Solutions · Roll out an AI usage policy

Roll out an AI usage policy that is enforced, not just published

An AI usage policy sets which teams may use which AI models, tools and data, and what each may spend. AgentNuro turns your policy into rules. See where AI is used today, write your own policies as versioned rule bundles, test them in a dry run, then enforce them on every request through the AgentNuro Gateway, with a kill switch when you need one.

Get your free AI policy gap report See the rollout plan

Starts read-only. Nothing is enforced until you publish.

app.cloudnuro.ai/policy Policy v3

Your policy, as rules

example rules and sample data, for illustration only

Card and Social Security numbers never go into a prompt.payload matches pii.card | pii.ssn Would deny 4 requests today

Engineering uses approved models only.model not in approved.engineering Would reroute 1,286 requests today

API keys never come back in a response.response matches secret.apikey Would redact 9 requests today

Support Ops hears about it at 90% of budget.project.spend.mtd > budget × 0.9 Would alert 2 requests today

EU customer data is processed in the EU.data.class = eu and region ≠ eu-west-1 Would deny 0 requests today

Dry run: rules are checked against live traffic and the results are recorded, but nothing is blocked or changed.

Why do most AI usage policies fail after launch?

Writing the policy is the easy part. It gets approved, shared and filed, while AI use keeps growing in places the document never reaches.

No one knows what is in use

New AI tools, models and agents arrive faster than anyone can review them. A policy cannot cover what you cannot see.

One rule for everyone

Engineering, Support and Legal need different models, budgets and data rules. A single document cannot express that, so it stays vague.

Switching it on feels risky

Enforcement that might break a production app gets postponed. The policy stays advisory, and everyone knows it.

No evidence it is followed

Auditors and boards ask what was used, which rule applied and who changed the policy. A PDF cannot answer any of that.

What should an AI usage policy cover?

Seven clauses that most enterprise AI usage policies, also called AI acceptable use policies, share. Each one maps to something AgentNuro can show or enforce. Treat them as a starting point: you write the rules that fit your organization.

Policy clause

What it says

How AgentNuro applies it

Where

Approved tools and models

Which AI providers, models and agents are approved, and for whom.

Insight discovers every AI tool, model and agent in use and flags the unsanctioned ones. Control reroutes or denies requests to models outside a team’s approved list.

Insight Control

Sensitive data

What data must never be sent to an AI model, or come back from one.

Control detects personal data such as card numbers, Social Security numbers and email addresses, and secrets such as API keys, in prompts and responses, then redacts or denies.

Control

Data residency

Where data may be processed.

Rules match the data class and the region of the route, and deny requests that would leave the permitted region.

Control

Spend and token limits

What each team, project or agent may spend.

Insight tracks spend by user, team, project, model and agent against budgets and alerts owners. Control truncates oversized requests and enforces spend ceilings on every request.

Insight Control

Agent accountability

Who owns each agent and what it may do.

Insight lists agents on Gemini Enterprise, AWS Bedrock, Azure AI Foundry and Power Platform with their owners. Control rules can log or stop Gateway traffic from agents with no named owner.

Insight Control

Incidents and exceptions

What happens when something goes wrong.

Rules alert owners by severity. The kill switch stops AI traffic through the Gateway in one step, by hand or automatically when a policy condition is met.

Control

Review and change control

How the policy is updated, tested and approved.

Bundles are versioned. Draft a change, compare it with the live version, test it in a dry run, then publish, with a record of who published what and when.

Control

Insight is read-only and works through provider, billing and identity APIs. Control enforces in real time on traffic routed through the AgentNuro Gateway.

How do you roll out an AI usage policy?

Five phases, from a read-only baseline to enforcement you can prove. Nothing reaches your users until you publish, and you can roll out one bundle at a time.

Phase 03AgentNuro Control

See what each rule would do, before it does it

You doRun the draft against live traffic and review which rules would match, how often and for whom.

AgentNuro doesRecords every would-be match with its rule and severity, without blocking or changing a single request.

Done when: No rule surprises anyone

Dry run results, 24 hourssample data, for illustration only

Rules tested

26

Would match

2,140

Rules tuned

3

The approved-models rule would reroute 1,286 requests a day, most from one project. Approve that model for the project, or tell the team before you publish.

How do you write AI policies as rules?

Every rule is a condition, a scope and an action. Rules live in bundles that you draft, version and publish. The options below are examples to try. You can write your own conditions on request, response, identity, cost and routing attributes.

Build a ruleexample options, try a combination

When

For

Then

When a prompt contains card or Social Security numbers, on all routes, deny the request.

if payload matches pii.card | pii.ssn scope all routes then DENY

Bundles, layered by purposeexample bundles, for illustration only

Org-wide baseline v38 rules · all routesEnforcing

Sensitive data v56 rules · all routesEnforcing

Cost ceilings v27 rules · by projectEnforcing

EU data residency v15 rules · EU tenantsEnforcing

Engineering pilot draft v24 rules · EngineeringDry run

Bundles evaluate top to bottom. The first matching deny wins.
Each version is kept. Compare a draft with the live version before you publish.
Switch a single rule off without unpublishing its bundle.

What happens when a request breaks the policy?

You choose the response for each rule, from a quiet log entry to a hard stop, and give it a severity. Many rules can guide rather than block: reroute to an approved model, redact the sensitive part, or alert the owner, so people keep working.

GuideStop

Redact

Masks the sensitive part and lets the rest continue.

Use for: email addresses, secrets in responses

One request, this response

RequestRedactSensitive part maskedthen sent

The kill switch

Stop AI traffic through the Gateway in one step when something goes wrong, then restore it when you are ready. Trigger it by hand, or let a policy trigger it for you.

An admin or owner stops AI traffic through the Gateway during an incident, investigates, and restores it with the same switch.

kill switch: on · by admin · during incident review

Where does your AI usage policy apply?

Rules are enforced in real time on AI traffic that routes through the AgentNuro Gateway. Everything else is still discovered and reported, so you can see what sits outside your policy and move it onto governed routes.

AgentNuro Control · enforced in real time

Apps, internal tools and agents your teams build on model APIs, routed through the Gateway. Every request is checked against your published bundles, across 83+ model providers.

Examplesinternal apps, product features, custom agents

Policy actionsdeny, redact, reroute, alert, log

When rules applyevery request, in real time

Kill switchyes

AgentNuro Insight · discovered and reported

AI tools and agents that run outside the Gateway, read through provider, billing and identity APIs. Insight applies policy softly with tags, alerts and budgets, and shows where usage still needs a governed route.

Exampleslicensed Copilot, Copilot Studio agents, unsanctioned tools

Policy actionstags, alerts, budgets

How it connectsread-only APIs

Changes for usersnone

AI spend on governed routessample data, for illustration only

Through the Gateway, enforced 64%Sanctioned, monitored in Insight 24%Unsanctioned, to review 12%

How do you prove the AI usage policy is being followed?

Boards and auditors ask for evidence, not a policy document. AgentNuro records which rule matched each request, what action it took and how severe it was, plus every change to the policy itself.

app.cloudnuro.ai/policy/evidence Audit ready

Policy evidence · last 30 days

sample data, for illustration only

All bundles

Rules enforcing

26

of 27, one switched off

Requests checked

2.4M

through the Gateway

Rule hits

21,480

across 5 bundles

Critical hits

18

all denied

Hits by rule

Answer repeat prompts from cacheServe from cache14,951

Approved models onlyReroute4,870

Mask email addressesRedact1,310

Requests over 8,000 tokensTruncate290

Project budget at 90%Alert41

Card or SSN data in a promptDeny18

Version history

Sensitive data v5Oct 2, 2026

Added a rule for secrets in responses. Changed email addresses from deny to redact.
published by m.alvarez@example.com
Cost ceilings v2Sep 24, 2026

Raised the Support Ops budget threshold after a dry run.
published by j.okafor@example.com
Org-wide baseline v3Sep 18, 2026

Switched off the agent owner rule while owners are assigned.
published by m.alvarez@example.com

Frequently asked questions

An AI usage policy, often called an AI acceptable use policy, sets out which AI tools and models people may use, what data they may share with them, what each team may spend, and what happens when the rules are broken. AgentNuro turns those clauses into rules that can be tested and enforced.

Start with visibility: AgentNuro Insight discovers every AI tool, model and agent in use with read-only access. Then write the policy as rules in AgentNuro Control, test them in a dry run, and publish. The AgentNuro Gateway applies each rule to every request in real time.

You write your own. The rules on this page are examples. Each rule combines a condition, a scope and an action, and conditions can use request, response, identity, cost and routing attributes, so the policy matches how your organization works.

Yes. Draft a bundle, compare it with the live version, and run it in a dry run against live traffic. You see which rules would match, how often and for whom, without blocking or changing any request. Publish only when the results look right.

Only where you choose to. Each rule has its own action: log, alert, serve from cache, reroute, truncate, redact or deny. Many policies guide rather than block, for example by rerouting to an approved model or masking an email address, so work continues.

Yes, for traffic through the AgentNuro Gateway. Rules can detect personal data such as card numbers, Social Security numbers and email addresses, and secrets such as API keys, in prompts or responses, then redact or deny them.

Yes. Every rule has a scope, such as all routes, a team, a project, a tenant or a region. Bundles evaluate top to bottom and the first matching deny wins, so you can keep an org-wide baseline and add stricter or looser rules for specific groups.

Either a person or a policy. An admin can stop AI traffic through the Gateway in one step during an incident, or a rule can trigger the kill switch automatically when its condition is met, such as spend passing a ceiling or an anomaly in request volume.

Not to start. AgentNuro Insight is read-only and shows every AI tool, model and agent in use, with tags, alerts and budgets as soft policy. Real-time enforcement, such as redaction, rerouting, denial and the kill switch, applies to traffic routed through the Gateway with AgentNuro Control.

No prompt content is stored. When traffic routes through the Gateway, prompts and responses are evaluated in real time against your rules and are not kept. Insight reads usage, spend and agent details through provider, billing and identity APIs.

Find the gaps in your AI usage policy.

Get a free AI policy gap report: every AI tool, model and agent in use, and which ones your policy does not cover yet. Read-only, nothing is enforced.

Get your free AI policy gap report