs AI-specific configuration.
• Need to connect AI governance with GDPR.
• Need to prepare for ISO 42001.
• Need to document controls for customers.
• Need support with vendor and procurement processes.
• Have AI systems but no complete inventory.
• Need audit-ready evidence.

The value is not simply choosing a software product. It is creating a governance process that the organisation can operate consistently as AI systems, vendors and regulations change.

Implementation Plan: 30, 60 and 90 Days

The first 30 days: Build the baseline

The organisation should:

• Create an initial AI inventory.
• Identify critical systems.
• List internal and external AI vendors.
• Identify personal and confidential data.
• Determine provider and deployer roles.
• Appoint internal owners.
• Define the initial scope.
• Identify urgent risks.

The organisation should avoid configuring a large platform before it understands the information that needs to be managed. 

Days 31 to 60: Define controls and workflows

During the second phase, the company should:

• Classify AI systems by risk.
• Define minimum control requirements.
• Create approval workflows.
• Document human oversight.
• Establish vendor assessment criteria.
• Map required evidence.
• Decide which information will be managed in the platform.
• Assign review and escalation responsibilities.

The organisation should also document how risks will be treated. A practical ISO 27001 risk treatment plan can provide a useful structure for assigning actions, owners, deadlines and verification evidence.

Days 61 to 90: Test and improve the process

The company should test the governance process with real use cases.

This may include:

• A high-impact internal use case.
• A generative AI tool used by employees.
• A vendor providing an AI-enabled SaaS product.
• A customer-facing AI feature.
• An AI system processing personal data.
• A material change to an existing model.

The test should confirm whether the platform can support the full process from inventory to approval, monitoring, evidence and review.

Internal Roles That Should Participate

EU AI Act compliance does not belong only to legal or IT. A useful governance tool should allow several teams to participate without duplicating work.

Legal and compliance

These teams define regulatory obligations, risk criteria, approval requirements and evidence expectations.

Privacy and the DPO

The DPO or privacy team should review how personal data is collected, used and shared across the organisation. This includes transparency notices, data subject rights, DPIAs, lawful bases, retention periods, processor relationships and international transfers. The review should also confirm that privacy decisions are documented and that business teams know when they need to involve the DPO.

Depending on the organisation’s size, activities and processing operations, it may also need to determine whether an external DPO is required. An external DPO can provide independent advice and ongoing oversight where the organisation lacks sufficient internal expertise or needs additional capacity for complex processing activities.

Security

Security teams should assess access, logging, vendors, vulnerabilities, incidents, resilience and continuity.

They should also confirm that AI systems are covered by the organisation’s broader security controls and incident response process.

Product and data teams

Product and data teams understand the purpose, datasets, model behaviour, performance indicators, limitations and material changes affecting each system.

Procurement and vendor management

Procurement should review contracts, subprocessors, data use, vendor responsibilities, model changes and termination arrangements.

AI risks often enter through SaaS products rather than through internally developed data science systems.

Management

Management should decide the organisation’s risk appetite, priorities, resources and escalation thresholds.

A governance platform should support management reporting without requiring executives to interpret technical system details.

How to Avoid an AI Act Tool Mismatch

Define the result before comparing brands

Decide whether the main objective is inventory, risk classification, technical assurance, privacy management, GRC integration, vendor governance or audit evidence.

A platform selected without a defined objective may appear comprehensive while remaining difficult to operate.

Check whether the tool supports the organisation’s taxonomy

The platform should distinguish between:

• Systems.
• Models.
• Agents.
• Datasets.
• Vendors.
• Use cases.
• Deployers.
• Providers.
• Material changes.
• Incidents.

A generic asset register may not be enough for AI governance.

Test the evidence workflow

Ask the vendor to demonstrate how the tool handles a real system from initial registration through assessment, approval, monitoring, change and retirement.

Review whether the platform can produce evidence for:

• Management.
• Internal audit.
• The DPO.
• The CISO.
• Customers.
• Regulators.

Review integration and ownership

Confirm whether the platform integrates with GRC, privacy, security, MLOps, procurement, ticketing and identity systems.

Also confirm who will maintain each integration and what happens when the organisation changes systems.

Plan for incidents and material changes

AI governance does not end when a system is approved. The organisation should define what happens when:

• The model changes.
• The purpose changes.
• The dataset changes.
• The vendor changes.
• The system becomes more autonomous.
• Performance deteriorates.
• A security or privacy incident occurs.

The process should connect with the organisation’s wider incident response procedures. A practical GDPR data breach response template can help structure investigation, notification decisions, remediation and lessons learned.

Document controls instead of relying on platform status

A dashboard showing “compliant” is not the same as evidence that a control operates effectively.

The organisation should maintain documented policies, procedures, review records, approvals, tests and corrective actions. Guidance on documenting ISO 27001 controls can help establish a more reliable evidence structure.

Final Recommendation

Companies should not buy AI governance software before they understand which AI systems exist, what risks they create and what evidence they need to retain.

If AI is already being used in products or operations, choose a tool that connects inventory, controls, vendors, approvals and reporting.

If the organisation already has corporate GRC, assess whether it can support AI-specific information before adding another platform.

If the main risks involve personal data, connect AI governance with GDPR, privacy operations and security controls.

PrivaLex can help define the compliance map, establish functional requirements, configure an existing platform and create an implementation process that the organisation can maintain. The objective is not simply to purchase another tool, but to create an AI governance programme that produces useful decisions and defensible evidence.

Frequently asked questions