Use generative AI at work only through a documented lifecycle: approve specific use cases, classify the data and people affected, test the system, limit access, require qualified human review, monitor results, and stop or change the use when risk exceeds your tolerance. Treat every model response as draft work rather than an authoritative fact.
This approach works for a small business using one chatbot and for an enterprise with connected agents. The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) is voluntary and sector-neutral; its Generative AI Profile, NIST AI 600-1, published July 26, 2024, adapts the framework to risks that are new to or intensified by generative systems.
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What a safe workplace AI program looks like
Safety is not a single setting in a chatbot. It is a set of controls around the entire use case: the business purpose, information supplied, model and vendor, connected systems, people making decisions, and the way outputs are monitored after launch.
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- Govern: assign an accountable owner, define acceptable risk, approve uses, and document decisions.
- Map: identify data, users, affected people, suppliers, integrations, jurisdictions, and possible harms.
- Measure: test accuracy, privacy, security, bias, accessibility, reliability, and misuse scenarios.
- Manage: apply access controls, human review, training, monitoring, incident response, and improvements.
Do not allow a tool simply because it is popular or free. Approve a purpose and a data flow, not an abstract technology category.
The main risks to control
Confidentiality and privacy
A prompt can contain personal information, customer records, health or financial details, source code, credentials, or trade secrets. Sending that content to a model-as-a-service provider may expose it to collection, retention, human access, cross-border processing, or training uses that your organization did not intend. Federal Trade Commission (FTC) privacy guidance published January 25, 2024 warns that providers must honor their commitments about collection, use, retention, and training.
Minimize what you send. Remove names, account numbers, exact dates, identifiers, secrets, and unnecessary context; use synthetic or redacted examples when the task does not require real data. Record the approved data flow, including the system receiving the prompt, the region in which it is processed, retention and deletion rules, and the people or services that can access it.
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Generative systems can produce fluent, plausible statements that are wrong, incomplete, or supported by citations that do not exist. Errors in calculations, code, legal or regulatory explanations, medical content, financial analysis, and customer communications can become operational or safety incidents.
Set a named human reviewer for every use case that can affect a customer, employee, supplier, payment, security control, legal position, or public statement. The reviewer must check source documents, calculations, citations, assumptions, and code in an appropriate environment. Record the evidence used and reject or escalate an answer that cannot be verified.
Bias, discrimination, and exclusion
Unequal results can arise from training data, the prompt, the model, the surrounding workflow, or the way staff act on an output. A résumé-ranking assistant, for example, may disadvantage groups through proxies for protected characteristics or through unequal access to the application process.
Test representative groups and contexts before launch and after material changes. Document known limitations, provide a route for a person to appeal or override a result, and prohibit unreviewed automated decisions about people. UK guidance on responsible AI in recruitment, published March 25, 2024, specifically highlights bias, discrimination, digital exclusion, supplier evidence, and repeated assurance checks.
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Security and misuse
Prompt injection can cause a model to ignore instructions, reveal data, or misuse a connected tool. Other attack paths include malicious files, data leakage, insecure plug-ins, poisoned retrieval content, excessive permissions, and generated code with vulnerabilities.
Use least privilege: give an integration only the data and actions required for its approved purpose. Separate development, test, and production environments; require strong authentication; log prompts, tool calls, approvals, and outputs where lawful; set rate limits; scan generated code and files; and test common attack scenarios. Keep an incident plan that covers disabling the integration, preserving evidence, notifying affected parties, and restoring a known-safe configuration.
Copyright, confidentiality, and contract terms
Before uploading content or relying on generated material, determine who owns or licenses the input, whether the provider retains it or uses it for training, who can access it, where processing occurs, and what restrictions or indemnities apply. A marketing statement is not a legal guarantee. Your procurement, privacy, security, and legal teams should approve the actual contract and configuration.
Workforce and organizational effects
AI adoption can change work allocation, reduce opportunities to practise skills, increase surveillance, or damage trust if employees do not understand how outputs are used. Involve affected workers, explain monitoring, provide training and support, and measure whether the tool improves quality rather than merely increasing throughput. UK human-centred generative-AI de-risking guidance published June 4, 2025 includes a hidden-risk register and a monitoring approach for these effects.
Decide which uses are allowed
Create a three-level register that staff can consult before opening a tool. Each entry should name the tool, owner, purpose, data classes, connected systems, user group, decision affected, approval date, and next review date.
| Category | Typical treatment | Examples |
|---|---|---|
| Prohibited | Do not perform the use with generative AI. | Unreviewed decisions about hiring, firing, credit, benefits, safety, or access to essential services; entering credentials, secrets, or regulated records into an unapproved consumer service. |
| Restricted | Use only in an approved environment with a named owner, documented data flow, testing, and human sign-off. | Customer-support drafts, code changes, employee analytics, contract analysis, recruitment assistance, or any workflow connected to production systems. |
| Low risk | Permit with basic training and normal review. | Brainstorming, rewriting non-confidential text, creating outlines, or summarizing public material when no consequential decision follows automatically. |
Classify the output as well as the input. A harmless prompt can generate a sensitive result, such as a list of customers inferred to be in financial difficulty. Store and share outputs according to the most sensitive information they contain.
Information that should not enter an unapproved chatbot
- Passwords, API keys, private certificates, recovery codes, and other authentication secrets.
- Non-public source code, security configurations, vulnerability details, or incident evidence.
- Customer or employee identifiers, contact details, health information, financial data, payroll, or disciplinary records.
- Trade secrets, unreleased product plans, acquisition information, pricing strategy, and confidential contracts.
- Data covered by a law, regulator, court order, non-disclosure agreement, or customer instruction that has not been cleared for that service.
- Content whose copyright or license terms do not permit disclosure or processing by the provider.
If the task genuinely needs sensitive information, use an organization-approved deployment with contractual protections and technical controls, or complete the task without a generative model.
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Review a provider before connecting company data
Procurement should obtain written answers and supporting evidence rather than relying on a product page. Keep the review with the use-case approval so a later model or contract change triggers reassessment.
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| Review area | Questions to answer | Evidence to retain |
|---|---|---|
| Retention and training | Are prompts, files, outputs, and telemetry retained? Are they used to train or improve a shared model? Can retention be disabled? | Contract clauses, configuration screenshots, and the provider’s current policy. |
| Access and deletion | Which provider staff, subprocessors, or customers can access content? How are deletion requests handled and verified? | Access model, deletion procedure, service-level commitments, and audit reports. |
| Location and transfers | Where is data processed and stored? What regional controls and transfer mechanisms apply? | Region settings, subprocessor list, and applicable transfer terms. |
| Security | How are accounts, keys, networks, tenants, files, and logs protected? How are vulnerabilities and breaches handled? | Independent assurance reports, penetration-test summaries, encryption details, and breach-notice terms. |
| Permissions and integrations | Can the system read or change production data? Are tools scoped per user, role, record, and action? | Permission matrix, API scopes, environment diagram, and approval workflow. |
| Quality and fairness | What evaluation evidence exists for your language, domain, and user groups? How are model updates announced? | Test results, limitations, update policy, and your own evaluation data. |
| Audit and exit | Can you export logs, investigate incidents, suspend access, and retrieve or delete data if the service is withdrawn? | Log-retention settings, export tests, termination assistance, and reversibility plan. |
FTC business data-security guidance recommends taking stock of retained information, scaling down collection, locking retained data, disposing of it securely, overseeing service providers, and planning for incidents. Apply those controls to AI suppliers as you would to any other processor.
Implement the controls step by step
1. Build an inventory
Ask every department to list chatbots, coding assistants, image or audio generators, meeting summarizers, embedded AI features, APIs, browser extensions, and agents. Capture the owner, purpose, model or provider, data type, connected system, users, geography, and decision affected. Include unsanctioned tools discovered through expense records, network logs, or staff surveys; bring them into the approval process instead of assuming they are gone.
2. Classify uses and data
Apply the prohibited, restricted, and low-risk categories. Map each data element to your existing public, internal, confidential, or regulated classification. Specify whether de-identification is sufficient and who can approve an exception. Make the rule visible in the tool’s start page, intranet, and onboarding material.
3. Complete vendor and legal review
Resolve retention, training use, deletion, access, regional processing, security controls, breach notice, subcontractors, audit evidence, intellectual-property terms, and exit rights. Do not enable a connector until the permission scope and contract match the approved use.
4. Define human accountability
Name the role that owns the outcome, not just the person who configured the prompt. Write when review is mandatory, what evidence the reviewer records, which decisions require a second reviewer, and when the work must be escalated or rejected. A reviewer must have enough subject-matter expertise and time to challenge the model.
5. Evaluate before launch
Create a test set that represents real languages, formats, edge cases, protected groups, failure modes, and adversarial prompts. Measure factual accuracy, calculation correctness, citation validity, privacy leakage, refusal behavior, security boundaries, latency, and accessibility. Test both normal and deliberately misleading inputs. Set pass criteria in advance; a good average score does not excuse a catastrophic failure in a high-impact case.
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6. Lock down access
Use strong authentication, role-based or attribute-based permissions, least-privilege API scopes, rate limits, network and environment separation, and approved storage locations. Prevent a model from sending email, changing records, deploying code, or purchasing goods without an explicit approval step. Log the identity, time, prompt or input reference, model version, tools called, output, reviewer, and final action where legally appropriate.
7. Train users
Training should cover data classification, redaction, prompt hygiene, source checking, calculation checks, copyright, bias, accessibility, prohibited uses, and incident reporting. Demonstrate realistic failure cases rather than promising that a particular model is safe. Refresh training when the tool, policy, connected data, or legal environment changes.
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8. Monitor in production
Track accuracy samples, refusals, complaints, overrides, near misses, privacy events, unusual usage, permission changes, supplier updates, and performance differences among relevant groups. Compare results with the pre-launch baseline. Monitoring must be owned by a person who can pause the use case, not merely by a dashboard that no one reviews.
9. Respond to incidents
Define a simple reporting route and preserve the relevant prompt, input source, model version, tool calls, output, and human actions. Triage whether data was exposed, a decision was affected, or a connected system was changed. Disable the account, connector, or use case when necessary; notify security, privacy, legal, affected workers, customers, or regulators according to your obligations; then correct the process and retest before re-enabling it.
10. Improve continuously
Reassess after an incident, near miss, model update, provider change, new integration, regulatory development, or new use. Keep the decision record, risk assessment, test results, approvals, user notices, incident history, and review dates together so another team can understand why the control exists.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make human review meaningful
“Human in the loop” is not enough if the person merely clicks approve. Give reviewers the original source material, model limitations, confidence information when it is meaningful, and a clear authority to reject the result. Require them to verify:
- Every material factual claim against an authoritative source.
- Calculations by reproducing them in a trusted tool.
- Citations by opening the referenced document and checking that it supports the statement.
- Generated code through review, tests, dependency checks, and security scanning.
- Recommendations for unintended discrimination, accessibility barriers, and impacts on people who are not represented in the prompt.
For consequential workflows, retain a short review record: what was checked, which sources were used, what was changed, and who approved the final action.
Test for bias and accessibility
Define the groups and contexts that could be affected before testing. Compare error rates, refusal rates, ranking outcomes, tone, translation quality, and required user effort where those measures are relevant. Include people with disabilities and users with different languages, literacy levels, devices, and connectivity. Investigate any material disparity rather than averaging it away.
Provide an understandable notice when AI contributes to a decision, a way to request human review, and a route to correct inaccurate information. If you cannot explain the result well enough for an affected person to challenge it, do not automate the decision.
Account for workforce effects
Consult employees and representatives who will use the system or whose work will be measured by it. Explain what is logged, who can see it, how it affects performance decisions, and how long records remain. Assess whether automation removes useful training opportunities, concentrates difficult tasks in a few roles, or encourages unrealistic workloads.
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Compare tools on risk, not just features
When choosing between two or more systems, use the same test set and decision criteria. A feature-rich assistant may create more risk if it has broad permissions or weak auditability.
| Comparison axis | What to establish |
|---|---|
| Data retention and training | Prompt, file, output, telemetry, deletion, and model-training terms. |
| Security and access | Authentication, tenant isolation, encryption, role controls, key management, and breach response. |
| Accuracy and evaluation | Evidence for your language and domain, update behavior, and results on your representative test set. |
| Bias and accessibility | Testing methods, known limitations, user support, and appeal mechanisms. |
| Integration permissions | Read/write scopes, approval gates, environment separation, and ability to disable individual actions. |
| Audit logs | Events captured, export format, retention, tamper resistance, and administrator visibility. |
| Geographic processing | Storage and processing regions, subprocessors, and lawful transfer arrangements. |
| Incident support | Notification time, investigation assistance, evidence access, and recovery commitments. |
| Contract terms | Confidentiality, intellectual property, liability, indemnities, audit rights, and exit support. |
| Cost and reversibility | Total operational cost, training and review effort, data portability, and how work continues if the provider or feature is withdrawn. |
A policy employees can follow
A useful policy is short enough to use and specific enough to enforce. At minimum, state:
- Which tools and versions are approved, who owns them, and where to obtain access.
- Which data classes may, may not, or may conditionally be entered.
- Which uses are prohibited and which require documented approval.
- When human verification, second review, user notice, or an appeal is mandatory.
- How prompts, outputs, logs, and generated files must be stored and shared.
- How to report suspected leakage, fabricated output, discrimination, malicious content, or unsafe automation.
- How often the use case is reviewed and what events trigger an immediate reassessment.
Publish a decision record for each restricted use. It should identify the owner, purpose, affected people, data flow, provider terms, test results, controls, residual risk, approval authority, and next review date.
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First 30 days: establish control
- Inventory tools and shadow use.
- Publish the prohibited-data rule and an interim reporting channel.
- Freeze new high-impact integrations until review.
- Assign an executive sponsor, risk owner, security contact, privacy contact, and workforce representative.
Days 31–60: test and train
- Classify use cases and complete vendor reviews for the highest-risk systems.
- Create representative and adversarial evaluation sets.
- Configure authentication, permissions, logging, retention, and environment separation.
- Train users and reviewers, then run a controlled pilot with documented sign-off.
Days 61–90: monitor and improve
- Review pilot accuracy, incidents, overrides, accessibility feedback, and workload effects.
- Run an incident exercise that disables a connector and preserves evidence.
- Approve, redesign, or stop each pilot based on defined thresholds.
- Schedule recurring reviews and supplier-change notifications.
There is no reliable universal percentage for how often workplace generative-AI systems fail; performance depends on the model, task, data, and controls. Use your own measured results and incident record instead of an invented benchmark. As FTC Chair Lina M. Khan stated in remarks published in February 2024, “there is no AI exemption from the laws on the books.” Existing privacy, security, employment, consumer-protection, intellectual-property, and safety obligations still apply when a model is involved.
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