Work Output: Access, privacy and audit
Objective. Read the access, privacy and audit view to understand that what a user sees is shaped by their role and scope, and that audit shows who saw or changed what - not whether a figure is correct.
This screen answers 'who can see which output data, and is use of it accountable?' For output data that touches individuals and clients, access scope and auditability are not an afterthought - they shape both trust and what any given figure even represents to its viewer.
See it in the demo
What this screen answers
CSMs use access, privacy and audit to confirm the right people see the right scope and to support a customer's governance conversation; Sales uses it to answer the 'is this surveillance, and who controls it?' question. The customer question is 'can we trust how this data is accessed and prove who used it?'
The task
Review how roles map to scope - who sees organisation-wide output, who sees only their team - and confirm it matches the customer's intended governance. Check that the audit trail records access and changes. For a CSM, the task is to align this configuration with the customer's privacy commitments and to be able to show, during a review, that a disputed figure's history and access are accountable. Treat employee-level output data with the sensitivity it warrants.
How to read it
Access scope changes what a figure means to its viewer: the same metric seen at team scope and at organisation scope is computed over different populations, so a number is only interpretable with the viewer's scope in mind. The classic misread is comparing two users' figures without realising they are scoped differently. Audit shows who accessed or changed what and when - it proves accountability, not correctness, so an audited figure is not thereby a validated one. Read access as context for every other screen, not as a standalone report.
Honest limits
This screen does not prove the customer's specific access model is configured, that privacy commitments are met, or that any figure is accurate. It shows the model on seeded data. Confirm with product how roles, scope and audit are configured and governed for the customer, and treat privacy and access design as a specialist setup topic to escalate rather than commit to in a demo.
Interpretation check
Access scope changes what a figure means to its viewer, so a metric is only interpretable with scope in mind; audit proves who saw or changed what, not that a figure is correct, so an audited number is not a validated one.
Practical task
Review how one role's scope shapes what it sees, and write a one-line finding noting one way a scoped difference could be misread as a data error, plus what you would escalate for the customer's governance setup.
Self-check
Two users quote different output figures for 'the team'. Before assuming an error, what do you check?
Their access scope - the same metric at team and organisation scope is computed over different populations. The difference may be scope, not a data error.
A figure has a full audit trail. Does that make it correct?
No - audit proves who accessed or changed it and when, which is accountability, not accuracy. Correctness still depends on definitions and source coverage.
Why treat access and privacy design as an escalation topic?
It is specialist setup tied to the customer's governance and privacy commitments - something to confirm with product and configure deliberately, not to commit to in a demo.
Verified vs demo. Screen is live in the demo at https://work-output.demo.timechamp.io/#/access; exact controls, labels and on-screen figures are SME-validation-pending.