Guides
How this work actually gets done.
Long-form notes on turning AI governance decisions into controls that hold up — written from doing the work, not from summarising other people's posts.
9 min read
How to design GenAI DLP controls with a control matrix
Why per-application DLP rules stop scaling once you have more than a handful of AI tools, and how a governance-category × data-family matrix replaces them with a model you can defend and reuse.
Read the guide →
In preparation
Building a Netskope GenAI DLP policy set
Policy order, required objects, and the fallback behaviour that decides what happens to an AI application nobody has assessed yet.
In preparation
Controlling ChatGPT uploads with Netskope
Prompt versus upload inspection, instance-level control, and why blocking the domain outright usually creates more problems than it solves.
In preparation
Mapping GDPR Article 32 to AI application controls
What “appropriate technical measures” means when the processing is a prompt, and which controls actually evidence it.
The guides describe the method. The platform runs it.
Everything in these guides is what Uraikkal automates — governance categories, the control matrix, vendor policy structure, and the evidence that proves the controls work.