These are the best platforms that help you document AI act obligations:
- PrivaLex
- OneTrust AI
- TrustWorks
- IBM watsonx.governance
- ServiceNow AI Control Tower
- Credo AI
- Holistic AI
Documenting AI Act obligations requires more than uploading policies to a shared folder. A company needs to connect each AI system with its regulatory role, risk classification, applicable requirements, responsible owners, controls and supporting evidence.
The right platform should make that information easy to update and retrieve. It should show why a system was approved, which risks were identified, what controls were implemented and whether any actions remain unresolved. It should also preserve previous decisions when the model, data, supplier or intended purpose changes.
There is no universal platform that works for every organisation. A company that already operates a corporate GRC may need a different solution from a business managing AI through its privacy team. The best choice depends on the number of systems, internal maturity, existing software and the level of technical monitoring required.
7 tools for documenting AI Act obligations
The options below take different approaches to AI Act documentation. Some focus specifically on AI governance, while others integrate AI compliance with privacy, enterprise risk, technical monitoring, or existing business workflows.
1. PrivaLex supported implementation
PrivaLex is not a software vendor. It helps companies determine what their chosen platform needs to document and how the resulting records should support AI Act compliance.
This distinction matters because software cannot decide the company’s legal position without reliable information and informed judgement. Before configuring a platform, the organisation needs to define which AI systems are in scope, whether it acts as a provider or deployer, how risks will be classified and what evidence will demonstrate that each obligation has been addressed.
PrivaLex can help create the documentation architecture that sits behind the software. This may include:
- An AI system inventory with agreed fields and ownership rules.
- A method for recording provider, deployer, importer and distributor roles.
- Risk classification questions and escalation criteria.
- A register of applicable AI Act obligations.
- A control library linked to legal requirements.
- Evidence requirements for each control.
- Review and approval workflows.
- Rules for material system changes.
- Supplier questionnaires and contract evidence.
- Management and audit reporting requirements.
- Integration points with privacy, security and procurement.
- Retention and version control rules.
The work is adapted to the systems the company already uses. If an existing GRC, privacy platform or workflow tool can support the required records, PrivaLex can help configure the governance model within that environment. If new software is necessary, the same work can become a functional specification for comparing vendors.
This approach is particularly useful when different departments have already created separate records. Privacy may maintain a DPIA, security may hold a risk review, procurement may store the supplier contract and the technology team may retain model testing results. PrivaLex helps connect those records so that the organisation can present a coherent compliance position.
Companies exploring the relationship between legal and privacy records can also review what legal teams and DPOs should document for the AI Act.
2. OneTrust AI Governance
OneTrust AI Governance can be suitable for organisations already using OneTrust for privacy, data governance, supplier risk or compliance activities.
Its main advantage is the ability to connect AI records with processes that may already exist in the organisation. An AI system involving personal data can be linked with privacy assessments, processing activities, data sources and vendor information rather than documented in a completely separate environment.
The platform may be considered when a company needs to maintain:
- An inventory of AI systems and use cases.
- Questionnaires for business and technical owners.
- Risk and impact assessments.
- Approval and remediation workflows.
- Links between AI, privacy and supplier records.
- Control ownership and supporting evidence.
- Reports for management and compliance teams.
OneTrust may be a logical option where employees already understand its workflow and assessment model. Reusing an established platform can reduce duplicate records and make it easier for privacy, compliance and procurement teams to collaborate.
However, companies should avoid copying a privacy workflow and simply renaming it as an AI assessment. The AI Act requires system specific information about purpose, role, risk, human oversight, testing, logging and changes. Those fields and decisions must be configured deliberately.
3. TrustWorks
TrustWorks combines privacy management and AI governance capabilities. It may suit European organisations that want to manage AI inventories, classifications, assessments and supporting documentation within a compliance focused environment.
The platform can be considered by companies that need:
- Registration of AI systems and use cases.
- Identification of system owners and vendors.
- Preliminary AI Act risk classification.
- Assessment and approval workflows.
- Controls linked to identified risks.
- Evidence collection from multiple stakeholders.
- Monitoring of outstanding actions.
- Reporting for internal governance.
Its connection between privacy and AI governance can be valuable when many AI systems process personal data. Legal, privacy, security and product teams can contribute to the same record while retaining responsibility for their respective assessments.
Before implementation, the company should define how detailed the inventory needs to be. Excessive questionnaires may discourage employees from registering new systems, while an inventory with too few fields will not support classification or evidence requirements.
A short initial intake followed by more detailed questions for sensitive systems will often be easier to operate.
4. IBM watsonx.governance
IBM watsonx.governance is relevant for organisations that need to document AI governance alongside model development, evaluation and monitoring.
It can be particularly useful where the company develops models internally or operates a large technical AI portfolio. In these environments, compliance evidence may need to include performance results, model facts, testing records, limitations and monitoring information that a conventional document repository cannot collect effectively.
The platform may help organisations manage:
- Model and use case information.
- Evaluation results and performance measures.
- Governance reviews and approval decisions.
- Identified risks and mitigation actions.
- Model limitations and intended use.
- Monitoring records and changes.
- Reports for technical and governance teams.
The central question is whether the company needs technical model governance, regulatory workflow management or both. A strong technical platform may still need to connect with legal assessments, supplier records, privacy documentation and corporate risk processes.
The organisation should test whether watsonx.governance can produce a complete evidence package for its intended audience. An engineering team, internal auditor, customer and regulator may each require a different view of the same system.
5. ServiceNow AI Control Tower
ServiceNow AI Control Tower may be appropriate for enterprises already using ServiceNow for risk, compliance, security, incidents, suppliers or operational workflows.
Its main advantage is integration with existing corporate processes. AI related approvals, control activities, exceptions and incidents can be assigned to teams that already manage their work inside the ServiceNow environment.
It may support documentation concerning:
- AI systems and business use cases.
- Owners, users and suppliers.
- Risk assessments and approval stages.
- Controls and assigned tasks.
- Exceptions and corrective actions.
- Incidents and investigation records.
- Periodic reviews.
- Management reporting.
ServiceNow can be effective when the organisation wants AI governance to become part of its wider risk programme rather than another isolated compliance tool.
The main implementation risk is treating an AI system like an ordinary information technology asset. The platform should distinguish between the system, model, data, intended purpose, supplier and affected individuals. It should also record human oversight, performance limitations and material changes.
A carefully designed taxonomy is therefore essential. Without it, the company may have efficient workflows but insufficient AI specific evidence.
6. Credo AI
Credo AI is a specialist AI governance platform designed for organisations that need a dedicated environment for AI policies, risks, controls and reporting.
It may suit companies that develop or integrate AI across several products, departments or markets. Its value is strongest when legal, compliance, risk, product and technology teams need a shared governance layer without forcing every record into a conventional privacy or enterprise risk format.
The platform may be evaluated for:
- AI system and model inventories.
- Policy and regulatory mapping.
- Risk and impact assessments.
- Control assignment and evidence.
- Review and approval workflows.
- Exceptions and remediation.
- Governance reporting.
- Coordination across internal teams.
A specialist platform can provide greater depth for AI governance, but it should not become disconnected from the systems where technical, privacy and supplier evidence is created.
During a demonstration, the company should ask how the platform receives evidence from existing tools, how it manages changes and whether records can be exported in a useful format.
7. Holistic AI
Holistic AI combines governance capabilities with AI risk assessment, testing and monitoring. It may be appropriate where the organisation needs to connect documentary evidence with a more technical understanding of system behaviour.
The platform can be considered when a company needs:
- Discovery and registration of AI systems.
- Risk and impact assessments.
- Governance controls and approval records.
- Technical testing and evaluation.
- Monitoring of system behaviour.
- Reporting across an AI portfolio.
- Evidence for internal review or external requests.
This type of platform may be valuable for organisations using AI in sensitive or highly scrutinised areas. Documentary controls are more persuasive when they are supported by testing and monitoring results.
The company should still define how technical findings translate into decisions. A bias test, performance measure or monitoring alert must lead to an assigned action, review or documented acceptance decision. Otherwise, the platform may produce information without establishing accountability.
For a wider view of available products, companies can compare additional AI governance and compliance software options.
Comparison of the 7 documentation options
The table below summarises where each option may fit and what companies should examine before making a decision.
| Option | Best suited to | Main documentation strength | Point to examine |
| PrivaLex supported implementation | Companies that need to define requirements before configuring software | Obligation mapping, evidence design and practical workflows | The selected software still needs internal owners |
| OneTrust AI Governance | Organisations already using privacy or GRC modules | Assessments, inventories and connected privacy records | Configuration can become complex |
| TrustWorks | European organisations combining privacy and AI governance | AI registration, classification and collaborative evidence | Confirm integration needs |
| IBM watsonx.governance | Businesses developing or operating models at scale | Model facts, evaluations and technical governance records | May require a separate compliance workflow |
| ServiceNow AI Control Tower | Enterprises already using ServiceNow | Tasks, approvals, incidents and control workflows | AI specific fields must be designed carefully |
| Credo AI | Organisations needing a dedicated AI governance layer | Policy mapping, reviews, controls and reporting | Assess fit with existing GRC and technical systems |
| Holistic AI | Companies requiring governance with technical assessment | Inventory, risk reviews, testing and monitoring | Confirm how evidence will be exported and retained |
What should an AI Act documentation platform record?
The AI Act does not require companies to purchase a particular platform. The purpose of the software is to help the organisation maintain documentation, records and evidence required for its role and systems.
For high risk systems, the EU AI Act includes requirements concerning risk management, data governance, technical documentation, record keeping, transparency, human oversight, accuracy, robustness and cybersecurity.
A useful platform should connect those requirements to the following record types.
AI system record
Each system should have a central record containing its purpose, owner, users, supplier, data, deployment context and lifecycle status.
The record should distinguish between the AI system, underlying model, related datasets, external providers and business process. Treating all of these as a single item can make later reviews difficult.
Regulatory role decision
The platform should record whether the organisation acts as a provider, deployer, importer or distributor.
It should preserve the reasoning, reviewer and approval date. If the system is modified or used for a new purpose, the role decision may need to be reconsidered.
Risk classification
The record should show whether the use case involves a prohibited practice, high risk system, transparency obligation or another category.
A selection from a menu is not enough. The platform should retain the facts, questions and reasoning supporting the decision.
Obligations register
Applicable requirements should be mapped to the individual system. This prevents teams from applying the same checklist to every use case regardless of risk or regulatory role.
Each obligation should have an owner, status, review date and related controls.
Control record
The platform should describe what the organisation does to address the obligation. Examples include human review, data quality checks, access controls, supplier reviews, user notices, performance testing and incident management.
The control record should identify who performs the activity, how often it occurs and what evidence it generates.
Evidence record
Evidence demonstrates that the control operated. It may include approvals, test reports, logs, meeting records, completed assessments, contracts, training records, notices and incident reports.
Each evidence item should have a date, owner, system reference, version and retention period.
Change record
Changes to the model, supplier, data, intended purpose, user population or level of autonomy may affect the classification and controls.
The platform should trigger reassessment when a material change occurs and preserve the previous decision history.
How to test a platform before purchasing it
A product demonstration should use one of the company’s real AI systems. Generic presentations can show attractive dashboards without proving that the software supports the organisation’s actual decisions and evidence.
Ask the vendor to demonstrate the following scenario:
- Register a new AI use case.
- Assign business, technical, privacy and compliance owners.
- Record the organisation’s regulatory role.
- Complete a preliminary risk classification.
- Escalate an uncertain or sensitive use case.
- Map applicable obligations and controls.
- Request evidence from internal teams and a supplier.
- Record an approval with conditions.
- Introduce a material change to the system.
- Reopen the relevant assessments.
- Record an incident or control failure.
- Export an evidence package for audit or customer review.
This exercise reveals whether the platform supports a real lifecycle or merely stores completed questionnaires.
The company should also test permissions, reminders, version history, integrations, search, reporting and data export. Evidence may contain personal, technical or commercially sensitive information, so access should be controlled carefully.
6 Common mistakes when documenting AI Act obligations
1. Buying software before defining the governance model
A platform cannot resolve unclear responsibilities. If the organisation has not decided who owns an AI system, who classifies it or who accepts risk, the software will only expose that uncertainty.
2. Creating a separate record for every legal framework
The same control may support the AI Act, GDPR, ISO 42001, ISO 27001 and customer requirements. Duplicating the record across multiple modules creates conflicting versions and unnecessary work.
The better approach is to maintain one control and map it to several requirements.
3. Uploading documents without linking them to decisions
A folder full of policies and reports does not show which obligation each document supports or whether the evidence remains current.
Every record should connect to a system, control, owner, decision and review date.
4. Ignoring evidence created outside the platform
Testing results may remain in development tools, contracts in procurement software and incidents in security systems. The platform does not need to duplicate every file, but it should provide reliable references and preserve traceability.
5. Making the intake form too complex
Employees may avoid registering AI if the initial questionnaire requires information they cannot reasonably provide.
Use a short first stage to establish purpose, owner, supplier, data and potential impact. Request more detailed evidence when the system reaches a higher risk threshold.
6. Forgetting export and retention requirements
The company should remain able to retrieve its records if it changes vendors. Before purchasing, test whether assessments, evidence links, decisions and version history can be exported in a usable format.
Final recommendation
The growing complexity of AI regulation means that organisations need to look beyond individual compliance tools and focus on building a governance framework that can keep pace with evolving regulatory requirements.
The EU AI Act is central to this shift, introducing obligations that require organisations to understand how AI systems are used, determine which regulatory requirements apply, assess and manage risks, define responsibilities, maintain appropriate documentation, and demonstrate ongoing compliance.
For many organisations, the challenge is not simply understanding the regulations, but translating them into practical processes that work across AI governance, privacy, risk management, security and existing business operations. Compliance therefore needs to be approached as an ongoing governance process rather than as a one-time documentation exercise.
This is where a structured approach to AI governance can provide value. Organisations should first establish what regulatory obligations apply to their AI systems, what controls and processes are required, who is responsible for them, and what evidence must be maintained to demonstrate compliance. Once these requirements are clearly defined, they can then determine whether existing systems, processes or dedicated compliance solutions are capable of supporting them.
Ultimately, effective AI Act compliance is not about collecting the largest number of documents or implementing a particular platform. It is about creating a coherent and defensible governance framework that shows what each AI system does, which obligations apply, how risks are managed, who is accountable for decisions, what controls are in place, and what evidence demonstrates that those controls remain effective over time.
Organisations deciding whether to configure an existing platform or introduce a new one can explore privacy and AI governance automation support.
Frequently Asked Questions (FAQs)
No. The AI Act does not require organisations to purchase a particular platform. However, software can make inventories, assessments, controls, evidence and reviews easier to maintain as the number of AI systems grows.
Possibly. A current GRC may be suitable if it supports flexible inventories, system specific assessments, obligation mapping, evidence, approvals, changes and reporting. The company should test whether it can represent AI models, data, suppliers, roles and human oversight accurately.
They may be sufficient for an initial inventory or a small number of lower risk systems. They become harder to manage when several teams need workflows, permissions, reminders, version control and connected evidence.
No. Software can ask classification questions and apply configured rules, but the result depends on accurate facts and appropriate legal interpretation. Uncertain or sensitive cases still require human review.
Relevant evidence may include assessments, approval records, testing results, supplier documents, contracts, user instructions, human oversight records, training evidence, logs, monitoring reports, incidents and corrective actions.
Using the same platform can reduce duplication when AI systems process personal data. However, the records should remain distinguishable because the AI Act and GDPR impose different requirements.
Documentation should be reviewed periodically and whenever there is a material change to the system, model, data, supplier, intended purpose, user group or deployment environment.
No. The platform supports workflows and evidence, but it cannot replace legal classification, technical testing, risk decisions or accountable internal ownership.