Features that make the difference in AI GRC
Not all AI GRC platforms are equal. To get real value from artificial intelligence in governance, risk and compliance, some capabilities are essential. This guide details the 10 points to check, and how Vailor addresses each one.
Features 1 to 5: the AI GRC core
1. Assessments prefilled from your documents, with the source excerpts cited. 2. RAG (Retrieval-Augmented Generation) to anchor every proposal in your data rather than in general knowledge. 3. Reading of project documents (architecture files, specifications, contracts) and, failing that, guided collection from business teams. 4. Several risk assessment depths (full EBIOS RM, flash assessment). 5. Exportable deliverables (PDF, Word, PowerPoint) and a decision-maker summary.
Features 6 to 10: operational excellence
6. Collaboration between business and security, with a pre-assessment the business starts in self-service. 7. Fine-grained role and permission management, custom roles included. 8. Modelling of complex organisations (group, subsidiaries, scopes) with settings inherited per entity. 9. Management dashboards with customisable indicators and PowerPoint export. 10. Control over data: data stored in France, choice of model, data never used for training.
How to evaluate an AI GRC platform
Use this checklist when evaluating AI GRC solutions, and ask to see each point working, not just described. Vailor was designed around these capabilities. Book 30 minutes: we listen to your context and tell you concretely how Vailor addresses it.
Common mistakes when evaluating these features
A demonstration is almost always run on a clean corpus: recent documents, consistent naming, a scope picked in advance. Your own material rarely looks like that. Ask to load your own files, including an architecture diagram exported as an image and a technical design document nobody has reopened in two years, then watch what the platform actually extracts. The second reflex is to demand traceability. Every generated sentence should point back to the source passage it came from, otherwise review turns into a full re-verification and the benefit evaporates. Third, and often overlooked: behaviour under uncertainty. An engine that answers with the same confidence whether the evidence exists or not costs more time than it saves. The most revealing test is to replay an analysis you have already done by hand: you know the right answer, so you see straight away what is found, what is missed and what is invented. Have that test run by the person who will actually write the deliverables, not by the team steering the purchase. Finally, test the way out: a deliverable that only exists inside the tool will not hold up in front of an auditor.
What to have ready before you start
An AI GRC platform only reads what you hand it, and four things decide the outcome. Scope first: the list of applications, data flows and third parties in play, each with a named owner, or the actions it generates will have nobody to carry them. Then the framework: choosing EBIOS RM for risk analysis, Annex A of ISO/IEC 27001 for internal control or the NIST CSF for steering does not imply the same vocabulary or the same deliverables, and stacking all three without a decision produces an unreadable register. Then the document sources: diagrams, architecture files, supplier contracts, procedures, including the ones asleep on a shared drive. This is where most projects slip, because nobody can say which version is authoritative. Finally the legal framing of the use case: which categories of document may be processed, by whom, with what audit trail, and what happens to files holding personal data or trade secrets. Hosting in France and the EU makes that conversation shorter; it does not remove it. Plan for the legacy too: an inherited risk register that is incomplete and never arbitrated stays incomplete once imported.
Measuring real effect and calibrating to your organisation
Four indicators are enough to judge after one quarter. The delay between the request for an analysis and the validated deliverable, measured before and after. The number of analyses actually produced over the period, because the first value of a platform is often to cover subjects that kept being postponed. The retention rate, meaning the share of generated content that survives human review: it tells you far more about engine quality than any demonstration. And register freshness, the median age of the last update on a risk, the only real sign that a framework is still alive. Any figure a vendor puts forward, Vailor included, is a hypothesis to test on your own files. Calibration then depends on your organisation. A team with a single security lead mostly wants analyses it would never have found the time to run. A multi-entity group wants consistency between subsidiaries, and role management becomes the central question. A regulated sector adds a burden of proof: every decision must stay defensible months later, including once the person who took it has moved on.
Conclusion: don't compromise on the essentials
An AI GRC platform without traceability, human validation or control over data will cost more time than it saves. Check these 10 points one by one, on your own documents. At Vailor, the principle stays the same everywhere: the AI proposes, your experts decide.