The AI GRC Software for Your Governance

The two phrases circulate together and get mixed up constantly. AI GRC means using artificial intelligence to run governance, risk and compliance work: assessments, deliverables, follow-up on action plans. AI governance runs the other way: putting controls around the AI systems your organisation builds or buys, using frameworks such as ISO/IEC 42001 and the European AI regulation. Mature organisations usually end up doing both, but they are separate programmes with separate owners.

Three questions usually settle it. Does the AI reach your whole repository (assets, risks, controls, evidence), or only the document currently open? Does it produce objects the tool can actually use, a risk, a control, an action, or just text you paste somewhere else? Can every generated sentence be traced back to the source it came from? A chat assistant added on top of an existing product tends to fail the last two.

Anything that commits the organisation. Accepting a residual risk, arbitrating the budget of a treatment plan, approving an exception or signing off a statement of applicability are management acts, and an auditor will ask who took them. AI is there to prepare the ground: gather the inputs, propose a first analysis, draft a deliverable. Human review is not a formality here, it is what makes the output defensible.

By following the trail. Generated content should show which documents it drew on, who reviewed it and when it was approved. The principle is the same as for a hand-written deliverable: what matters is not the tool but the traceability of the decision. In practice, ask any platform for version history, a named approver and links back to the source material. Without that, the time saved upstream comes back as audit questions later.

A spreadsheet holds up while the scope stays small. It breaks as soon as you need to map one control to several frameworks, find the evidence behind a status, know who changed a rating last week, or produce a dated view for a steering committee. Those four needs are exactly what an audit asks about. A platform handles them natively, and AI only pays off when it has structured data to reason over: on a spreadsheet, there is nothing for it to work with.

Four points deserve a written answer: where the data is hosted and under which jurisdiction, which subprocessors are involved, model providers included, whether your content can be used to train a model, and how you get your data back at the end of the contract. These are also the elements your record of processing activities and your GDPR processor clauses will need. As SaaS, Vailor stores data in France (AWS Paris region), runs AI processing on AWS Bedrock in EU regions, never uses data to train a model, and lets you choose the model, including a self-hosted one.

No, and regulation is pushing the other way. NIS2 widens the range of entities in scope to medium-sized companies across many sectors, and DORA imposes a demanding ICT risk management framework on financial entities. Those organisations rarely have a dedicated compliance team. That is exactly where automating collection and drafting changes the arithmetic: the issue is not headcount, it is the ratio between the scope to cover and the hours available.