AI Authorization Policy Generator

Can a manager in one branch read a customer record belonging to another branch? If you had to go and read the code to answer that, your authorisation rules are not written down anywhere. They exist as scattered conditions, and every new feature adds another one that may or may not agree with the rest.

What is AI Authorization Policy Generator?

It is a free page that produces permission models. You describe the roles, the things they act on and the rules your business actually applies, and it returns a policy: a matrix of who can do what, the ownership conditions attached, and the places the check has to happen.

The word policy matters. Authentication asks who you are and is answered once. Authorisation asks whether this person may do this thing to this specific record, and it has to be answered on every request. Writing it down as a model is what makes it consistent.

Why Use AI Authorization Policy Generator?

Because broken access control is consistently one of the most common serious weaknesses in web applications, and it is not caused by exotic attacks. It is caused by one endpoint that checks the role but never checks ownership of the record, added by someone who was in a hurry and had no policy to check against.

Having an explicit model prevents that in a mundane way. When the rules exist as a document, a new endpoint has something to comply with, a reviewer has something to check, and a test suite has something to encode.

How Does AI Authorization Policy Generator Work?

You describe roles, resources and rules, and it builds the model. The most productive way to write the input is as sentences about real situations rather than as a list of permissions, because situations carry the conditions that lists leave out.

The page uses the shared security options panel, so Security Focus, Strictness and the toggles apply, and the result appears in a card with a live word count and an export row underneath.

Three kinds of rule show up in almost every model, and they are enforced in different places.

Step-by-Step Guide

1. Open AI Authorization Policy Generator. Free, with no account step.
2. List the roles and say what each one is for, in business terms.
3. List the resources, and for each say what actions exist on it.
4. Write the conditional rules as sentences, including the exceptions people ask for.
5. Set Security Focus to Authorization and Strictness high, then generate.
6. Read the matrix and look specifically for cells you did not expect to be allowed.
7. Turn each rule into a test before you implement the checks.

Before the policy is treated as agreed, work through this.

• ✅ Every action on every resource has an explicit answer, including deny.
• ✅ Record level ownership is stated wherever it applies, not just role access.
• ✅ The default for anything unlisted is deny rather than allow.
• ✅ Administrative override is a named rule with an audit trail, not an implicit one.
• ✅ Rules that change with record state, such as published or locked, are captured.
• ✅ Every rule has a corresponding test that fails when the check is removed.

Key Features

Best Use Cases

Once the policy exists, the endpoints that enforce it need checking against it, and AI API Security Analyzer is the page for reviewing that surface.

Advanced Options Guide

Ten controls sit behind the accordion. Security Focus and Strictness shape the model, and Custom Instructions is where your business rules go.

Example Inputs

Rules written as situations beat rules written as a list. Here is the shape that works.

Multi tenant job scheduling app. Roles: owner, manager, technician, and an
internal support role. Resources: jobs, customers, invoices, and team members.

Owners do everything within their own tenant. Managers create and assign jobs
and read invoices but cannot change invoice totals. Technicians see only jobs
assigned to them, and can update status and notes but never the price.
Customers records are visible to owners and managers only. Internal support
can read anything except invoice payment details, and every support read must
be logged. Nobody can act outside their own tenant, ever. A job that has been
invoiced becomes read only for everyone except the owner.

The last two sentences are the valuable ones. Tenant isolation as an absolute rule tells the model that the check belongs in the query rather than the route. And the invoiced state rule is a condition on the record rather than on the role, which is exactly the kind of thing that gets forgotten and then discovered when a technician edits a billed job.

Example Outputs

With Security Focus on Authorization and Strictness high, the model arrives as a matrix plus the conditions that the matrix cannot express.

Resource: job
                  owner   manager   technician   support
  list              yes     yes      assigned      yes*
  view              yes     yes      assigned      yes*
  create            yes     yes         no          no
  assign            yes     yes         no          no
  update status     yes     yes      assigned       no
  update price      yes      no         no          no
  delete            yes      no         no          no

  * support reads are logged with actor, record and timestamp

Conditions that apply on top of the matrix
  T1  Every query is scoped by tenant_id. This is enforced in the data layer,
      not in the route, so a missing route check cannot leak across tenants.
  T2  assigned means job.technician_id equals the current user id, checked on
      the record after loading, never inferred from the request.
  T3  If job.invoiced_at is set, only owner may update any field.
  T4  Any denial returns the same not found response as a missing record, so
      the existence of another tenant's job is not revealed.

The conditions are where the design lives. T1 puts tenant scoping in the data layer, which means a forgotten check in a new controller cannot cause a cross tenant leak. T4 is the small choice that stops your permission model becoming an enumeration tool.

AIToolsay gives each job a page that already knows the domain, so this prompt box expects roles and rules rather than an open question. The options are security specific, with a strictness dial that decides how conservative the model is and toggles that control whether you get reasoning, enforcement examples or both. The model selector lets a second engine build the same policy when a permission looks too generous. It is free with no account step, which makes revising the model as the product changes practical rather than aspirational. Session history keeps each version listed under the result while you compare. The rest of the security tooling on AIToolsay is arranged the same way, so the authentication design before this and the code review after it are each a page away.

Frequently Asked Questions

Write down the permission rules you believe your application enforces, generate the model, then check three of them against the code. Most teams find at least one rule that exists only in their heads. The Telegram community is a good place to compare permission models, and the newsletter or push notifications will let you know when new security tools arrive.

Let AI Speak.