AI & Security
The Hidden Risk of Shadow AI at Work — Part 1
An employee’s helpful AI shortcut can put company information, decisions, and systems outside your control. Here is how to bring AI use into the open without shutting down its benefits.
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AI narration by Nexus · Tech Pulse intro/outro. Full transcript below.
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Imagine a sales manager racing to finish a proposal. They upload a customer spreadsheet to a personal AI account and ask for a summary. The answer arrives in seconds. The proposal goes out on time.
There is no malicious intent in this hypothetical example. But the business may now have customer information in a service nobody reviewed, under an account nobody manages.
That is how a helpful shortcut can become a security problem.
For small and midsize businesses, the practical question is straightforward: Do you know which AI tools your team uses, what information goes into them, and what those tools can access or change?
What is shadow AI?
Shadow AI is work-related AI use outside a company’s approved tools and oversight. It can include a personal chatbot account, an AI browser extension, a meeting assistant, or an AI feature switched on inside existing software. The UK’s National Cyber Security Centre describes the issue as AI use that falls outside approved systems and processes.
The word “shadow” can sound deliberate. An employee may simply be solving a problem faster than the company’s approval process allows. That makes a clear, usable path to approved AI especially important.
Four places the risk shows up
The following scenarios are hypothetical, not accounts of actual customer incidents.
1. Sensitive information leaves your control
A manager pastes employee performance notes into a writing assistant. An estimator uploads a confidential pricing sheet. A technician shares a support log containing customer details or credentials.
Before approving that use, the company needs to understand how the service handles inputs, uploaded files, and outputs. Model training is only one question. Retention, human access, sharing settings, and connected third parties also matter.
Avoid assuming that every AI provider trains on everything submitted. For example, OpenAI says its business offerings do not use business data for model training by default. That promise is distinct from retention and access controls, which still need review for the specific product and account.
2. A polished answer becomes an unchecked decision
Imagine an AI-generated proposal that quietly invents a service capability, or a spreadsheet formula that produces a plausible but incorrect margin. If nobody checks the work, the error becomes the company’s error.
NIST identifies confidently incorrect content, privacy risks, and overreliance on AI among the concerns organizations should manage. Treat generated content as a draft requiring review, with more scrutiny as the consequences increase.
3. A convenient integration gets excessive access
A hypothetical meeting assistant asks to connect to a calendar, mailbox, and shared drive. Someone clicks “Allow” to save time without checking what those permissions cover.
The concern grows when an AI tool can send messages, edit records, or trigger workflows. Review exactly which resources it can read and which actions it can take. Joint guidance co-authored by CISA and international partners emphasizes limiting agent privileges rather than granting broad access.
Canadian Centre for Cyber Security · Careful adoption of agentic AI
4. Small subscriptions create unmanaged dependencies
Consider a team buying an AI service on a company card without a security or purchasing review. Months later, it holds important work, renews automatically, and has only one administrator who is leaving the business.
This is shadow procurement: the company has adopted a supplier without assigning ownership for the account, its data, or its eventual exit. Even an inexpensive tool deserves a named owner and an approval decision.
A practical starting plan for your business
You do not need a large committee to begin. Use these steps to make expectations clear and give employees a workable alternative to improvising.
1. Ask what people are already using
Start a constructive conversation with each team. Ask which tools help them, which tasks they support, whether they use personal or company accounts, and whether anything connects to business systems. Include free trials, browser extensions, and AI features in software you already pay for.
Keep a simple inventory with an owner for each tool. NIST’s AI Risk Management Framework includes inventories, defined responsibilities, staff training, and ongoing review as elements of AI governance.
2. Publish a short, specific policy
Give employees answers they can use during a busy day:
- Which tools and account types are approved?
- What information may they enter, and what requires additional approval?
- Which outputs need review before use?
- Who approves integrations, new subscriptions, and expanded permissions?
- Where should someone report a mistake?
Use examples from your own workflows. “Do not paste customer account records into an unapproved chatbot” is clearer than “use AI responsibly.”
3. Offer approved tools that solve real problems
Identify a few useful, lower-risk tasks, such as drafting from public information or brainstorming with fictional examples. Provide company-managed access where appropriate and make the request process for new tools easy to find.
Ask employees to flag gaps in the approved options. A policy becomes more workable when people have a supported way to get their work done.
4. Check the settings and permissions
Before approval, document the product tier, training terms, retention and deletion options, access controls, and third-party connections. Check multifactor authentication and how accounts are removed when employees leave. Grant only the permissions needed for the approved task, and recheck when features or terms change.
5. Practice review with real examples
Run a short exercise using fictional business data. Ask staff to spot an invented claim, verify a calculation, and remove unnecessary details before prompting. Assign responsibility for checking customer-facing work. Require appropriate testing and technical review before using generated code or automation in production.
6. Make reporting easy
Tell employees to report suspected sensitive uploads, unexpected integrations, or consequential output errors promptly to a named contact. Ask for the tool, account, approximate time, and type of information involved through your approved reporting channel. Avoid spreading the sensitive content further. Have IT or security coordinate containment and preserve relevant evidence.
Start with one conversation
At your next team meeting, ask: “What AI tools are helping you work, and where are our current options falling short?”
Use those answers to choose approved tools, set practical boundaries, and give someone ownership of the process. Your team can gain the benefits of AI with a much clearer understanding of where company information goes and who remains responsible for the work.
Sources & further reading
These resources provide context for the data-handling and governance principles discussed above.
- NCSC · The hidden risks of shadow AI
Guidance on AI use outside approved systems and processes.
- OpenAI · Enterprise privacy
Business data commitments; review the specific product and account terms.
- NIST · Generative AI Profile (PDF)
Generative AI risks, including confidently incorrect content, privacy, and overreliance.
- Canadian Centre for Cyber Security · Careful adoption of agentic AI
Joint security guidance co-authored by CISA and international partners.
- NIST · AI Risk Management Framework Core
Governance practices including inventories, responsibilities, training, and ongoing review.
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