Every team shipping an LLM feature eventually hits the same wall: the model works in the demo, but nobody can answer "who owns this in production, how do we know if it drifts, and what happens when it's wrong." That's not a legal problem, it's an infrastructure gap — and in India specifically, it's becoming an expensive one fast.
The numbers back this up. Enterprise AI investment in India grew 119% year over year, but a recent industry study found only 22% of Indian enterprises actually have testing, auditing, and risk-assessment processes around what they've shipped. The country's AI governance maturity score sits at 55/100 against a regional Pacesetter benchmark of 78 — a 23-point gap that tracks almost exactly with the ROI gap between governed and ungoverned AI deployments (149% ROI for mature-governance orgs vs. meaningfully less for everyone else).
From an engineering standpoint, "governance" breaks down into four concrete, buildable things:
- A model registry — not a wiki page, an actual queryable inventory: every model/agent in prod, its owner, its data sources, its review cadence.
- MLOps monitoring — drift detection, automated retraining pipelines, version control with rollback, centralized audit logging. This is the part that turns governance from a policy doc into something enforceable.
- Escalation protocols — a defined path (with a human in the loop) for when a model's output deviates from expected parameters, especially for anything touching a high-stakes decision.
- Audit functions — scheduled internal or third-party review of outputs against both business goals and whatever regulatory surface applies (DPDPA at minimum, RBI/SEBI overlays if you're in fintech).
None of this is exotic — most teams already have fragments of it (a logging pipeline here, a security review process there). The actual work is stitching it into one system someone owns end to end, instead of leaving it distributed and undocumented.
Worth knowing if you're in India specifically: MeitY published the country's AI Governance Guidelines in November 2025 — currently voluntary, but I'd bet on it becoming a de facto procurement/audit checklist within a couple of budget cycles, the way a lot of "voluntary" frameworks do.
Full writeup, including the four-pillar framework and real INR cost ranges for building this out: AI Governance for Indian Businesses in 2026
Curious how other teams are handling model registries in practice — homegrown tooling, or has anyone found a vendor platform that doesn't feel like overkill for a mid-sized deployment?
Top comments (0)