Treat it as a governance and access problem, not just a productivity choice. Approved tools should have reviewed permissions, sensitive data should stay inside monitored environments, and employees should understand that unapproved AI services and agents create unmanaged data paths.
Make shadow AI a training topic about control boundaries, not a novelty topic
Awareness programmes work best when they explain that shadow ai changes the organisation’s data and access posture, not just its software stack. Employees need to recognise that an unapproved chatbot, browser plug-in, code assistant, or agent can move sensitive material outside monitored workflows, even when the tool feels harmless or useful. A practical awareness message should connect everyday use to approval status, data classification, and the consequences of bypassing sanctioned routes.
The most effective programmes do not try to shame users for adopting new tools. They teach people how to tell the difference between approved AI services with reviewed permissions and unsanctioned services that create hidden data paths. That distinction matters because awareness only changes behaviour when staff understand which use cases are permitted, which kinds of data are prohibited, and which tool features, such as OAuth consent or file access, can quietly expand exposure.
Good awareness content should also explain that shadow AI is not limited to the model itself. It includes connected agents, browser extensions, SaaS add-ons, and copied prompts that can persist outside the intended environment. Shadow AI and AI Agent Discovery Guide is useful here because it frames the problem as discovery plus governance, which is exactly the mental model employees need before they can follow policy consistently.
What employees need to understand about approved use and data handling
A strong programme gives people a simple rule set: use approved AI tools for approved purposes, keep sensitive data inside monitored environments, and never assume a consumer AI service inherits the security controls of an internal platform. That rule set should be reinforced with concrete examples, such as source code, customer records, credentials, internal strategy, and regulated data, because abstract warnings are easy to ignore while realistic scenarios are easier to remember.
Awareness also needs to cover permissions, not only content. Many shadow AI cases begin with legitimate sign-in but excessive consent, overbroad file access, or the reuse of personal accounts for work tasks. When the programme explains why reviewed permissions matter, users are less likely to grant access casually and more likely to escalate before connecting a new AI service to corporate data. The AI Acceptable Use Policy Template is a practical companion because it translates those expectations into employee-facing rules about approved tools, data classes, and prohibited use.
Awareness should also connect the issue to governance, because shadow AI often appears when employees try to solve a real business problem faster than the approval process allows. If people do not understand where to request an exception, how a use case gets reviewed, or what happens after approval, they will keep using unofficial tools in parallel. A clear AI Governance Guide helps anchor the organisational side of the message by showing that approved use is a managed lifecycle, not a one-off sign-off.
How to make awareness effective in day-to-day behaviour
Awareness programmes should be built around the actual workflows where shadow AI appears: drafting, summarising, coding, research, note taking, and data transformation. The point is to show employees where shortcuts create risk, then give them a better default. If sanctioned tools are slower or harder to find than consumer AI, users will route around policy, so awareness should be paired with a visible approved-tools list and a simple escalation path.
Training should also reinforce that “AI at work” is not a single category. A text generator, a browser extension, a connected SaaS assistant, and an autonomous agent can carry very different permissions and consequences. Awareness that treats all of them as identical will fail to change behaviour. The better message is that any tool which can read, retain, transform, or export company data needs the same discipline normally applied to other managed services with access to sensitive information.
Where organisations already run security education, shadow AI should be woven into existing human-risk messaging rather than launched as a one-off campaign. That lets teams reinforce the same judgement repeatedly: if a tool is not approved, do not use it with corporate data; if a use case is legitimate, request an approved path. Security Awareness and Human Risk Management Guide is a strong fit for that approach because it supports behaviour change, not just policy awareness.
Risk and Threat Considerations
Shadow AI creates exposure because employees may unknowingly disclose sensitive information to services outside corporate monitoring, logging, retention, and contractual control. The risk is not just leakage, but loss of visibility into where the data goes next, who can access it, and whether it is reused to train or enrich another service.
Failure mechanism: Users connect unapproved AI tools or agents to corporate accounts, paste sensitive content into external prompts, or authorise broad OAuth scopes that create unmanaged data paths and durable third-party access.
Impact: The organisation can lose control over confidential information, weaken auditability, and increase the chance of downstream compromise if stolen tokens, exposed outputs, or connected agents are later abused.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, CIS Controls v8 and NIST SP 800-53 Rev 5 set the technical controls, while ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RR-01 — Roles, Responsibilities, and Authorities | Shadow AI awareness depends on clear ownership for approved use and exception handling. |
| PR.AA-01 — Identities and Credentials Are Issued, Managed, Verified, Revoked, and Audited | Shadow AI often creates unmanaged access through accounts, OAuth grants, and tokens. | |
| PR.DS-01 — Data-at-rest is Protected | Awareness must reinforce keeping sensitive data inside controlled environments. | |
| Recommendation — Assign clear ownership for AI approval, monitoring, and exception decisions. Control and review AI-linked credentials and consent grants continuously. Restrict sensitive data from unapproved AI tools and storage paths. | ||
| ISO/IEC 27001:2022 | A.5.10 — Acceptable use of information and associated assets | Shadow AI awareness is fundamentally about acceptable use and employee behaviour. |
| A.5.12 — Classification of information | Staff need data-class rules to know what cannot be entered into AI tools. | |
| Recommendation — Define and communicate acceptable AI use rules for staff and contractors. Map AI usage rules to information classification and handling requirements. | ||
| CIS Controls v8 | CIS-14 — Security Awareness and Skills Training | This topic directly concerns awareness content and behaviour change for employees. |
| Recommendation — Add shadow AI scenarios to security awareness and role-based training. | ||
| NIST SP 800-53 Rev 5 | AT-2 — Awareness Training | The question is about what awareness programmes should teach about shadow AI. |
| AC-20 — Use of External Information Systems | Shadow AI commonly involves external services used to process organisational data. | |
| Recommendation — Include shadow AI usage rules, data handling, and reporting expectations in training. Restrict and monitor employee use of external AI services for work data. | ||
Practitioner Guidance
What to prioritise: Teach employees the difference between sanctioned AI use and unsanctioned AI access, then make the approved route easier to follow than the unofficial one. If the approved path is not convenient, awareness alone will not hold.
What to verify: Confirm that the training reflects actual business workflows, approved tools, data classes, and exception routes. A generic “do not use shadow IT” message is too weak for AI because it does not address file access, consent grants, agents, or data retention.
Decision rule: If a tool can accept corporate data, connect to work accounts, or act on behalf of a user, treat it as a governed access problem and require review before use. If it cannot be monitored or supported, it should not be used for business data.
Practitioner takeaway: The goal is not to stop employees using AI, it is to ensure that AI use stays inside controlled, observable, and supportable boundaries.