09/02 – Connectively (42)

How To Govern AI Usage, Policy, And Identity Without Slowing Teams Down

Written by Nidhi Jain

Close to three-quarters of companies plan to deploy agentic AI within the next two years. Only 21% currently have a mature governance model for it, according to Deloitte’s State of AI in the Enterprise 2026 report. Adoption is moving faster than governance frameworks, and that gap is where most AI risk actually lives.

AI governance is often treated as a single initiative: write a policy, get sign-off, move on. In practice, it’s three distinct disciplines, and each one needs its own approach.

1. Usage: You Can’t Govern What You Can’t See

People adopt AI tools the way they adopt any tool that helps them work faster: quickly, informally, and often without telling anyone. A team lead tries a new assistant and keeps using it. A developer wires up an API key to speed up a workflow. This is simply how work happens now, faster than any formal approval process moves.

Most companies have a record of what was approved. Far fewer have a real-time picture of what’s actually running. Building that picture requires:

• Continuous discovery, since new tools appear weekly, not quarterly
• Visibility into AI features activating inside tools already in use, not just standalone AI apps
• Usage tracked by team and by tool, so spend and ownership stay connected

This is the layer AI usage control tooling is built to solve: knowing what’s running before it becomes something to explain later.

2. Policy: Enforcement At The Moment It Matters

Writing an acceptable use policy is straightforward. Enforcing it at the exact moment someone is about to share sensitive data with an AI tool takes more than a document. Effective policy enforcement includes:

• Enforcement at the point of behavior, not discovered later in a quarterly audit
• A clear, low-friction path to an approved alternative, so people are redirected rather than simply blocked
• An audit trail that shows the policy was applied, not just that it exists

3. Identity: The Layer Most Programs Add Last

AI governance conversations tend to center on people. But AI systems create their own identities too: service accounts, API keys, and agents acting on a company’s behalf without a human directly involved.

These non-human identities accumulate the same way unused software licenses do. Someone connects a project to real systems, moves on, and the access remains active well after it’s needed. Tracking this well means knowing, for every non-human identity:

• Who owns it, and whether that person is still with the company
• Exactly what it can access, not just that it exists
• Whether it’s still active, so it can be revoked as soon as it’s not needed

Treating non-human identity management as its own discipline, distinct from human access reviews, is what makes this manageable at scale. Ownership, not just discovery, is usually what separates the programs that hold up from the ones that don’t.

Where Most Governance Programs Actually Stall

The instinct is to solve all three problems with one tool, or worse, one policy document. They need different disciplines:

Discipline	Best handled as
Usage	Continuous discovery
Policy	Enforcement at the point of action
Identity	Lifecycle tracking to deprovisioning

Each requires a different rhythm. Usage needs constant discovery. Policy needs real-time enforcement. Identity needs lifecycle tracking. Treating all three as one static checklist is usually where governance programs stall.

The Companies Winning At This Aren’t The Strictest Ones

The companies that handle AI governance well aren’t the ones with the strictest rules. They’re the ones with the clearest visibility. I’ve watched this play out enough times to be confident: when a team knows what’s running, what it can touch, and who’s accountable, most access issues resolve themselves before they ever become incidents.

The friction people blame on governance is almost always something else: uncertainty. Teams working around a policy they don’t fully understand. Security finding out about a tool after it’s already in use. Visibility removes that friction for everyone, not by adding more rules, but by replacing guesswork with a clear picture everyone can act on.

AI adoption isn’t going to slow down, and it shouldn’t have to. The companies that will handle this well aren’t the ones trying to contain AI. They’re the ones building governance that moves at the same speed adoption does.

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Author Bio: Nidhi Jain is the CEO and Co-founder of CloudEagle.ai, an AI-powered AI governance, SaaS management, and SaaS security platform that gives enterprises one command center to discover, secure, govern, and optimize their entire SaaS and AI stack, including AI usage control and both human and non-human identities. CloudEagle.ai is a Y Combinator company (W22).