Level 1 · Guide 05
Check Korean workplace data before using Claude
Whether workplace data may be used depends on its category, purpose and legal basis, account terms, company policy, and contracts. Minimization or pseudonymization is not permission; confirm international processing, subprocessors, retention, deletion, and the approved environment, and exclude sensitive health data from this exercise.
Product features may change. Check the last verified date and official sources.
Core concepts to know first
Focus on the decisions these terms support in real work rather than memorizing them.
Core concepts
1. Upload capability is not permission
Technical upload capability is not organizational permission. Determine whether the task needs real data at all, classify every field, confirm the processing basis and contractual conditions, and prefer a synthetic substitute. In this guide, the first observable move is to write the purpose and inventory every proposed data field.
2. Ask whether real data is necessary
Confirm policy, legal basis, service terms, retention, and approvals. Preserve the approved input and the evidence behind the result so a reviewer can distinguish what the source says from what Claude inferred. This directly controls the risk that pseudonymized data can still be personal data.
3. Use a practical four-tier classification
Replace real records with a minimal synthetic dataset for practice. Record the decision and the remaining uncertainty instead of hiding it in polished prose. The review must explicitly test whether consumer and commercial account terms may differ.
4. Distinguish consumer and commercial terms
Treat the possibility that outputs and connected tools can expose data beyond the original input as a required test case. The intended result is a purpose, field, environment, and approval decision sheet, not an unreviewed answer that merely looks complete.
5. Review inputs, outputs, and connected access
For Check Korean workplace data before using Claude, the final concept joins the earlier checks into an operating boundary: use approved inputs, expose evidence and uncertainty, and stop before a consequential action. Ownership and a reproducible review determine whether a purpose, field, environment, and approval decision sheet may move beyond training.
Synthetic work scenario
How this applies at work
This scenario was written for learning and is not a real customer case.
A fictional HR team wants to summarize survey comments. It removes the sensitive-data fields from scope, creates synthetic comments, and asks privacy and security owners to confirm purpose, legal basis, service terms, retention, and the approved account before any real use.
The scenario is newly written for this guide and is neither a customer case nor a performance claim. Its specific deliverable is a purpose, field, environment, and approval decision sheet. A reviewer can reproduce the work from the synthetic inputs without access to customer, employee, health, contract, or confidential company data.
Try it yourself
Choose one small task and follow the steps. Confirm organizational policy and data boundaries before using sensitive materials.
Step 1. Write the purpose and inventory every proposed data field
Write the purpose and inventory every proposed data field. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Step 2. Confirm policy, legal basis, service terms, retention, and approvals
Confirm policy, legal basis, service terms, retention, and approvals. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Step 3. Replace real records with a minimal synthetic dataset for practice
Replace real records with a minimal synthetic dataset for practice. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Completion checklist
Check only what you verified yourself. Every item must be checked before saving completion.
Some items are still unchecked. Review the result again.
What could go wrong?
Plausible language does not guarantee accuracy. Compare the result with originals, calculations, permissions, and current information.
- Pseudonymized data can still be personal data
- Consumer and commercial account terms may differ
- Outputs and connected tools can expose data beyond the original input
Boundaries that require human review
Academy practice stops at draft, preview, or approval pending. Actions with real impact require separate owner approval outside Academy.
What AI can do
- Step 1. Write the purpose and inventory every proposed data field
- Step 2. Confirm policy, legal basis, service terms, retention, and approvals
- Step 3. Replace real records with a minimal synthetic dataset for practice
What a person must approve
- Applicable law, contract, organizational security policy, and explicit approval boundaries take priority. Stop and ask the responsible owner when they conflict.
- Academy exercises never send, publish, purchase, delete, execute contracts, or change permissions. A responsible person performs any real action through a separate process.
This guide is educational and does not replace legal, security, or privacy judgment for your organization.
Questions about this guide
- When is this guide complete?
- It is complete when the stated outcome is ready and another reviewer can retrace the inputs, evidence, boundaries, and decision. The target outcome is “A purpose, field, environment, and approval decision sheet.”
- May I practice with real company data?
- No. Use synthetic material in the Academy. Real data requires a separate review of policy, legal basis, contracts, minimization, retention, deletion, and the approved environment.
- What if the product screen differs from this guide?
- Product behavior can change. Check the verification date and official sources, then retest the current account and plan with a small, low-risk example.
Official sources and further reading
Recheck the current product and policy status in these primary sources.
Authorship and review
Save progress
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Academy does not collect or store work materials, prompts, or outputs. It runs no separate analytics scripts beyond basic server access logs.