MeitY announced the DEPA for AI Training Handbook in Bengaluru on October 31, 2026, with support from iSPIRT Foundation. Its announcement describes purpose-bound collaboration in which institutions retain control of their data. The stated ingredients include permissions, controlled computing environments, privacy safeguards and provenance recording the data’s origin and use.
Primary source: MeitY’s October 31 handbook announcement
The announcement quotes Principal Scientific Adviser Ajay Kumar Sood describing a model travelling to the data. For someone evaluating a pilot, this suggests a useful first task: draw the actual processing path. A statement about where processing occurs does not, by itself, show which outputs leave that environment.
Separate the announcement from implementation evidence
iSPIRT’s earlier technical overview describes confidential clean rooms, electronic contracts and a training sandbox. It also describes a graphical interface for exploring and running scenarios. These materials provide technical background; their existence is not evidence that a particular organisation has deployed or validated a working collaboration.
Primary technical background: iSPIRT’s DEPA-Training overview
This article reviews the announcement and technical overview. It does not review the full newly launched handbook, test the software or assess a real organisation’s data-sharing arrangement. Keep those evidence boundaries clear when discussing a possible project.
Draw a fictional processing path
Imagine two invented equipment makers studying failure patterns using fabricated machine logs. One keeps its synthetic records in Environment A, while the other uses Environment B. A training program is proposed to run against those records. This is a planning exercise, not a description of an existing DEPA deployment.
On a worksheet, give each environment its own box. Add the training program and draw an arrow for each proposed input and output. Name what each arrow carries: program files, synthetic records, model parameters, evaluation results or diagnostic logs. Avoid using a single arrow labelled data for several different transfers.
Beside every arrow, record who initiates it, who receives it and what evidence would establish that the intended transfer occurred. If the team cannot identify an output’s recipient, mark the question unresolved. A complete-looking architecture drawing should not turn an unknown into an assumed control.
Choose one observable question
For a demonstration, ask the provider to show one narrowly defined run with fabricated inputs and its resulting artifacts. Compare those artifacts with the worksheet. Was an output created where expected? Can the team connect it to the intended input version and run? Record the observation separately from the proposed design.
An unexpected diagnostic file deserves the same attention as a model file: identify what it contains and where it went. Do not infer that an architecture description establishes the contents of every artifact. Resolving these questions helps define the next evaluation step without moving real institutional records merely to explore the interface.
The immediate value of this exercise is a clearer pilot brief: the intended computation, participating environments, permitted transfers and evidence still needed. A successful synthetic demonstration would remain a limited observation, not proof of privacy, production readiness or model quality.
Read the processing path
