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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsTo use support conversations for AI evaluation, first decide what behavior you need to assess, then keep only the conversation details required to judge it. Remove or mask unnecessary identifiers, preserve continuity only when needed, and review the entire transformed conversation for details that could still identify a customer. Removing names alone does not establish that a conversation is anonymous.
Start with the evaluation question
Define what the evaluation is meant to measure before editing the data. For example, are you checking whether the AI follows a policy, escalates an issue appropriately, or retrieves the correct detail? Keep the minimum conversation context needed to score that behavior. The UK Information Commissioner’s Office (ICO) discusses data minimisation, masking and anonymisation in its AI and data protection guidance.
Review the full conversation, not just the customer’s first message. A detail that seems unnecessary in isolation may explain a later turn; conversely, a long history may include personal information unrelated to the evaluation. Remove irrelevant turns and details where doing so will not undermine the evaluation question.
Choose redaction, pseudonymisation or anonymisation
| Approach | What it does | Privacy and legal framing | When it may fit |
|---|---|---|---|
| Redaction or masking | Removes or obscures selected information in a working copy. | Does not by itself prove the remaining conversation is anonymous. | Direct details are unnecessary for the evaluation. |
| Pseudonymisation or tokenisation | Replaces identifiers while retaining additional information or a link separately. | Under the ICO’s UK GDPR guidance, pseudonymised personal data remains within data-protection law. | Cross-turn or cross-example continuity is needed. |
| Anonymisation | Seeks to reduce identification risk sufficiently that people are no longer identifiable by means reasonably likely to be used. | Requires assessment of residual and linkage risk; removing direct identifiers alone is not enough. | The evaluation can proceed without retaining a route back to identity. |
These descriptions reflect ICO guidance and are not a legal determination for every jurisdiction. The ICO quotes Article 4(5) UK GDPR as defining pseudonymisation as processing personal data so it cannot be attributed to a specific person without additional information, provided that information is kept separately and protected by technical and organisational measures. The ICO’s guidance states: “Pseudonymised personal data is in scope of data protection law.” See the ICO’s pseudonymisation guidance.
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Transform unnecessary or identifying details
Remove or mask direct identifiers and any details that do not serve the evaluation purpose. Practical examples include:
- Replace a customer’s name with
[CUSTOMER]when the name is irrelevant. - Replace an account number with
[ACCOUNT_ID]. If turn-to-turn continuity is necessary, use an appropriately designed stable token instead. - Generalise a highly specific place, date or rare event when that precision is not needed to assess the AI.
- Remove passwords, authentication codes, payment credentials and private contact details from evaluation copies.
These are illustrative applications of minimisation and masking guidance, not a universal list of fields to remove. If a token allows records to be linked, treat that capability as a privacy risk rather than assuming the text is anonymous.
Rank #2
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Check the whole conversation for residual identification risk
Free text can identify someone indirectly. A combination of location, dates, unusual circumstances, job details or a distinctive incident may point to a person even after names and account fields are removed. Review the transformed conversation together with any context that will accompany it, and consider information available elsewhere that could be linked to it.
The ICO’s anonymisation guidance says to assess whether identification is possible by means reasonably likely to be used, including through a “motivated intruder” test. It covers free text and was published on 28 March 2025; the ICO says the guidance is under review following changes made by the Data (Use and Access) Act. Check the current wording of the ICO’s anonymisation guidance when applying it.
Rank #3
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Protect any mapping and review the process
If you retain a separate mapping between tokens and customer identities, keep it apart from the evaluation dataset and restrict access. Consider whether the token or transformation could be reversed through brute-force, dictionary or guesswork attacks, and use technical and organisational safeguards appropriate to the risk. The ICO recommends choosing pseudonymisation techniques in light of purpose, risk, safeguards, utility and accuracy, and testing their effectiveness.
Repeat the risk assessment when the evaluation purpose, dataset, audience, linkage possibilities or release context changes. A dataset that is suitable for a restricted internal evaluation may carry different risks if shared more widely. NIST’s September 2023 SP 800-188, De-Identifying Government Datasets: Techniques and Governance, offers technical background on balancing disclosure risk with analytical utility; it concerns government datasets and is not a universal legal rule for private support teams.
Rank #4
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Keep the legal scope clear
The cited ICO materials explain UK data-protection guidance; they do not decide an organisation’s lawful basis, customer notice, contract terms or obligations under another jurisdiction’s laws. Redaction for an evaluation dataset is a data-handling measure, not by itself a conclusion that the underlying use is lawful or that the result is anonymous.
Quick Recap
Best Value
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