Usually a read-only database credential, file store, or bucket representative enough to answer whether the product fits your environment.
Most organisations have AI usage policies. Almost none have technical controls that enforce them. The IBM Institute for Business Value surveyed 1,006 senior executives across 20 countries. The findings are unambiguous.
SOURCE — IBM IBV: AI IN MOTION, APRIL 2026
VestraData governs what you hold. VestraShield governs what you send to AI. Both run on vestracore: one ML engine, consistent entity resolution across every surface.
Scan every database, file store, and cloud bucket. Find regulated data before an auditor does. Anonymise in-place, generate synthetic exports, govern what leaves your boundary.
Your people are already using AI with sensitive data. VestraShield is the control layer between them and every LLM endpoint: browser, IDE, API, and agentic pipelines.
Discovery, remediation, and proof across every data source your organisation uses — all feeding into a single, tamper-evident audit record.
Connect any database, file store, or cloud bucket. VestraCore samples schemas, scores fields by name, value, and context, and returns field-level findings with confidence scores and row counts. No schema knowledge required upfront.
Generate statistically faithful datasets for engineering, QA, and ML pipelines. Referential integrity preserved. Statistical distribution and correlation matched to production. Exports go directly to staging databases or object storage.
Watch repositories for new documents and datasets. When a match is found, a governed clean copy is produced automatically so partner handoffs and AI tooling never receive raw PII. Monitors SharePoint, Drive, S3, and SFTP drops.
Intercept prompts and file uploads before they reach ChatGPT, Claude, or Copilot. Policy can warn, block, or transform without treating every employee as a privacy expert. Audit trail aligned with VestraCore review records.
Every action across every surface writes to a hash-chained, immutable audit record. Regulators see what was found, what changed, who approved it, and when. Evidence is generated automatically, not assembled manually before an audit.
Run the detection and policy layer directly inside an existing pipeline or product. Python, Node.js, Java, and .NET. The embedded SDK runs the full ML stack in-process. No server required. Event-driven for data marketplace ingestion.
Your people are already using AI with sensitive data. VestraShield is the control layer between them and every LLM endpoint.
Four intercept planes. One policy engine. Zero bytes reach any LLM without being governed first.
Every VestraData workflow follows the same sequence — from credential to evidence — regardless of data source or deployment model.
Three models. No vendor lock-in. No requirement to move data to assess it.
Deploy into your own AWS, Azure, or GCP account. Your networking, your IAM, your storage. No production data routed to vendor infrastructure.
Run inside a private data centre or restricted network segment. No internet dependency at runtime. For teams where operational data egress is ruled out by policy.
Embed the detection and policy layer directly into an existing pipeline or product when a standalone deployment is not the right fit.
We are working with a small number of design partners in regulated industries — organisations with a real privacy, compliance, or AI governance problem and a team willing to work closely with us to solve it well.
Design partners get hands-on support from the team, early access to new capabilities, and a shorter loop from feedback to shipped product. For legal design partners, we produce a co-branded SRA response template you can adapt for your own regulatory submissions. Terms are structured for an early partnership and a defined pilot scope.
Apply as a design partner →Not a slide deck. Not a sandboxed environment with fabricated data. We connect to something real in your organisation and you see actual output.
After the session, you should know whether the deployment model works, whether the first workflow is meaningful, and whether a pilot is justified.
Median time to first scan: under 4 hours from credentials.After the session, you should know which intercept planes apply to your environment, what your policy configuration looks like, and whether a 30-day pilot is the right next step.
Target: under 4 hours from deployment to first live intercept.