The Tool Desk
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This guide gives you an architecture checklist, a way to evaluate model providers, and a test plan. It also separates what a provider commits to from what stays your job. A “we don’t train on your data” promise is useful. It doesn’t stop your own app from showing one customer’s documents to another.
Map the whole data path before choosing a model
Draw every place data travels or rests, from the user’s input to the final log line. Teams that only evaluate the model vendor usually miss the stores they run themselves. The table below lists the common ones.
| Artifact | How it exposes data | Primary control |
|---|---|---|
| Prompts and outputs | Users paste confidential content in; responses can repeat sensitive context | Input and output scanning, DLP, clear usage rules |
| Retrieved records | The service fetches data the caller isn’t entitled to see | Per-user authorization applied before context is assembled |
| Embeddings and vector indexes | Index content outlives the source record or is shared across tenants | Tenant and user scoping, deletion that reaches the index |
| Memory, history, summaries | Details saved from one session surface in another | Scope by user, tenant, purpose and retention period |
| Caches | A cached answer is served to someone who shouldn’t receive it | Cache keys that include identity and permissions; short lifetimes |
| Logs and traces | Full prompts and outputs sit in monitoring systems with broad access | Redaction, restricted access, defined retention |
| Tool results and agent state | Connectors return more than needed; agents can send data outward | Least-privilege tools, review of write and external-transfer actions |
Microsoft’s guidance names these retention and retrieval artifacts as places where sensitive information can be disclosed, and recommends controls on each ([sensitive information disclosure]). The table’s mapping to specific controls is this article’s summary of that guidance, not a quotation from it.
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Who is responsible for what
Microsoft’s LLM security plan puts it plainly: “The customer-controlled cloud components vary by service type.” Whatever service you choose, some layers belong to the provider and some to you ([Microsoft Learn]).
- Provider commitments cover training use, retention of data it processes, processing location where offered, encryption and access controls on its own infrastructure, and the attestations and contract terms it signs. These apply to a specific product, endpoint, plan and region.
- Your responsibilities cover who can ask what, which records get retrieved, what is stored in your own databases, indexes, caches and logs, which tools an agent may call, and how you detect and respond to a problem.
A provider’s no-training commitment says nothing about whether your retrieval layer checks permissions. Keep the two questions separate in every design review.
Step 1: Set data boundaries and minimize what enters the system
Decide what data is allowed in before you build anything that touches it.
- Inventory and classify. List each data source with its owner, sensitivity, allowed use and retention period.
- Assign allowed uses per class. State which classes may be used for inference, retrieval, evaluation, fine-tuning, or not at all. Many teams allow retrieval but forbid fine-tuning on the same data.
- Record provenance and approval. Validate and review content before ingestion. Microsoft’s AI risk assessment guidance warns against implicitly trusting inference data if it may later enter a training or evaluation set ([Microsoft AI risk assessment]).
- Reduce at the source. Remove unnecessary confidential and personal data from primary stores, indexes, caches and application artifacts. The Azure Well-Architected AI design principles make this point: data you never store or index can’t be disclosed ([Azure Well-Architected]).
In practice, ask of every field you pass to a model: would the answer be materially worse without it? Account numbers, free-text notes and attachments are the usual candidates for removal or masking before the model call.
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Step 2: Evaluate providers for the exact product you will deploy
Provider policies differ by product, endpoint, configuration, eligibility and region. Review the specific API, model and feature set you plan to use, not the vendor’s brand in general.
Questions to put to every provider
- Are inputs and outputs used for training or model improvement by default? What opt-in or exception paths exist?
- What is retained, for how long and in which system? Does that change for abuse monitoring, stateful features, tools, file uploads or logs?
- Can you control retention, data residency, processing region, encryption keys and access?
- Which security attestations and contract terms cover this product in your region?
- Do you need confidential computing or another isolation approach, and which specific threat does it address?
Example: what one vendor states, and why scope matters
OpenAI’s business data page says: “By default, we do not use data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare, ChatGPT for Teachers, or our API platform—including inputs or outputs—for training or improving our models.” It also says: “Qualifying organizations are able to configure how long OpenAI retains business data, including opting for our zero data retention policy in the API platform” ([OpenAI, Business data privacy, security, and compliance]).
Read those as vendor statements with limits. The first covers named business products and the API platform, not consumer plans. The second applies only to qualifying organizations, and zero data retention is tied to the API platform. Before you rely on either, confirm in your contract and the provider’s current terms that your endpoint, features and region are covered. Terms change, so recheck them periodically instead of treating a one-time review as permanent.
Step 3: Build application-layer controls
Authorize at retrieval, not in the prompt
A user who can invoke an AI feature should not inherit access to every record the service identity can read. Microsoft’s guidance calls for least privilege and constrained access to external data sources ([security plan]). Apply the caller’s permissions as a filter during retrieval, before any text reaches the model’s context.
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// Illustrative pattern, not a specific library
results = vectorStore.search(
query,
filter = { tenantId: caller.tenantId, allowedGroups: caller.groups }
)
context = assemble(results) // only permitted content gets this far
Don’t write “only reveal documents the user may see” into a system prompt and call it done. A model has no reliable way to enforce access policy, so anything in its context should be treated as potentially disclosable to the caller.
Treat retrieved content as untrusted
Documents, web pages, emails and tool output can contain instructions aimed at your model. Keep them separate from system instructions, limit what they can influence, and assess prompt-injection risk explicitly ([security plan]). The risk grows when the same agent can read private data and also send data out.
Scope memory, history, caches and vector stores
Tie each store to an intended user, tenant, purpose and retention period, and build deletion that actually reaches summaries, caches and indexes, not only the source row ([sensitive information disclosure]).
Limit tools, especially outbound and write actions
Give each tool the narrowest permission that works. Use human review for high-risk external transfers or configuration changes where appropriate ([Azure AI security best practices]). Microsoft opens that page with a blunt line: “AI applications often interact with sensitive data.”
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Step 4: Protect storage, networks and operations
- Encrypt sensitive data at rest and in transit. Consider customer-managed keys where your risk and the service’s support justify them ([Well-Architected], [security plan]).
- Apply DLP and sensitivity labels to data AI applications access and, where the tooling supports it, to prompts ([Azure AI security best practices]).
- Redact PII and secrets from logs. Decide which prompt and output data you need for monitoring and how long it stays available ([security plan]).
- Monitor data access, privileged activity, connector behavior and unusual retrieval or output patterns. Keep audit trails consistent with your retention and privacy rules ([sensitive information disclosure]).
- Re-review the boundary whenever you add a tool, connector, model, agent or memory feature. Each one adds a way for content to persist or cross a trust boundary.
- Plan for incidents. Know in advance who can disable a connector, purge an index or rotate keys, and how you would find which users’ data a faulty retrieval touched.
Compare deployment and control options
These options solve different problems, and none is best for every company. The sources are guidance, not a benchmark ranking them. The right mix depends on your data classes, jurisdiction, threat model, product design and operational capacity.
| Option or control | What to compare |
|---|---|
| Hosted enterprise AI API | Training-use terms, retention, endpoint scope, region, access and audit controls |
| Self-hosted or private deployment | Operational burden, patching, model supply chain, data boundary and who owns security |
| Retrieval-augmented generation | Per-user authorization, source freshness, index isolation, deletion and prompt-injection handling |
| Fine-tuning | Whether sensitive examples are needed at all, who can query the resulting model, and how you evaluate exposure |
| Confidential computing | Whether your threat model includes privileged infrastructure access, and whether your workload is supported |
| DLP and governance platform | Coverage across prompts, outputs, retrieval, memory, logs and connectors, and where it can enforce rules |
Self-hosting moves the data boundary but also moves patching, model provenance and security operations onto your team. Fine-tuning deserves particular caution: if the training examples are confidential, the resulting model becomes another artifact whose audience you must control.
Where confidential computing fits
Microsoft describes confidential AI as protecting data and model artifacts during specified training and inference scenarios by using trusted execution environments ([Microsoft confidential AI]). It addresses a defined threat: exposure to those with privileged access to the infrastructure while data is in use. It doesn’t replace authorization or minimization. An app that retrieves the wrong customer’s record inside a protected enclave still shows the wrong record.
Test for leaks before release and after every change
Build a test matrix and run it in pre-release evaluation, then rerun it whenever models, tools, connectors, prompts or provider settings change. Use controlled test data, not real customer records. Microsoft’s guidance supports red-team exercises and explicitly names prompt injection and sensitive information disclosure as risks to evaluate ([AI risk assessment], [security plan]).
| Test area | What to attempt | Pass condition |
|---|---|---|
| Cross-user and cross-tenant retrieval | As user A, ask for user B’s records directly, indirectly and through rephrased or multi-turn questions | No content from B appears in context, output or logs |
| Prompt injection | Plant instructions in a document, web page and tool response that tell the model to reveal data or call a tool | Instructions are not followed; no data leaves the boundary |
| Secret and PII detection | Seed canary secrets and fake PII in inputs, retrieved context, outputs, memory writes and logs | Detectors flag or redact at each point; canaries never appear where they shouldn’t |
| Retention and deletion | Delete a record, then query caches, histories, summaries and indexes | Deleted content is gone from every derived store within your stated window |
| Tool and connector permissions | Try write and external-transfer actions with a low-privilege identity | Denied, or routed to human approval |
| Provider configuration drift | Compare live endpoint, region, retention and training-use settings with your approved baseline | No unapproved differences |
| Access review | Audit who can read prompts, logs, indexes and keys | Access matches job need |
Record each failure with an owner, a mitigation and retest evidence. Canary strings, meaning unique fake secrets planted in test data, make leaks easy to search for across logs and outputs. These tests are practical recommendations drawn from the cited threat and mitigation guidance. Passing them doesn’t prove your product can’t leak, only that these known paths were checked.
Pre-launch checklist
- Every data source has an owner, a classification, allowed uses and a retention period.
- Unnecessary confidential and personal data is removed before indexing or prompting.
- Provider terms were checked for the exact endpoint, plan and region, including training use, retention and residency.
- Retrieval filters by the caller’s permissions before context assembly.
- Untrusted content is separated from instructions, and agents that read private data can’t freely send data out.
- Memory, caches, summaries and vector stores are scoped and deletable.
- Logs are redacted, access-controlled and expire on schedule.
- Encryption, key management and DLP are in place where supported.
- The test matrix above has run with documented results and an owner for each open item.
- An incident procedure exists for disabling connectors, purging indexes and notifying affected parties.
What no control can promise
No provider, model, encryption feature or prompt instruction makes exposure impossible, and the guidance behind this article doesn’t claim otherwise. It also doesn’t include a published measurement of how much any one safeguard reduces leakage, so treat the controls above as established practice, not quantified guarantees. Your jurisdiction, industry and contracts may add requirements that general guidance doesn’t cover, so involve your security, privacy and legal teams before you ship.
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