Glossary · Governance
AI governance framework
An AI governance framework is the documented set of policies, roles, controls and records that determine who may deploy an AI system, on what data, with what testing, and who is accountable when it fails.
An AI governance framework is the documented set of policies, roles, controls and records that determine who may deploy an AI system, on what data, with what testing, and who is accountable. IBM found only 4% of Indian organisations have embedded AI-risk frameworks, while 83% of executives call governance essential.
- A governance framework covers policies, roles, controls and records for deploying AI.
- IBM IBV, November 2025: 83% of Indian executives call governance essential.
- IBM IBV: only 4% of Indian organisations have embedded AI risk frameworks.
- India governs AI through existing law: the IT Act, DPDP Act and consumer protection.
- Core artefacts: an approval gate, a model register, an evaluation harness and an escalation path.
Also known as: AI governance, AI risk framework
An AI governance framework is the documented set of policies, roles, controls and records that determine who may deploy an AI system, on what data, with what testing, and who is accountable when it fails.
It is not a policy document that sits on a shelf. It is the working machinery (an approval gate, a register, an evaluation harness, an escalation path) that people actually follow when they put AI into production.
How an AI governance framework works
A framework connects a few concrete artefacts into a process. A policy states what AI may and may not be used for. An approval gate decides who can deploy a given system, on what data, before it goes live. A model or use-case register records what is running, so nothing is invisible. An evaluation harness tests each system against fixed cases before and after changes. Human accountability (including human-in-the-loop checkpoints) maps a named person to each consequential decision. And an escalation path says what happens, and who acts, when something goes wrong.
Together these turn "we should govern our AI" into a repeatable sequence with records at each step. The records are the point: governance you cannot evidence is governance you do not have when an auditor, a regulator, or a failed deployment asks.
Why an AI governance framework matters for enterprise AI adoption
The gap here is stark and well-measured. IBM's Institute for Business Value found that 83% of Indian executives call effective governance key to successful AI, while only 4% of Indian organisations have embedded frameworks to manage AI-related risks (IBM IBV, 27 November 2025). Nearly everyone agrees it matters; almost no one has built it. That is not a content gap. It is an execution gap, and it is where a governance engagement earns its place.
Regulation is closing on the same point. India's AI Governance Guidelines, released by MeitY in November 2025, govern AI through existing law rather than a new statute: the IT Act 2000, the DPDP Act 2023, and consumer protection legislation, coordinated by a new AI Governance Group (IAPP, November 2025). For a GCC the practical obligations (consent provenance for training data, audit trails, documented risk ownership) are exactly what a framework produces as a by-product of running properly.
Common mistakes with an AI governance framework
The first mistake is writing the policy and stopping there. A document nobody follows is not governance; the register, the gate and the evaluation harness are what make it real. The second is diffuse accountability: governance "owned by the committee" means owned by no one, and no one can answer for a specific system when it matters.
The third is waiting for perfection. Teams delay governing anything until they can govern everything comprehensively, and in the meantime systems ship ungoverned. A framework that covers the highest-risk systems in 90 days and matures from there beats a flawless framework that never launches, which is, in effect, why the embedded-framework figure sits at 4%.
Related terms
- Human in the loop: the accountability checkpoints a framework documents.
- Evaluation harness: the testing control at the core of the framework.
- Workflow absorption: what governance is ultimately protecting: real, measured change.
- Model Context Protocol: a single boundary that makes tool access auditable.
- EDI and EDIFACT: where legally binding documents make governance non-optional.
How Chokmah approaches the AI governance framework
We build governance you can evidence, and we build it in the right order. Under a governance and CoE retainer we stand up the workable first version (policy, approval gate, register, an evaluation harness for the highest-risk systems, and an escalation path) inside about 90 days, then mature it as more systems come under it. We map India's MeitY and DPDP obligations to concrete artefacts, and we insist on named accountability per system, because a framework nobody owns is the 4% figure waiting to happen.
Sources
- IBM Institute for Business Value, AI Infrastructure That Endures (India), 27 November 2025. https://in.newsroom.ibm.com/2025-11-27-83-of-Indian-executives-say-effective-governance-is-key-to-successful-AI-infrastructure
- IAPP, India releases DPDPA rules and AI Governance Guidelines, November 2025. https://iapp.org/news/a/notes-from-the-asia-pacific-region-india-releases-dpdpa-rules-ai-governance-guidelines
The governance loop
Text description of this diagram
Five governance stages arranged in a circle connected by a dashed ring: Define, Instrument, Evaluate, Review, Adjust. The circular layout shows AI governance as a continuous, repeating cycle rather than a one-time document.
Related terms
- Human in the loopHuman in the loop is a workflow design in which a person reviews, approves or corrects an AI system's output at defined checkpoints before it takes effect, keeping accountability with a human.
- Evaluation harnessAn agent evaluation harness is a repeatable test suite that scores an AI agent's outputs against fixed, versioned cases before and after every change, so teams can tell regression from variance.
- Workflow absorptionWorkflow absorption measures whether AI has actually changed how work runs (steps redesigned, cycle time reduced, errors cut) as opposed to adoption, which only counts access such as seats and logins.
- Model Context Protocol (MCP)The Model Context Protocol (MCP) is an open standard that defines how AI applications connect to external tools and data through one uniform interface instead of many bespoke integrations.
- EDI and EDIFACTEDI is the structured, computer-to-computer exchange of business documents; EDIFACT is the United Nations standard, defined by ISO 9735, that specifies the syntax those documents use in trade and transport.
Frequently asked questions
At minimum: a policy stating what AI may and may not be used for; an approval gate that decides who can deploy a system and on what data; a model or use-case register recording what is live; an evaluation harness that tests systems before and after changes; human accountability mapped to each consequential decision; and an escalation path for when something goes wrong. It is a set of records and controls people follow, not a document that sits on a shelf.
India has no standalone AI statute. Its AI Governance Guidelines, released by MeitY in November 2025, govern AI through existing law (the IT Act 2000, the DPDP Act 2023, and consumer protection legislation) coordinated by a new AI Governance Group. For an organisation the practical obligations that follow are consent provenance for training data, audit trails, and documented ownership of AI-related risk. A framework is how you meet those obligations in a checkable way.
Accountability has to be named, not diffused. Governance typically needs an owner with authority (often a transformation or risk leader) supported by the workflow owners who run each AI system day to day. IT provisions and secures; the business owns the outcome and the risk of each use case. The failure pattern is governance owned by everyone and therefore no one; the fix is a named accountable person per system and a forum that reviews them.
A workable first version can be stood up in about 90 days: policy, an approval gate, a register of what is live, an evaluation harness for the highest-risk systems, and an escalation path. That is not a finished framework (it matures as more systems come under it), but it is enough to stop deploying AI blind. Waiting for a perfect framework before governing anything is how the 4% figure stays at 4%.
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