These are the 10 best tools and solutions to document AI Act risks and controls:
- PrivaLex
- Credo AI
- OneTrust AI Governance
- ServiceNow AI Risk and Compliance
- IBM watsonx.governance
- Microsoft Purview and Compliance Manager
- TrustWorks
- Vanta
- Drata
- Confluence or Jira
Documenting AI Act risks and controls is not the same as saving PDFs in a folder. A company needs to show why an AI system is in scope, which role the organisation has, which risks have been identified, which controls reduce those risks, who is responsible, what evidence exists and when it was last reviewed.
Documentation also has two layers. The first is the regulatory layer: AI Act requirements, GDPR, ISO 42001, ISO 23894 or contractual commitments. The second is the evidence layer: records, versions, approvals, logs, tests, instructions for use, training, vendor assessments, incidents and risk acceptance decisions.
Regulation (EU) 2024/1689 requires serious traceability for high-risk AI systems. Article 9 covers lifecycle risk management; Article 11 technical documentation; Article 12 logs; Article 13 information and transparency; Article 14 human oversight; and Article 17 a quality management system for providers. The right tool should connect those requirements without duplicating the same control in ten places.
The 10 best tools to document AI Act risks and controls
1. PrivaLex
At PrivaLex, we help build the documentation layer before turning it into software: obligation matrix, risk register, control library, minimum evidence, owners, review frequency, acceptance criteria and audit pack.
The work does not start with a polished screen. It starts by deciding which document answers each question: which system is assessed, what harm it could cause, which control mitigates it, which proof shows that the control exists and which decision has been recorded. This logic can connect with AI Act and ISO 42001, ISO 27001 certification, GDPR audits and AI compliance governance when AI touches personal data, security or people.
PrivaLex is useful when you need to:
- Design an AI risk and control register from scratch.
- Define which evidence corresponds to each AI Act article.
- Create a requirement-risk-control-evidence-owner matrix.
- Avoid duplication across the AI Act, GDPR, ISO 42001, ISO 27001, NIS2 and DORA.
- Prepare criteria to configure Credo AI, OneTrust, ServiceNow, IBM, Microsoft, TrustWorks, Vanta, Drata or Confluence.
- Check whether documentation works for enterprise customers, audits or regulators.
The goal is not to produce more documents. It is to make sure each document has a purpose, owner, date, linked control and verifiable evidence.
2. Credo AI
Credo AI can be a strong option when a company needs a specialist platform to register systems, map obligations, document controls, manage evidence and maintain traceability between AI, risks and regulatory frameworks.
It makes sense in organisations where several teams create or use AI: product, data, engineering, legal, compliance, privacy and security. Its value is in centralising system records, policies, assessments, controls and evidence so the AI committee can see what is approved, pending, conditional or blocked.
It can be useful for:
- Documenting systems, models, agents, vendors and use cases.
- Keeping decision records by risk and control.
- Mapping controls to the AI Act, ISO 42001 and the NIST AI RMF.
- Generating evidence for periodic review.
- Creating status reporting for compliance and management.
Define the internal taxonomy first. If every team uses different names for “risk”, “control”, “mitigation” or “evidence”, the platform will inherit the confusion.
3. OneTrust AI Governance
OneTrust can be useful when AI risk and control documentation needs to live alongside privacy, vendors, GRC, data governance and executive reporting. Many companies already manage GDPR records, DPIAs, third parties or privacy controls in OneTrust. Adding AI can reduce fragmentation if the documentation model is designed properly.
It can be useful for:
- Recording AI systems alongside datasets, agents and vendors.
- Connecting risk assessment with privacy and third parties.
- Mapping controls to internal and external frameworks.
- Managing attestations, reviews and approvals.
- Preparing evidence for internal audit or enterprise customers.
The weak point appears when technical risk requires detailed model metrics, drift or red teaming. In those cases, OneTrust can act as the decision and evidence repository, but will need technical integrations.
4. ServiceNow AI Risk and Compliance
ServiceNow is interesting if the company already uses the platform for IT, risks, controls, incidents, assets, changes or audit. AI documentation can benefit from existing workflows: intake, classification, approval, issues, exceptions, tasks, evidence and reporting.
It can be useful for:
- Documenting systems, models and datasets inside corporate processes.
- Recording inherent risk, controls and residual risk.
- Creating issues when evidence is missing or a control fails.
- Connecting AI risks with changes, incidents and security controls.
- Preparing dashboards for risk committees or internal audit.
Its main value is avoiding another silo. If the team already works in ServiceNow, documenting AI there can make follow-up more real.
5. IBM watsonx.governance
IBM watsonx.governance can help when risk and control documentation needs more technical depth: factsheets, metrics, production models, workflows, alerts, fairness, drift, explainability and links to business processes.
It can be useful for:
- Documenting predictive and generative models with technical metadata.
- Maintaining factsheets and control records.
- Connecting model metrics with risks and decisions.
- Producing evidence of reviews and approvals.
- Supporting model risk management in regulated sectors.
For a company with critical models, this technical layer may matter more than a general control library. For a small company using only AI-enabled SaaS tools, it may be more complex than necessary.
6. Microsoft Purview and Compliance Manager
Microsoft can make sense when documentation is concentrated in Microsoft 365 Copilot, Foundry, Azure OpenAI, Teams, SharePoint, OneDrive or corporate data. Purview helps retain evidence related to data, audit, retention, eDiscovery, DLP and use of supported generative applications. Compliance Manager adds assessments and compliance templates.
It can be useful for:
- Documenting prompt, response and sensitive-information risks.
- Recording DLP, retention, audit and eDiscovery controls.
- Using assessments related to the EU AI Act, ISO 42001 or NIST AI RMF.
- Connecting AI evidence with Microsoft 365 security and compliance.
- Maintaining traceability of interactions in supported environments.
It does not cover all regulatory documentation on its own. Its role is strongest as a data and security evidence layer within a broader system.
7. TrustWorks
TrustWorks can be suitable for European companies that want to document AI from a perspective close to privacy, vendors, compliance and team collaboration. It is especially useful when the first risks appear in AI-enabled SaaS, generative tools and internal processes rather than proprietary models.
It can be useful for:
- Documenting AI use cases and systems.
- Classifying risks under the AI Act.
- Recording controls and mitigations.
- Managing collaboration across legal, privacy, security and product.
- Preparing evidence for internal review.
It works best when the company already knows the minimum fields the register needs: purpose, data, vendor, owner, role, risk, controls, evidence and review date.
8. Vanta
Vanta can be practical if the company already uses it for compliance, security, SOC 2, ISO 27001 or customer evidence. It is not an AI Act-specific tool, but it can help document policies, controls, evidence, vendors and workflows linked to AI use.
It can be useful for:
- Keeping evidence of AI-related controls.
- Documenting acceptable-use policies and training.
- Answering customer questionnaires.
- Connecting AI risks with existing security controls.
- Building an initial documentation layer in startups and scaleups.
The limitation matters: if there are high-risk systems, proprietary models or specific AI Act obligations, Vanta should be complemented with a more detailed regulatory matrix.
9. Drata
Drata can add value when the main objective is to integrate AI evidence into an existing compliance programme: security controls, vendors, policies, training, audits and continuous evidence. Like Vanta, it should not be treated as a complete AI Act solution, but it can organise part of the documentation layer.
It can be useful for:
- Retaining evidence for security controls and vendors.
- Managing internal AI-related policies.
- Keeping compliance tasks and owners.
- Preparing security audits where AI appears as an additional risk.
- Connecting AI risk with ISO 27001 or SOC 2 controls.
It is better as an operational evidence layer than as a complete technical documentation platform for the AI Act.
10. Confluence or Jira
Confluence or Jira can be enough in early phases if the company does not yet need a specialist platform. The key is not to use them as a loose wiki: they need structure, templates, owners, statuses, required fields and linked evidence.
They can be useful for:
- Creating risk and control assessment templates.
- Keeping committee decisions and minutes.
- Managing remediation tasks.
- Documenting system, model, dataset or vendor changes.
- Linking requirements with tickets and evidence.
This option requires discipline. Without a clear taxonomy, a wiki becomes a graveyard of old decisions. With a good template, it can work as a bridge before implementing GRC or AI governance tooling.
The requirement-risk-control-evidence matrix
AI Act documentation should read like a traceability chain. Each relevant system needs a matrix connecting:
- Requirement. Article, obligation, standard, contract or internal policy.
- Risk. Harm scenario, cause, impact, likelihood and residual level.
- Control. Technical, organisational, contractual, human or documentary measure.
- Evidence. Concrete proof that the control exists and works.
- Owner. Person or team responsible for maintaining the evidence.
- Frequency. Review event or periodicity.
- Status. Pending, implemented, accepted, overdue, excepted or blocked.
Article 11 of the AI Act requires technical documentation for high-risk systems, and Annex IV details information such as intended purpose, versions, interaction with other systems, data, oversight measures, performance, risks and controls. The matrix prevents that documentation from being disconnected from real decisions.
Evidence by AI Act article
A useful tool should organise evidence by obligation. Not all evidence is the same or serves the same purpose.
Article 9, risk management. Risk register, methodology, severity criteria, inherent and residual risks, treatments, formal acceptance and reviews.
Article 10, data and data governance. Provenance, quality, representativeness, data preparation, known biases, corrective measures and limitations.
Article 12, automatic records. Event logging should support lifecycle traceability, risk-situation identification, post-market monitoring and operational oversight.
Article 13, transparency and instructions. Information for deployers, instructions for use, limitations, expected performance, residual risks and oversight measures.
Article 14, human oversight. Roles, competencies, authority, intervention procedures, training, escalation and evidence of human decisions.
Article 15, accuracy, robustness and cybersecurity. Test results, metrics, thresholds, vulnerabilities, security controls, red teaming, performance monitoring and corrective actions.
Article 17, quality management system. Policies, responsibilities, change control, document management, vendors, incident reporting, resources and accountability.
Control library: avoiding duplicate work
The usual problem is not that controls are missing. It is that the same controls appear under different names.
An AI control library should have:
- Unique control ID.
- Control objective.
- Risk mitigated.
- Associated obligations.
- Expected evidence.
- Owner.
- Frequency.
- In-scope systems.
- Implementation status.
- Relationship with other frameworks.
The ISO 42001 management system helps organise this work. It does not replace the AI Act, but it supports documentation of responsibilities, policies, impact assessment, lifecycle, data, information for interested parties, responsible use and third-party relationships.
Audit pack for customers or authorities
Documentation should not be prepared only for a formal audit. In B2B, many customers already request AI evidence during due diligence, security, privacy or procurement reviews.
A minimum pack should include:
- Inventory of relevant systems.
- Company role and risk classification.
- Requirement-risk-control-evidence matrix.
- Open and accepted risk register.
- Implemented controls and evidence.
- Vendor assessments and contracts.
- Training and AI literacy records.
- Logs or monitoring evidence where relevant.
- Incidents, corrective actions and material changes.
- Review date and responsible owner.
Where personal data is involved, this pack should be coordinated with the DPO’s role in AI projects and with processing records or DPIAs. In certified environments, connect it with the ISO 27001 risk treatment plan to avoid duplicate evidence.
Next step
The best tool to document AI Act risks and controls is the one that helps answer an uncomfortable question quickly: if someone reviews this system in six months, can they understand what was decided, why it was decided and which proof supports it?
If the answer depends on remembering conversations, searching messages or reconstructing loose tickets, documentation is not yet strong enough. The company needs a shared matrix, a control library, owner-based evidence and an audit pack that can be shared without improvisation.
PrivaLex can help design that documentation structure, map it to the AI Act, ISO 42001, ISO 23894, GDPR, ISO 27001 and customer requirements, and turn it into concrete requirements for the tool you choose.
In an initial assessment, we can review what documentation you need to prove AI risks and controls to auditors, customers or management.
Frequently asked questions
It depends on role and risk, but usually includes inventory, classification, risks, controls, technical documentation, logs, instructions for use, human oversight, data quality, tests, vendors, training and incidents.
It is a traceability table connecting each obligation with the risk it addresses, the control applied, the evidence proving it, the owner and the review frequency.
It can work if it supports AI-specific fields: purpose, data, model, vendor, role, classification, logs, human oversight, metrics, changes, incidents and evidence by obligation. Otherwise, it will only be a partial repository.
Yes, where there are relevant privacy, security, transparency, vendor, customer or reputational risks. Documentation can be proportionate, but key decisions and evidence should be retained.
They often ask for an AI inventory, use policy, vendors, data controls, security, training, risk documentation, oversight measures, incidents, audits and an explanation of how generative AI use is controlled.