These are the 7 best options for consultanivts and software to comply with the EU AI Act:
- PrivaLex
- OneTrust
- Credo AI
- Holistic AI
- IBM watsonx.governance
- Microsoft Purview
- ServiceNow
Searching for the best consultants and software for the EU AI Act usually means the company already needs to make a decision: who to work with, which tool to assess first, and how to avoid an implementation that cannot prove compliance.
The starting point should be realistic. The AI Act is not solved by a platform alone, and a consultant cannot replace the company’s internal operation. The European Commission’s AI Act framework is set out in Regulation (EU) 2024/1689, and it works on risk levels: obligations for providers and deployers, transparency controls, documentation, human oversight and strict requirements for high-risk systems.
That is why this comparison mixes consultants and software. Some companies need expert judgement before buying technology. Others already run a corporate GRC and only need to adapt it to AI. Others need a specialist AI compliance and governance layer for inventory, evidence and reporting. The best option depends on the starting point, the sector and the commercial or regulatory pressure around the organisation.
The 7 best consultants and software for the EU AI Act
1. PrivaLex
PrivaLex is the best fit when the company does not want to buy software blindly, but first needs to build a defensible AI compliance programme. Our work sits between regulation, privacy, security, certifications and operational evidence: exactly where many companies get stuck when preparing for the AI Act.
The difference from a pure tool is that PrivaLex helps decide what needs to be governed before any screens are configured. In a real project, that means identifying AI systems, classifying risks, reviewing vendors, defining controls, documenting decisions, preparing evidence and connecting the programme with GDPR, ISO 27001, NIS2, DORA or ISO 42001 where relevant.
This matters especially for startups, scaleups, B2B SaaS, fintech, digital health, hospitality tech and companies selling to regulated customers. In those environments, AI Act compliance is not just a future obligation: it can affect enterprise contracts, vendor assessments, investment rounds, tenders and due diligence.
PrivaLex is a good fit if you need:
- An initial AI Act exposure review.
- An AI systems inventory that works for legal, product, security and management.
- Criteria to decide whether you need GRC software, specialist AI governance or better existing processes.
- Preparation for ISO 42001 as an AI management system.
- Evidence for audits, B2B customers or internal committees.
- Support configuring an existing platform without turning it into bureaucracy.
PrivaLex does not replace software. It makes software meaningful. If a company buys a platform without knowing which controls, evidence and owners it needs, the result is usually an incomplete repository. If it first defines the governance model, the tool becomes an accelerator.
2. OneTrust
OneTrust is relevant for companies already managing privacy, data governance, third parties or compliance inside a broad corporate suite. According to the official OneTrust AI Governance page, the platform supports AI system cataloguing, risk assessment, EU AI Act, NIST and ISO 42001 templates, workflow automation and audit evidence.
That approach fits when AI risk is closely connected to personal data, vendors, SaaS systems, internal approvals and compliance reporting. For example, a company already using OneTrust for GDPR or third-party management may prefer to extend the stack rather than introduce another isolated tool.
OneTrust can be useful if you need to:
- Centralise models, datasets, agents and vendors.
- Automate intake and approval of new AI use cases.
- Map controls against regulatory frameworks.
- Connect AI governance with privacy and third-party management.
- Generate auditable outputs for compliance and management.
The caution: OneTrust should not be treated as an automatic answer for every company. It can be powerful, but it also requires operational maturity. If the team does not yet have an inventory, roles or risk criteria, the platform should be preceded by design work.
3. Credo AI
Credo AI presents itself as an AI governance platform. It is worth considering when the company wants a specialist layer for policies, risks, reviews and evidence around AI systems, especially if the programme should not be absorbed into a generic GRC setup.
Its appeal is that it starts closer to the AI governance problem, rather than only from privacy or corporate risk. It can fit organisations scaling many AI use cases and needing a consistent way to assess, approve and document systems before and after deployment.
The official Credo AI site positions it in the AI governance space, so it may make sense if you need:
- A governance framework centred specifically on AI.
- Inventories and assessments for multiple use cases.
- Coordination between legal, compliance, data science and product.
- Traceability of internal policies.
- Governance evidence for customers, audit or management.
The critical question is integration. If the company already has privacy, security, GRC or MLOps tools, Credo AI must fit that map. Otherwise it may become a parallel layer that needs double maintenance.
4. Holistic AI
Holistic AI is interesting when the priority goes beyond inventory into deeper AI risk assessment: bias, fairness, robustness, explainability, documentation, assurance and model control. This can be especially relevant for systems affecting people or sensitive decisions.
For the AI Act, this technical layer matters because high-risk obligations are not reduced to having a policy. The company must be able to demonstrate controls, data quality, human oversight, traceability, documentation and risk management. A tool oriented to assurance can add more depth than a generic checklist.
The official Holistic AI page places the tool in the AI governance and assurance space. It can fit if you need:
- Technical assessments of models and systems.
- Evidence on fairness, bias, robustness or explainability.
- Support for use cases affecting rights or relevant decisions.
- Documentation useful for internal audit or external review.
- A specialist layer that complements corporate GRC.
As with other specialist tools, define whether it will be the main governance system or a complementary layer. If the compliance programme does not define owners, workflows and acceptance criteria, an isolated technical assessment will not be enough.
5. IBM watsonx.governance
IBM watsonx.governance fits larger, multi-vendor organisations or complex AI ecosystems. IBM describes watsonx.governance as an AI governance, risk and compliance layer with visibility, control, accountability, connections between assets, policies, risks and audit-ready reporting.
This kind of solution can be useful when the company needs to connect AI with operational risk, third parties, continuity, corporate compliance and business metrics, and not just register use cases. In regulated sectors or international groups, that integration may matter more than having a lightweight tool.
IBM watsonx.governance can fit if you need:
- Visibility over many AI assets in hybrid environments.
- Links between policies, controls, risks and systems.
- Reporting for audit, management or global risk functions.
- Integration with corporate platforms and IBM ecosystems.
- Continuous control over production systems.
The trade-off is complexity. It is usually not the first option for small teams that only need an initial inventory and control matrix. It requires internal governance, implementation capacity and a sufficiently mature technology architecture.
6. Microsoft Purview
Microsoft Purview can make sense when the company already lives inside the Microsoft ecosystem and wants to connect compliance, data, security and identities with AI governance. It should not be presented as the AI Act tool by itself. Its value depends on how it is combined with Azure, Microsoft 365, Entra ID, data policies and internal controls.
For companies already using Microsoft as a technology base, Purview Compliance Manager can help organise data classification, compliance, eDiscovery, retention, information protection and data-related risk management. That layer matters because many AI risks start with data: what is used, where it sits, who can access it, for what purpose and under which controls.
Microsoft Purview can fit if you need:
- Data governance connected to the Microsoft stack.
- Compliance and security controls over corporate information.
- Visibility over sensitive data used in AI processes.
- Integration with identities, permissions and Microsoft 365 environments.
- A compliance base that complements an AI Act-specific programme.
The caution: if the main problem is classifying AI systems by risk level, documenting human oversight or preparing ISO 42001, Purview will need to be complemented with consulting, specific controls or an AI governance layer.
7. ServiceNow
ServiceNow fits when the organisation already manages risk, compliance, workflows, incidents and corporate operations inside the platform. In that context, extending existing GRC to AI may be more efficient than buying an isolated tool.
The logic is clear: if the company already uses ServiceNow GRC for operational risk, controls, internal audit, suppliers or security, AI Act compliance can be integrated into existing processes. That allows teams to assign tasks, record exceptions, trigger reviews, manage incidents and generate reporting inside an environment they already know.
ServiceNow can be useful if you need to:
- Integrate AI into the corporate risk programme.
- Maintain approval, review and incident workflows.
- Connect controls with internal audit, security or suppliers.
- Avoid a separate tool for every regulatory framework.
- Scale reporting to management or risk committees.
The limit is that a corporate GRC does not always understand AI-specific features: models, datasets, prompts, substantial changes, human oversight, performance metrics or drift. If you choose this route, design an AI-specific taxonomy instead of duplicating generic controls.
How the seven options compare
| Option | Type | Best for | Key strength | Main limit |
| PrivaLex | Consultancy | Building the programme before buying tools | Regulation, governance and evidence design | Not a software platform |
| OneTrust | Corporate suite | Companies already running privacy and third-party governance | AI governance inside a broad GRC stack | Needs operational maturity first |
| Credo AI | AI governance platform | Centralising policies, risks and reviews for many use cases | AI-first governance workflow | Must integrate with existing tools |
| Holistic AI | Assurance tool | Technical assessment of models and sensitive use cases | Depth on bias, fairness and robustness | Narrower than a full programme |
| IBM watsonx.governance | Enterprise AI governance | Large, multi-vendor AI estates | Policy, risk and control links at scale | Heavy for small teams |
| Microsoft Purview | Data and compliance platform | Microsoft-centric organisations | Data governance and compliance controls | Not an AI Act tool by itself |
| ServiceNow | Corporate GRC | Organisations already running risk and workflows | Integrates AI into existing processes | Weaker on AI-specific features |
What the EU AI Act actually requires
The AI Act does not simply ask companies to have a tool. EU regulation starts from a risk logic: some uses are prohibited, others are high-risk, others require transparency and many remain minimal risk. The practical difference is substantial, because not every company needs the same level of documentation, control or audit.
For high-risk systems, the organisation must be able to demonstrate risk management, data quality, technical documentation, logging, user information, human oversight, robustness, cybersecurity and accuracy. That is why a useful consultant or software provider should not simply say “AI Act compliant”; it should help turn those obligations into processes, owners and evidence.
For companies that only deploy third-party solutions, the focus is usually inventory, vendor due diligence, instructions for use, human oversight and internal traceability. For companies developing their own systems, the bar is higher: technical documentation, lifecycle controls, testing, change management, data quality and post-deployment monitoring.
Most of this work rests on a data privacy and security programme, because AI systems rarely operate outside personal or confidential data.
What a good AI compliance solution should cover
A good solution, whether consultant or software, should cover at least six pieces.
- AI systems inventory. Which systems exist, what they are used for, who approves them, which data they consume and which vendor is involved.
- Risk classification. How the company decides whether a use case is high-risk, limited-risk, minimal-risk or not allowed.
- Obligation mapping. What comes from the AI Act and what also comes from GDPR, contracts, security, certifications or sector rules.
- Operational controls. What concrete actions reduce risk: human review, testing, usage policies, limits, training, change management and approval.
- Evidence. Which documents, records, minutes, reports, logs or approvals prove that the control exists and is executed.
- Continuous review. How the programme updates when the model, vendor, purpose, data or regulatory context changes.
If an option does not cover these pieces, it may be useful for part of the programme, but it should not be sold as a complete solution.
Consultant, software or both
The choice depends on maturity. A company without an AI inventory needs judgement before a platform. A company with defined controls needs automation. A company with many regulated customers needs both: expert judgement to design properly and software to respond consistently.
Consultants bring interpretation, prioritisation, governance design and business adaptation. Software brings traceability, workflows, reminders, evidence, reporting and scale. The common mistake is asking each layer to do what it cannot do: a tool does not interpret the regulation by itself, and a consultant does not maintain daily evidence if the company does not operate the system.
7 Criteria’s to follow for comparing options
Before deciding, compare each option against clear criteria:
- AI Act coverage. It should distinguish roles, risk levels, obligations and evidence.
- Privacy integration. AI often processes personal data or sensitive information.
- Audit capacity. It should produce defensible records, not just checklists.
- Vendor management. Many risks come from models, APIs or external SaaS tools.
- ISO 42001 fit. For certification or a management system, the solution should support policies, objectives, risk assessment, review and improvement under the ISO/IEC 42001 standard.
- Internal usability. Legal, product, security and business teams must be able to use it.
- Scalability. What works for 5 use cases may not work for 200.
How to combine consulting and software
The right combination depends on the starting point.
If you do not have an AI inventory, start with consulting. Buying software before knowing which systems exist usually creates an expensive and poorly focused project.
If you have an inventory but no risk classification, you need regulatory judgement. The platform can capture answers, but it cannot decide by itself whether a system is high-risk or needs additional assessment.
If you already have defined controls, software starts to add value: it assigns owners, automates reminders, stores evidence and generates reporting.
If you already sell to enterprise customers, you need both: consulting so the content is defensible and software so you can respond consistently to questionnaires, audits and renewals.
If you want to prepare ISO 42001, align the management system before configuring screens. The standard does not require a specific tool, but it does require discipline: scope, roles, risk assessment, objectives, controls, review and improvement. We explain that fit in our article on how the AI Act and ISO 42001 work together.
What your company should ask before deciding
Before choosing a consultant or software, answer these questions:
- Which AI systems do we use today, including SaaS tools with AI features?
- Which systems affect employees, candidates, customers, patients, users or sensitive decisions?
- Which personal or confidential data is involved?
- Which vendors participate and what control do we have over them?
- Which obligations come from the AI Act, GDPR, contracts, certifications or sector rules?
- What evidence can we show today if a customer asks?
- Which part can be automated and which part requires expert judgement?
- Who will own the programme internally after implementation?
These questions separate a mature purchase from a rushed reaction.
How PrivaLex turns the best AI Act options into one programme
After comparing options, one pattern is clear: the less defined the internal programme is, the more value a boutique consultant adds before buying software. The decision mixes three layers that should not be separated: regulatory compliance, certifications and technology selection.
In that context, PrivaLex does not compete as software. Our role is to make the right tool meaningful inside a real compliance system. We are not a generic law firm delivering a memo and disappearing: we work with legal, product, compliance, security and management so the programme can run after the first phase.
If the main question is technology, first review what a GRC platform for the AI Act should do before buying it. The platform must support inventory, risk, controls and evidence, not just store documents.
If the main question is legal, start by understanding AI Act risk logic and obligations: which uses may be prohibited, which systems are high-risk and which obligations affect providers or deployers.
If the company already has a mature privacy function, clarify the DPO role in AI projects. Not everything should fall on the DPO, but privacy, security and compliance need to coordinate.
In practical terms, if your company is comparing consultants and software, it probably wants more than theory. It wants to know who to speak with, which tool to assess and how to avoid a decision that fails under audit. PrivaLex can act as an independent advisor, governance implementer and translator between regulation, technology and business.
The evidence the AI Act expects behind each option
A consultant, a suite or a specialist tool is only useful if it leaves defensible evidence. These are the artefacts a high-risk programme has to produce, and the layer that normally produces each one.
| Evidence | What it shows | Normally produced by |
| AI systems inventory | Which systems exist, their purpose and owner | Consultant with product and legal |
| Risk classification | Why a system is high-risk, limited-risk or minimal | Regulatory judgement, not software alone |
| Technical documentation | Design, data, testing and limits of the system | Engineering, guided by the consultant |
| Human oversight records | Who reviews decisions and how | AI governance tool |
| Vendor and model assessments | What external models and APIs commit to | GRC or privacy tool with the consultant |
| Logs and monitoring | How the system behaves after deployment | Platform and assurance tooling |
| Approval and review records | Decisions, exceptions and sign-off | GRC workflow |
| Training and awareness | That the team knows the rules | Internal programme with the consultant |
The lesson is simple: choose the option based on the evidence you are missing, not on the length of its feature list.
Conclusion
If you are starting, prioritise expert consulting and inventory. Without a systems map, there is no reliable technology purchase.
If you already have inventory and controls, assess software. Look for traceability, evidence, privacy and security integration, reporting and vendor management.
If you sell to regulated customers, combine both layers. You will need judgement to design the programme and technology to sustain it.
If you want to use compliance as a commercial advantage, do not stop at minimum requirements. Build a system that can answer audits, due diligence and customer questions without improvising each time.
At PrivaLex, we can help define which combination of consultant, tool and certification makes sense for your company. Request a free risk assessment and we will review your starting point against the EU AI Act, or book a call to talk it through.
Frequently asked questions
It depends on the starting point. If you do not yet have an inventory, risk classification or governance model, start with consulting. If controls are already defined and you need to operate evidence, software adds more value.
No. A platform helps organise inventory, tasks and evidence, but compliance depends on correct classification, real controls, internal responsibilities and continuous review.
Companies that need to move quickly, connect the AI Act with GDPR, ISO 42001, security and B2B customers, and turn compliance into operational evidence. It fits especially well for startups, scaleups and regulated technology companies.
First check whether your current GRC can extend to AI. If it supports inventories, workflows, controls, vendors, evidence and reporting, it may be enough. If it cannot capture models, datasets, substantial changes or human oversight, it will need adaptation or a specialist layer.
No. ISO 42001 provides the management system; the tool helps operate it. You can implement ISO 42001 without a dedicated platform, but if there are many AI systems, vendors and evidence flows, software improves traceability.
There is no single best platform for every company. TrustWorks and OneTrust are relevant where AI governance needs to connect closely with privacy; Credo AI focuses on AI governance and policy controls; Holistic AI adds assessment and technical assurance; IBM and ServiceNow suit complex enterprise environments; and Microsoft Purview is useful where data, Copilot and information security are central. The right option depends on whether the priority is inventory, privacy, technical testing, corporate GRC or audit evidence.
Highly regulated companies usually need a combination of specialist advice and technology rather than a standalone tool. Providers should be able to connect AI inventories, GDPR assessments, sector obligations, suppliers, security controls and audit evidence. The selection should also consider requirements arising from frameworks such as DORA, NIS2, ISO 27001 or ISO 42001 where they apply.
Large enterprises should evaluate both advisory firms that can design the governance model and platforms capable of operating it at scale. The assessment should test intake workflows, ownership, escalation, integrations, evidence exports and support for multiple business units and jurisdictions. A pilot using real AI use cases is more informative than relying only on demonstrations or feature lists.