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Use generative AI at work only through an organisation-approved tool and workflow, and share the minimum information needed for the task. Before you paste a prompt, upload a document or connect an integration, identify what data it contains, why it is needed, where it will travel and who may access it. Remove identifiers and unnecessary detail whenever possible. If your policy does not clearly permit the use, stop and ask your privacy, security, legal or IT contact.
A chat box is an interface, not a privacy boundary. A work request can involve the AI provider, cloud storage, plug-ins, browser tools, meeting systems, subprocessors and internal data stores. Treat the complete data flow as the thing you are approving.
Why an AI prompt can become a data-protection issue
Typing into a chatbot can look like a one-to-one conversation, but the service may process the prompt across several components and suppliers. An uploaded spreadsheet may pass through file storage, malware scanning, search or retrieval services, model infrastructure, logging systems and administrative consoles. A connector can also make information available that is not visible in the text box.
The relevant question is therefore not “Is this tool branded for business?” It is “What information is entering which systems, for what purpose, under whose access and retention rules?” A business label does not by itself establish that prompts are deleted, excluded from model training, inaccessible to provider staff or stored in an acceptable location. Those settings must be confirmed in your organisation’s approved configuration and current contract.
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The Information Commissioner’s Office (ICO) describes security for AI as a contextual, holistic exercise because interconnected components and third-party relationships create risks that a single product setting cannot answer. Its guidance is UK-focused, reflects data-protection good practice rather than a universal legal rule, and is under review following the Data (Use and Access) Act. Other legal and contractual duties may apply.
A safe employee workflow, from task to prompt
1. Check the policy and approved tool
Start with your organisation’s acceptable-use, information-security and privacy policies. Confirm both the tool and the particular use case are approved. Approval for drafting public copy does not automatically cover customer records, source code, legal advice, health information or confidential strategy documents.
If the policy is silent or ambiguous, do not guess. Ask the designated privacy, security, legal or IT contact what classification is allowed, which account or workspace to use, whether files may be attached and whether an integration is permitted. This pause is a practical application of risk-based governance, not a claim that every organisation must use identical rules.
2. Classify the material before sharing it
Assume information is sensitive until your policy says how it may be handled. Typical high-risk categories include:
- Customer, patient, student or employee records.
- Names combined with contact details, account numbers, identifiers or transaction histories.
- Passwords, API keys, access tokens, private certificates and security-test data.
- Unpublished financial results, pricing, merger plans, product roadmaps and investor material.
- Source code, architecture diagrams, vulnerability details and proprietary algorithms.
- Legal advice, litigation material, regulated records and information subject to a confidentiality agreement.
These are practical categories, not a universal classification scheme. Your organisation may define additional labels or allow some uses under controlled conditions.
3. Ask whether the original data is necessary
Define the task in one sentence, then reduce the input to what is essential. A model that needs to rewrite a paragraph usually does not need the entire contract. A request to calculate a formula may need synthetic numbers rather than a customer export. A request to identify themes may need a short, de-identified sample rather than every message.
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The ICO’s data-minimisation guidance states: “The data minimisation principle requires you to identify the minimum amount of personal data you need to fulfil your purpose, and to only process that information, and no more.” Minimisation still applies when an AI system performs the processing.
4. Remove identifiers and use placeholders
Before sending text, replace real values with neutral labels:
- “Client A” instead of a person’s name.
- “Account 001” instead of an account number.
- “Region North” instead of a small location that could identify someone.
- “Project Orion” or “the internal migration project” instead of an unpublished project name.
- Fictional amounts and dates when the exact figures do not affect the task.
Check the remaining context. Removing a name alone may not anonymise a person if a rare job title, date, location, incident description or combination of facts can identify them. Keep a secure mapping between placeholders and real records only where the business process requires it; do not paste that mapping into the AI tool.
5. Write a narrow prompt
State the role, task, constraints and desired format without adding background that the model does not need. For example:
Rewrite the following de-identified paragraph in plain English for a general audience. Keep the meaning, do not add facts, and return one paragraph: [text]A narrow prompt reduces exposure and makes human review easier. Do not include “just in case” context, hidden instructions from a document or a full email thread when a short excerpt will answer the question.
6. Check files, connectors and integrations separately
An attachment, browser tool, meeting-transcript feature, plug-in or connector can expose a broader data set than the visible prompt. Before enabling one, confirm:
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- Which repositories, mailboxes, calendars or tickets it can read.
- Whether it can write, send, publish or delete data, rather than only read it.
- Which provider and subprocessors receive the content.
- How logs, cached copies, embeddings and generated files are retained and deleted.
- Whether the integration is covered by your organisation’s approval.
Use the smallest permitted scope. A connector that can search an entire drive is not necessary for a task involving one approved folder.
7. Review the output before anyone relies on it
AI output can be inaccurate, incomplete or unexpectedly disclose information from the input or connected context. Check facts, calculations, citations, confidentiality and audience before forwarding or publishing it. Treat generated code as untrusted until it passes your normal security and quality checks.
Do not paste generated text directly into a decision affecting a person—such as hiring, discipline, benefits, credit, access or performance assessment—without the human review, documentation and safeguards required by your organisation and applicable law. The official guidance supports governance and risk review but does not prescribe one universal workplace output-check procedure.
8. Report an accidental disclosure promptly
If sensitive information was pasted into an unapproved tool, or an approved workflow behaved unexpectedly, use the organisation’s incident-reporting route immediately. Include what was shared, when, which account or workspace was used, who may have accessed it and any files or integrations involved. Do not conceal the event or independently delete logs and evidence before receiving instructions; those actions can hinder assessment and response.
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No universal “never” list replaces your policy, but the following should be treated as prohibited unless a documented, approved workflow specifically allows them:
- Passwords, one-time codes, private keys, API tokens and recovery codes.
- Unredacted identity, health, financial, payroll or disciplinary records.
- Customer or employee exports containing direct or indirect identifiers.
- Non-public acquisition, litigation, regulatory, financial or safety information.
- Proprietary source code or vulnerability details that are not covered by an approved coding assistant.
- Material received under a non-disclosure agreement when the agreement does not permit the proposed disclosure.
Even when a tool is approved, upload only the fields and rows needed for the stated purpose. Approval is not a licence to transfer an entire database.
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How an organisation should assess an AI use case
Map the complete data flow
Document the tool, account type, provider, connected services, data entering and leaving the system, storage locations, retention points, subprocessors and people whose information may appear. Include prompts, attachments, conversation history, telemetry, audit logs, retrieved documents, generated files and support access.
Security cannot be judged by a single checkbox. The ICO emphasises that AI systems’ connected components and third-party relationships require holistic risk management. Identify what happens when a provider outage, misconfiguration, compromised integration or support request occurs.
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Define the purpose at each lifecycle stage
For personal data, record a specific, legitimate purpose before processing and test whether the use is necessary, lawful, transparent and consistent with people’s reasonable expectations. Model development, adaptation or fine-tuning, evaluation, deployment, monitoring and support can represent different purposes. A purpose that justifies drafting an internal summary does not automatically justify using the same conversations to improve a general model.
Document who is responsible for each stage, what data is required, how long it is retained and how a person can exercise applicable rights. De-identification and other privacy-enhancing techniques can reduce exposure, but they do not remove every security, re-identification, accuracy or governance risk.
Build minimisation into procurement and deployment
Require the project team to identify the minimum personal data and permissions needed before purchase or configuration. Prefer controls that make the safe path easy: restricted workspaces, role-based access, short retention, disabled public sharing, connector allow-lists, secret scanning, audit logs and tested deletion procedures. Reassess the design when the model, provider, integration or intended purpose changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to ask a provider and your procurement team
Do not infer answers from marketing language or a familiar brand. Obtain the current product terms, data-processing agreement, security documentation and the exact settings for your account and region. Record the answer, owner and review date.
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| Decision area | Questions to verify |
|---|---|
| Retention and deletion | How long are prompts, files, outputs, logs, backups and embeddings kept? Can the organisation set a shorter period and verify deletion? |
| Model training | May prompts or uploaded files be used to train, evaluate or improve a model? What is the default, and what contractual control changes it? |
| Human and subprocessor access | Can provider staff or subprocessors view content? For what support, safety or legal purposes, with what controls and records? |
| Data flow and location | Which countries, regions and cloud services process or store the data? Does a connector send it outside the approved boundary? |
| Administration and audit | Are single sign-on, role controls, export restrictions, logging, alerting and e-discovery available to administrators? |
| Integrations | What can each connector read or change, and can access be limited to a workspace, folder, field or time period? |
| Incidents and exit | How are incidents reported, how quickly are customers notified, and how can data be returned and securely deleted when the service ends? |
| Purpose and classification fit | Does the configuration meet the organisation’s requirements for this specific data classification and business purpose? |
These are evaluation axes, not a regulator-issued ranking of products. A tool suitable for public, low-risk text may be unsuitable for confidential or regulated material.
Can I paste work information into ChatGPT?
Only if your organisation has approved the specific account, configuration and use case. Remove unnecessary personal and confidential information, use placeholders or synthetic examples where feasible, and confirm retention, training, access, deletion and integration controls. A personal account or a business-branded interface is not evidence that the content is permitted.
Is it safe to upload company documents to an AI tool?
Safety depends on the document, purpose, data flow and approved controls. Check whether the file contains identifiers, secrets, contractual restrictions or information about people; whether the tool and upload path are approved; and whether the file can be accessed, retained, used for training or passed to connected services. If those facts are not established, do not upload the original document.
Workplace monitoring is a separate privacy activity
Monitoring employees’ AI use through email, chat, browser activity or other work messages creates its own processing operation. UK ICO guidance says an employer considering such monitoring should complete a data protection impact assessment (DPIA), inform workers about the purposes and methods, and consider whether less intrusive network or metadata signals could answer the management question before inspecting message content.
This guidance is UK-specific and should not be presented as law in every jurisdiction. Monitoring must have its own purpose, necessity, access controls, retention period and transparency notice; an organisation cannot treat it as an automatic extension of its AI-tool approval.
A practical go/no-go checklist
- Purpose: Can I state exactly why the AI processing is needed?
- Approval: Is this tool, account, feature and use case approved?
- Classification: Do I know what personal, confidential, regulated or secret data is present?
- Minimisation: Can a shorter excerpt, summary, placeholder set or synthetic example do the job?
- Identity protection: Have I removed direct and indirect identifiers that are not necessary?
- Data flow: Do I know which providers, integrations, locations and storage systems receive the content?
- Controls: Are retention, training, human access, deletion and audit settings documented for this account?
- Human review: Who will verify the output before it affects a customer, employee, decision or publication?
- Escalation: Do I know how to report an accidental disclosure or unexpected output?
If any answer is unknown, keep the material out of the tool until the responsible team resolves the gap. Responsible workplace AI is not about avoiding useful systems; it is about matching the data, purpose and permissions to a workflow the organisation can explain and control.
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