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What can a delivery file reveal about your business?

AuteurBastien CabirouCo-Founder & CEO
Date8 novembre 2026

Removing names does not settle whether a file belongs in an AI prompt. Routes, timings et trip counts can still reveal how a business operates. Start with the person who understands that information.

Glenn's example: the value is in the pattern

In our conversation, Glenn Tan, CEO et co-founder of Zavior, describes a hypothetical last-mile delivery business. Its valuable information could include how quickly drivers move between places et how many trips they complete in a period.

The sensitive detail may be the operating pattern: how quickly goods move et how much work the team completes. The person running that operation can explain why the pattern matters.

A list of names, passwords or payment-card numbers will not answer that question on its own. The operations owner may recognise a commercially useful combination that is hard to identify from individual field names.

Glenn TanPDG et cofondateur de Zavior

Glenn Tan s’appuie sur une entreprise fictive de livraison du dernier kilomètre pour expliquer la protection des informations propres à une entreprise.
Lire la transcription en anglais

Extrait de l’entretien original : De 52:20.80 à 53:09.76 dans l’entretien original.

So beyond the standard guardrails that the existing modern LLMs have, we need to have business guardrails as well. What business guardrails means is that if I were to sell a service for last mile delivery, where is my main IP at? My main IP is actually possibly in the space of how fast my drivers move from one place to another and how many trips did they do in one time, right? In one phase or one term. So that information is now contextually my IP, right? In a way, if I want to put some guardrails, if I run a business like that, I will put guardrails around that kind of data not being shared.

Start with the employee's task

Take a fictional employee who wants help rewriting a customer update about a delivery delay. The source spreadsheet contains route IDs, exact driver timings, trip counts et internal margin notes.

For this exercise, the operations owner has approved a short service notice. The detailed operational file is not approved for this task. The employee can work from the notice without passing the spreadsheet to the assistant.

Fictional input-selection exercise

Task: Rewrite an approved customer notice in clear language.

Permitted input: Some deliveries may arrive one working day later than usual. We will send an updated delivery window.

Leave out:

  • Route IDs et exact driver timings
  • Trip counts et internal margin notes
  • The original operational spreadsheet

Fictional example. The operations owner has approved this notice for this task only.

Ask the data owner to make the same distinction for one real recurring task: what does the employee need, et what does the source reveal that the task does not require?

Turn the decision into a check

Record the approved service, account, device et input path beside the permitted information. Security or IT can then check the configured policy on that exact route using synthetic content.

Compare a permitted notice with a separate fictional fixture containing the information the rule is meant to protect. Write down the expected response before testing, then record what actually happened. Check whether the remaining input still supports the employee's task.

If the expected control cannot be demonstrated, keep that question open et give it an owner. A successful check on one paste or upload path does not establish coverage for another.

Make the handoff clear

The business data owner decides which information is appropriate for the task. Security defines the required protection; IT verifies the supported configuration. The employee needs a short instruction et a place to ask about exceptions.

If an MSP or MSSP supports the rollout, identify the client's decision owner et the provider's technical support owner before the first pilot. A restriction should lead to a clear next step, not an unresolved handoff.

Provider training et retention terms are a separate part of the review. A no-training commitment does not decide which information the organisation permits the employee to send.

Define the next check

Use the sensitive-file scenario in Aona's policy-control guide to record the rule, test scope, expected response et next owner. Start with synthetic information.

Use the policy-control worksheet

Separate AI training terms from data-sharing decisions

Evaluate AI data protection with Aona

Évaluez vos besoins en sécurité IA des employés

Discutez de la couverture de découverte et des résultats de politique que vous devez tester pour les applications et chemins d'entrée pris en charge sur les clients installés. Dans un cas anonyme dans le secteur de la santé australien, les requêtes soumises en dehors de l'outil approuvé sont passées de 446 à 32 en 30 jours, soit une réduction de 92,8 %.

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Rapport SOC 2 Type II. Essai guidé gratuit de 30 jours après confirmation des exigences de déploiement.

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À propos de l'auteur

Bastien Cabirou avatar

Bastien Cabirou

Co-Founder & CEO

Bastien Cabirou is the Co-founder & CEO of Aona AI, where he leads the company's mission to help enterprises govern AI adoption securely and at scale. With deep expertise in AI security and enterprise risk management, he is a recognised voice on Shadow AI, AI governance frameworks, and the evolving regulatory landscape.

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