Security and data
Security and data handling for AI automation.
How I approach access, permissions, sensitive workflows and data handling when scoping or building AI automation.
Principles
How I work with your systems.
Access starts narrow
I agree the minimum access needed before any implementation work. Where possible, work starts from examples, exports, sandbox data or limited permissions before touching production systems.
Sensitive workflows get extra review
Workflows involving customer data, pricing, finance, legal review, HR, regulated activity or private documents are reviewed for permissions, approval points and failure paths before automation is proposed.
Human control is designed in
AI should not silently make high-risk decisions. I define where humans approve, where logs are kept and where the workflow should stop instead of guessing.
What this means in practice
Before a build, the workflow should have a named owner, known data sources, permission boundaries, review points and a clear fallback if automation should not continue. This is especially important when the system touches customer communication, financial data or internal decision records.
Let's talk
What would make the biggest difference to your business?
Tell me what you're working on. We can work out the next step together.
Or email z@madesimple.ai