Why the choice of cyber AI GRC platform matters
Many GRC solutions now announce artificial intelligence, and not all approaches are equal. Some add an assistant on top of an existing tool, while others, like Vailor, were designed around AI from the start. This guide sets out the criteria that make the difference before you sign.
Criterion #1: AI-native vs AI added later
The first question is where the AI actually works. In a platform designed for AI, it sits at the heart of the workflow: it reads the documents you upload, prefills the assessment fields from cited excerpts, and every proposal goes through an expert before it is kept. In a tool where AI was added after the fact, it is often limited to a writing assistant next to the existing screens. Ask to see where each proposal comes from, and who validates it.
Criterion #2: Data sovereignty
For French and European organisations, control over data is non-negotiable. Check where it is hosted, which models are used, whether your data trains them, and which deployment options exist. As SaaS, Vailor stores data in France (AWS Paris region), and its AI processing runs on AWS Bedrock in EU regions. Vailor also installs in your own infrastructure (Docker). Your data is never used to train models, and you choose the model, including a self-hosted one.
Criterion #3: GRC business expertise
General-purpose AI is not enough for GRC. Your platform must know the risk assessment methods (EBIOS RM, flash assessment, your in-house method), the regulatory frameworks (NIS2, DORA, NIST CSF 2.0), and produce deliverables your governance bodies can read. Vailor covers EBIOS RM end to end, offers a flash assessment for less critical projects, and exports its deliverables to PDF, Word and PowerPoint, with a decision-maker summary.
Test the platform on your own data, not on a demo
A vendor demo always runs on a dataset picked because it behaves well. The test that actually tells you something is to replay, inside the platform, a risk analysis your own team has already produced: your assets, your control framework, your internal vocabulary, then compare the two deliverables line by line. Frame the pilot before you launch it: a real but bounded scope (one business process, one subsidiary, one application domain), four to six weeks, and success criteria written down in advance. Measure rework rather than polish: how many risk scenarios go straight to the bin, how many proposed controls miss the point, how long an analyst spends correcting. Any gain a vendor announces, Vailor included, should be verified on your own perimeter, not in a slide. Test the awkward cases too: a poorly documented asset, a supplier who answers half the questionnaire, an in-house framework nobody has ever mapped. Those are the cases that decide the outcome.
What to have ready before the first workshop
Pilots that stall rarely stall on technology: what is missing is the raw material. Before you start, gather an asset inventory that may be incomplete but is current, the list of business processes and their owners, the existing risk register exactly as it stands, and the evidence sources you already rely on (tickets, scan results, audit reports). Name the person who signs off what the AI produces: with no designated reviewer, deliverables pile up in a queue and the pilot quietly dies. On the IT side, plan the connection to your directory, the definition of permissions per scope, and a review of the processing with your data protection officer. Prerequisites also shift with size and sector: an essential entity under NIS2 does not face the same supervisory regime as an important entity, and a financial entity under DORA has to wire in its register of information on ICT third-party arrangements from day one. Those differences decide which modules you open first.
Exit terms, traceability and the metrics you track after go-live
Three requirements are negotiated in the contract, not once the platform is installed. Exit first: your analyses, risk register, action plans and evidence must be exportable in an open, re-importable format, with no vendor assistance required. Traceability next: every generated item should carry its sources, the model and version that produced it, and the name of the person who approved it. An auditor or a certification body will ask who wrote what, and an untraced output becomes a finding. Monitoring last: record your baseline before deployment, or you will have nothing to compare against. Four indicators are enough: the elapsed time between opening an analysis and signing off its deliverable, the share of generated content accepted without substantial rewriting, the proportion of controls backed by dated and recent evidence, and the age of the oldest unresolved gap. Review them after a quarter, using the same definitions.
Conclusion: Questions to ask vendors
Before choosing your cyber AI GRC platform, ask these questions. Where does the AI work, and who validates its proposals? Where is the data hosted, and is it used to train models? Which models are used, and can you choose your own? Which methods and frameworks are available today, and which are only announced? Ask Vailor too: book 30 minutes, we listen to your context and give you concrete answers.