Turn a discovered AI tool into a reviewed decision
Finding an unfamiliar AI tool is the start of a review. The useful next question is what someone is doing with it, through which account and with which information. Approve a defined use case rather than treating a recognizable vendor name as blanket permission.
For IT owners, security reviewers and application sponsors
A research assistant appears in the usage inventory
Several analysts are using an AI research service. Procurement has no corresponding record, and the team says it saves time when preparing market summaries.
What you are working with
- The discovered service and the people who can explain its use.
- Examples of task types, without copying confidential prompts.
- The account arrangement and any connected information sources.
A safer approach
- Find a business sponsor for the actual research workflow.
- Review public-source research separately from internal document uploads.
- Publish the approved use and restrictions in the existing tool register.
Expected outcome: The organization can explain which research activity is permitted and which missing evidence prevents broader approval.
Work through the procedure
Validate the discovery with its user
Check that the observation refers to the expected service and a current task. Ask whether use occurs in a browser, native app or integration. A catalog entry alone does not establish the account, data uploaded or importance of the workflow.
Identify the information and access involved
List the data categories, sources and people affected. Distinguish an employee asking about public material from granting access to internal files. Record requested permissions and account ownership; do not assume a login domain proves the purchased plan or administrative control.
Complete a use-case review
Give the sponsor a focused questionnaire covering the task, provider evidence and existing alternatives. Ask the relevant security and procurement owners to resolve uncertainties. If only public-data use is supportable, record that boundary instead of inventing assurances about internal-data handling.
Publish and verify the decision
Update the tool register with the sponsor, allowed data, approved route and next review trigger. Explain the outcome to users and test any chosen technical policy on a harmless example. Keep deferred requests visible so they do not vanish into informal approvals.
What to check before proceeding
1. Someone owns the proposed use
- Ready when
- A sponsor can explain the value, users and intended input.
- If the check fails
- Keep the record under review and request a business owner.
2. Evidence matches the account being used
- Ready when
- The reviewed provider terms and settings apply to the actual account arrangement.
- If the check fails
- Resolve the mismatch before approving confidential information.
3. Users can find the resulting boundary
- Ready when
- The register describes permitted tasks and restrictions in plain language.
- If the check fails
- Publish a usable decision before marking the review complete.
Common mistakes to avoid
- Approving every feature of a vendor because one low-risk task passed review.
- Treating a discovered domain or catalog risk label as proof of a provider's contract, plan or data-processing behavior.
Evaluate this workflow with Aona
Where Aona can help
Aona's discovery and usage information can provide a starting point for identifying AI tools in use. Its supported policy and data-protection paths can help implement a reviewed decision where coverage is confirmed.
What to confirm
Discovery does not perform procurement approval or guarantee enforcement across the catalog. Provider terms, permissions and account ownership still require a reviewer to establish the facts.
Turning this policy into an operational rollout?
Discuss the teams, devices and AI tools in scope, who will own the policy, and which deployment and evidence requirements need to be met before rollout.