How confident are you that your test data is secure, realistic, and fit for AI and regulatory scrutiny?
Answer a few quick questions to see where you stand — and what to do next.
Question 1 of 7
Test Data Strategy
How do you currently provision test data?
Data Relationships
How well does your test data preserve relationships across systems?
e.g. customers → orders → payments, or end-to-end process flows
Data Risk & Compliance
Can you clearly demonstrate how test data is protected and who has access to it?
Speed & Scalability
How quickly can you provision test data for new environments or releases?
Data Quality for Testing
How confident are you that your test data reflects real-world system behaviour?
AI Readiness
How are you testing and validating AI models?
Repeatability & Evidence
Can you repeat your test data process and evidence how testing was performed?
High Risk
Your test data approach is likely increasing risk under NIS2
Your responses suggest reliance on production data, limited control, and gaps in data realism. This creates exposure across both compliance and system reliability — and increases the risk of false confidence in testing.
Developing
You've made progress — but key gaps remain
You've taken steps to reduce data risk, but challenges around data relationships, scalability, and consistency may still impact testing reliability and AI validation.
Advanced
You're on the right path — now it's about scaling
You have strong foundations in place. The next step is optimising for scale, consistency, and auditability to fully support AI assurance and evolving regulatory expectations.
Join our upcoming webinar: Supercharging AI Assurance with Test Data Automation
