5  Whose data is it?

A support engineer removes names from a ticket export and prepares to upload it to an AI service. The file still contains exact contract values, unusual product combinations, locations, and free-text descriptions. Someone says the data is anonymous. What evidence would justify that description?

The first task is to trace the records and their possible uses. The presence or absence of a name is only one part of that assessment.

5.1 Names are not the whole identity

An email address may identify someone directly. Other fields can identify them in combination or when linked to information held elsewhere. A small employer, distinctive role, and a particular incident might narrow the possibilities more than a surname would.

Terminology varies across jurisdictions. Under Australian privacy law, de-identification asks whether the information is no longer about an identifiable or reasonably identifiable individual. Removing direct identifiers may be one step; context and additional controls can matter. It is not accurate to define de-identified data as necessarily re-identifiable, or anonymous data as impossible to identify “by anyone, ever.” OAIC: De-identification and the Privacy Act

Also distinguish a company from a person. A contract record about a large company is not automatically personal information, but a sole trader, named contact, or free-text account history may make the record personal. Commercial confidentiality can apply even where personal-information law does not. Your organisation’s data rules may therefore be stricter than a narrow identifier check.

The practical question is who could identify someone, using what other information, with what effort, in the proposed release context. Write that context down. A restricted internal analysis and a public download are different releases.

5.2 Follow the whole workflow

Draw the route from collection to deletion. Useful places to inspect include the source database, prompt, retrieval index, output, application log, backup, and any connected service. These are possible surfaces, not proof that every request reaches all of them.

A summarisation request does not automatically train a model. Some services may retain inputs or allow eligible content to be used for training; others make different commitments. Model memorisation is a separate mechanism: research has demonstrated extraction of some memorised training text, but that is not equivalent to every prompt becoming retrievable training data. Carlini et al., 2021

For each arrow in the diagram, ask what data crosses it, who can access it, what authorises the use, how long it remains, and how removal would work. Include failure cases: a debug log enabled during an incident, a copied screenshot, or an administrator using a different support tool.

5.3 Legal scope: a review task, not an AI verdict

This chapter provides orientation, not legal advice. Determine which laws, contractual duties, sector rules, and organisational policies apply to the actual activity. Tessera’s fictional location and architecture are teaching context, not a legal assessment.

For entities covered by Australia’s Privacy Act, relevant questions include collection under APP 3, use/disclosure under APP 6, cross-border handling under APP 8, and security/retention under APP 11. These provisions have qualifications and exceptions; they are not four unconditional slogans. OAIC’s AI guidance treats inputs and outputs containing personal information as part of the assessment and recommends due diligence before adoption. OAIC: AI product guidance

For example, APP 6 includes circumstances permitting secondary use or disclosure. “A support record can never be used for another purpose” is too absolute. Equally, possessing the record does not authorise every new use. Establish the basis before repurposing it. OAIC: APP 6 guidance

The EU GDPR may apply outside the EU in specified circumstances; having an overseas customer alone does not settle its scope. Where it applies, assess lawful basis, rights, privacy by design, security, transfers, and whether an impact assessment is required. Have a qualified reviewer check the relevant provisions rather than treating “GDPR compliant” as a product feature. European Commission: who the data protection law applies to

The decision to use real records stays with the accountable people. An AI-generated checklist can help organise questions; it cannot provide the missing permission.

5.4 Minimise before transforming

Start with the purpose. If the task is to practise classifying ticket themes, invented tickets may be sufficient. If the task is to count issue types, aggregated counts may be sufficient. If the task genuinely requires individual histories, identify the minimum fields and access needed.

Transformations have costs. Removing a region may reduce a linkage risk while also hiding an uneven service outcome. Binning a contract value may preserve broad analysis but lose a detail required for an authorised billing task. Document both the privacy effect and the analytical loss.

Pseudonyms need separate handling of the lookup table. Free-text fields need their own review; a structured dataset can look clean while a note contains a name or a rare event. Retention is a design decision too: decide what must be kept for review and what can be removed, subject to applicable obligations.

5.5 What a group-size check can establish

For chosen quasi-identifiers, group identical combinations and find the smallest non-empty group. That minimum is the dataset’s k-anonymity with respect to those chosen fields. A group of one is a warning that the combination is unique within this release.

The choice of fields matters. Grouping by region and industry says nothing about uniqueness after adding an exact contract value. Changing a field outside the grouping cannot change the group’s size. Nor does a minimum group size prove that sensitive attributes cannot be inferred, or that linkage is impossible. Treat the calculation as one diagnostic, not an anonymity certificate.

In the four-row exercise below, suppressing Industry increases the minimum group size from one to two when Region alone is considered. Keeping exact Contract value makes each row unique again if that field is included. The calculation is visible; the release decision still needs its context.

5.6 A review you can defend

Imagine an assistant says, “Names are removed, so upload is safe.” A useful correction does not ask it to sound more cautious. It supplies the missing work: the field inventory, possible linkage information, destination, permissions, and proposed controls.

The resulting decision might be: “Do not upload these records yet. Use lab-v1’s invented material for the prototype, and request a review of the actual flow.” That is an actionable result. It preserves progress without treating an unverified label as permission.

Carry that data-boundary sheet into What are you giving up? Deployment candidates can now be compared against the same constraints.

5.7 Independent practice: a small linkage audit

Time: 20 minutes. Input: these four invented customer records. The row labels are for this exercise only.

Row	Region	Industry	Contract value
A	South	Education	41,200
B	South	Education	58,900
C	Metro	Education	47,500
D	Metro	Retail	62,300

1. Group by Region and Industry. Count the records in each non-empty group: 2, 1, and 1. The smallest group has one member.
2. Suppress Industry and calculate the group sizes using Region alone: 2 and 2. Explain which business question becomes harder to answer.
3. Now retain exact Contract value as another potential matching field. What happens to uniqueness in this tiny table?
4. Draw the proposed flow from source to prompt, output, log, and backup. Mark what is known, what is assumed, and who must approve the next step.

Artefact: a data-boundary sheet in the dossier. Success: show the counts and explain why removing names, or achieving groups of two on selected fields, does not prove anonymity. Actual linkage risk depends on other available information and the release context. Do not attempt to identify real people. This is a reasoning exercise, not a legal compliance determination.