A blanket instruction to use AI responsibly leaves an employee with several unanswered questions. Can they summarise a client document? May they connect an assistant to a shared drive? Is the free version acceptable for the same work as the company’s paid account? Without practical answers, each person ends up making their own judgement.
Shadow AI describes AI use that takes place outside the organisation’s approved processes or visibility. Leaders can address it by making the approved route clear and useful. The aim is to understand which tasks people want to improve, decide what information and access those tasks require, and put appropriate boundaries around the use.
Begin with tasks, tools and information
Ask teams where AI already helps them or where they would like to use it. Include writing, coding, research, meeting notes and customer communication. The discussion should identify the tool, account type, information entered and any connection to company systems. Avoid turning the initial conversation into a search for somebody to blame.
A useful first inventory distinguishes public information from internal material, customer data and credentials. Record the person responsible for each use case and the business purpose. This makes the review specific enough to act on: summarising a public report presents a different decision from connecting an assistant to a folder of client contracts.
The NCSC’s guidance for leaders on AI and cyber security stresses the importance of understanding risks and treating security as part of the system’s lifecycle. For everyday AI use, that starts with knowing what the company is introducing and what it expects the tool to do.
Approve a use case with clear conditions
Approval should describe the work allowed, the account to use and the information that can be submitted. A vendor name on a list is not sufficiently precise. The same service may offer different settings, features and contractual arrangements, and a team can change how it uses an approved tool over time.
Suppose a hypothetical sales team wants help drafting follow-up emails. An initial approval could permit working from a staff-written summary that excludes confidential details, require review before sending and restrict use to the designated company account. If the team later wants to connect the assistant directly to its CRM, that should trigger a fresh decision about access.
NIST’s Generative AI Profile provides a structured resource for identifying and managing risks associated with generative AI. A business can apply that approach proportionately by documenting the purpose, likely failure modes and safeguards for each important use case.
Review permissions as carefully as prompts
A review should look beyond the text someone types into a chatbot. Ask whether the application can read files, access messages, retrieve shared content or take actions in another service. Check what access is necessary for the proposed task and how the company would remove it.
For a pilot, start with a limited group and clearly defined information. Decide who reviews the output and what the employee must verify before using it. A research summary may need source checking; a customer message may need factual and account-specific review; code may need the team’s normal security and quality checks.
Give staff a way to pause when a tool asks for unexpected access. The response should be clear: record the request, ask the designated owner and wait for approval before extending permissions. This helps keep an approved experiment from silently becoming a much broader deployment.
Make policy visible in everyday work
Visibility tools can help connect written rules to actual use. Noon Security provides visibility into employee AI and SaaS usage, supports approved and unapproved app classifications, and applies policies by team. For leaders assessing this kind of product, the key exercise is to follow an ordinary employee request from discovery to an agreed decision. Can the responsible team identify the application, understand who uses it and explain the relevant policy? Decide what action should follow each finding, who owns exceptions and how employees can request access. The resulting process should make approved AI use easier to understand and maintain.
Give the policy a review cycle
Assign a business owner to important AI uses and revisit them when the work, information or permissions change. New features can alter an application’s reach, and an experiment may become business-critical without an explicit decision. A review date creates an opportunity to check whether the original approval still fits.
Keep the employee guidance short and concrete. Include examples of acceptable work, information requiring additional approval and a contact for questions. Explain how to report an accidental upload or unexpected action. The person receiving that report should have a defined route to assess what was shared and what steps are possible.
Track the questions employees ask. Repeated requests can reveal a legitimate need for a better approved tool or a rule that nobody understands. Leadership should use that feedback to improve the programme. Practical AI guardrails work when people can see both the boundary and a usable route to completing their work within it.
