If your organization gives staff access to Claude — through internal tools, approved products, or the API — the single highest-leverage risk control is not a technical one. It is making sure every user carries a small, accurate mental model of what the system is, what it can be trusted for, and what to do when something feels off. This article lays out the core concepts that training should cover. It is recommended practice, not a compliance checklist; adapt it to your industry and policies.
Concept 1: what the model is — and is not
Employees need a working answer to "what am I talking to?" The honest version: a language model generates fluent, usually useful text, but it is not a database, it does not know your company's private facts unless they were provided in the conversation, and it can state wrong things with complete confidence. That failure mode — plausible, confident, wrong — is the one to drill, because it is precisely the kind of error human intuition is worst at catching.
The practical rule to teach: calibrate trust to verifiability and stakes. Drafting, summarizing, brainstorming, and reformatting are low-risk because the human can check the output directly against material they already have. Factual claims the employee cannot verify, calculations feeding decisions, and anything leaving the company under its name require verification before use. Legal, medical, financial, and HR determinations should never rest on model output alone — a qualified human owns those decisions.
Concept 2: what never goes into a prompt
This is where training must be concrete, because "be careful with sensitive data" is unactionable. Give staff a short, memorable prohibited list tied to your data classification — typically: customer personal data beyond what the approved use case covers, credentials and keys, unreleased financials, material non-public information, and anything under legal hold or special regulatory protection. Then tell them which tools are approved for which data, because the rules differ by surface: an approved internal tool built on the API is a different situation from a personal consumer account, and your policy should say so explicitly.
It helps to explain the "why" accurately rather than with folklore. For Anthropic's commercial API, official documentation states that conversation content is not retained by default beyond a short operational window (inputs and outputs are automatically deleted within 30 days by default), and retained data is not used to train models without express permission. Two nuances are worth teaching: content flagged as violating the Usage Policy can be retained much longer, and explicitly submitting feedback (for example, a thumbs up/down) stores that data for years and can permit its use. On Bedrock, Google Cloud, and Foundry, the cloud provider is the data processor and its policies govern — so "check which platform this tool runs on" is a legitimate training point, not trivia.
Concept 3: when and how to escalate
Every user should know three escalation triggers cold:
- Output that could cause harm if acted on or shipped — a policy violation, dangerous instructions, defamatory or discriminatory content.
- A suspected data exposure — they sent something they should not have, or the model's output appears to contain someone else's confidential data.
- Behavior that looks manipulated — the tool suddenly ignoring instructions or behaving as if it received directions from the content it was processing (prompt injection, explained in plain terms).
The escalation mechanism must be cheap and blame-free: a named channel, a short form, a response-time expectation. If reporting a mistake is embarrassing or bureaucratic, mistakes will simply go unreported, and you lose the early-warning system that training exists to create.
Making it stick
One annual slide deck changes nothing. What works, as recommended practice: a short onboarding module with concrete examples from your own tools; role-specific supplements for teams with special exposure (support, HR, finance, legal); refreshers when something material changes — a new tool, a model migration, a policy update, or a real internal incident (anonymized incidents are the best training material you will ever get). Keep completion records; they are exactly the kind of documentation examiners ask about.
Where to go next
This article covers the baseline for all staff. For the tiered structure above it — power users and practitioners — see building a tiered AI literacy program. For the policy layer, see departmental usage policies and data privacy basics.