Data Readiness
Before connecting AI to your enterprise data, discover sensitive information, map PII exposure, and define secure access controls.
What changes for your team
Before a model touches SharePoint, S3, databases, or file shares, teams need to know where sensitive data sits, who can access it, which repositories are over-permissioned, and what should be masked or excluded.
We can use Pelestra to review supported repositories through approved read-only connections. Deployment, scan coverage, findings storage, and any external processing are agreed before sensitive material is connected.
Basic regex scanners create noisy findings. We use a 3-tier approach: pattern matching, NLP-based named entity recognition, and exact data match. It reads column headers and surrounding context to improve classification of SSNs, financial data, and sensitive IP.
The assessment records reviewed repositories, sensitive-data findings, access concerns, relevant transfer paths, and controls to consider. It identifies candidates for bounded testing; it does not certify that a workflow is risk-free.
How it works
Agree on the work, information, business rules, user roles, desired output, and what the AI is allowed to do.
Bring in only the approved documents, records, business data, control libraries, or playbooks needed for the task.
Give your team findings, matrices, notes, data-backed answers, or work queues with sources and clear reasons for exceptions.
Track edits, accuracy, time saved, exception quality, and adoption before applying the system to more work.
FAQ
It reviews the proposed data scope, sensitive information, permissions, processing paths, and controls needed before an AI workflow is tested or deployed.
Scanning can be designed to run in your approved environment without creating a separate central copy of source files. We separately agree where findings, extracted context, logs, and model requests are processed and retained.
Technology partnership
Have something in mind?