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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsEvery connector, plugin, retrieval index, and callable tool expands an AI system’s authority. To extend a system safely, define the task and data boundary first, give each extension only the functions and downstream permissions it needs, enforce authorization in code and connected services rather than in prompts, and secure every stage of the retrieval and action pipeline. Treat the model as an untrusted decision-maker: it may select an unsafe tool, misunderstand a result, or follow instructions hidden in data.
What changes when you add an AI extension?
An extension is not just a convenience feature. A calendar connector, database tool, file search index, browser function, code runner, or third-party API gives the model a route to information or an action that it did not previously have. The model can now choose when to call that route and can pass arguments derived from user messages, retrieved documents, or tool output.
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That creates two separate security questions:
- What can the extension reach? This covers records, files, systems, network destinations, and data returned to the model.
- What can the extension do? Reading, sending, editing, deleting, publishing, purchasing, or changing permissions have different consequences.
OWASP’s 2025 entry on excessive agency describes the usual root causes as excessive functionality, excessive permissions, and excessive autonomy. A secure design reduces all three. A model should not receive a broad tool merely because a narrow function would be harder to implement, and a successful tool call should not automatically authorize a consequential action.
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Before selecting a connector or model, write down the exact outcome the user needs. Then define the data and actions required to produce it. This turns “connect the AI to our systems” into a testable boundary.
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Record the task
- The user-visible outcome, such as answering questions about a customer’s own contracts or drafting—but not sending—a support reply.
- The identities involved: end user, service account, tenant, team, and any delegated role.
- The data classes that may be read, transformed, retained, or returned.
- The actions that are allowed, prohibited, or require a separate approval.
- The systems and destinations that the extension may contact.
Use your organization’s existing classification scheme for personal, confidential, regulated, or trade-secret data. The important point is to name the boundary before implementation, not to rely on a model instruction such as “do not reveal private information.”
Define the authority of each request
Access should be evaluated for the requesting user and task at the moment of use. A service account with access to an entire document repository is not an acceptable substitute for checking which documents that user may see. Where possible, pass a tenant, user, document-set, and operation scope to the enforcement point and reject requests that lack one.
Inventory every integration before enabling it
Create an inventory entry for each plugin, tool, connector, model provider, embedding service, vector store, and downstream API. NCSC guidance for secure AI system development applies to systems built with other providers’ tools and services, and NIST’s generative-AI risk material highlights privacy, intellectual-property, and security risks from third-party integrations.
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For every entry, document:
- Provider, product, version, hosting region, and contract owner.
- Functions exposed to the model, including hidden or inherited functions.
- Credentials, scopes, network routes, and reachable systems.
- Data sent to the provider and data returned from it.
- Whether the integration can write, delete, transmit, execute, or change permissions.
- Logging, retention, training-use terms, incident notification, and deletion procedures.
- Who approves changes and who responds when the provider, model, or connector changes.
NIST’s draft AI 600-1 material mentions due diligence, service-level agreements, software bills of materials, and SSAE reports as possible inputs to third-party risk management. They are evidence to evaluate, not universal requirements; the appropriate evidence depends on the sensitivity and jurisdiction of your system.
Choose an architecture by control, not by marketing label
“Private,” “enterprise,” or “on-premises” does not by itself prove that a design prevents leakage. Compare candidate architectures on the data that crosses boundaries, the precision of permissions, the controls available at each pipeline stage, provider assurance, and the operational work your team must own.
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| Design option | Data exposure question | Permission granularity | Pipeline control | Operational burden |
|---|---|---|---|---|
| Self-managed model and retrieval stack | Which data leaves your controlled environment, if any? | Potentially fine-grained if identity and filtering are implemented correctly | Highest ability to inspect ingestion, retrieval, generation, and action paths | You maintain infrastructure, patching, models, indexes, and incident response |
| Managed model with a private integration layer | What prompts, documents, telemetry, and tool results cross the provider boundary? | Depends on your gateway and the provider’s tenant and role controls | Strong control in your gateway; less visibility inside the provider service | Shared responsibility; contracts and provider evidence matter |
| Direct third-party plugin or connector | Which data is sent directly to the plugin and its subprocessors? | Often limited to the plugin’s available scopes; verify rather than assume | May be difficult to enforce or observe outside your application | Lower integration effort but greater dependency on provider behavior and change notices |
The safest choice is the one that can enforce the required user, task, document, and action boundaries while producing enough logs to investigate failures. A design that exposes less data but cannot apply per-user filters may be less suitable than one with a larger provider boundary and strong, verifiable controls.
Use least privilege at two levels
Limit the extension itself
Expose only the functions needed for the task. If the task is document question-answering, do not provide send-mail, edit, delete, shell, database-write, or open-ended URL-fetch functions. Prefer separate, typed operations such as search_customer_documents and create_draft_reply over a generic “run API request” tool.
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Keep arguments narrow. Accept an approved document collection identifier instead of an arbitrary path; accept a bounded date range instead of unrestricted query text; accept a destination selected from an allowlist instead of any URL. Validate types, length, encoding, and allowed values before a call reaches the downstream service.
Limit the downstream credential
The connector’s credential should have only the permissions required for its narrow functions. Read-only document access should not include deletion or sharing. A reporting integration should not be able to alter source records. Separate credentials by tenant, environment, and action where practical, and rotate or revoke them through normal identity controls.
Least privilege must survive failure. A malformed argument, prompt injection, stale permission, or model error should produce a denial, not an implicit fallback to an administrator token or a broader search path.
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Keep authorization outside the model
Prompts can explain policy, but they are not an enforcement boundary. OWASP’s excessive-agency guidance warns that prompt injection and model error can trigger inappropriate actions. The application or connected service must independently authenticate the caller, authorize the requested object and operation, validate arguments, and record the decision.
A safe tool-call path
- Authenticate the request. Establish the human or service identity and tenant before the model receives data.
- Constrain available tools. Select the smallest tool set for the task rather than exposing every registered function.
- Validate the proposed call. Check schema, object ownership, tenant, destination, size, and operation against policy.
- Authorize at the resource. Ask the source system or policy service whether this identity may perform this exact action on this exact object.
- Execute with a scoped credential. Never substitute a broad administrative credential because the model requested one.
- Filter the result. Remove fields the user or task is not allowed to see before returning tool output to the model.
- Require confirmation where impact is high. Sending external messages, deleting data, changing permissions, making financial commitments, or publishing content should normally require an independent approval step.
Confirmation should show the actual target, parameters, and material consequences. “Proceed?” is not meaningful if the user cannot see which records will be changed or where information will be sent.
Secure RAG as an end-to-end pipeline
Retrieval-augmented generation does not remove security risk. The OWASP RAG Security Cheat Sheet states: “RAG does not reduce risk — it redistributes it across the data pipeline, creating new attack surfaces at every stage from ingestion to generation to output.” Protect each stage instead of treating the vector database as a trusted box.
1. Ingestion
Authenticate the source, verify provenance, scan files, and apply tenant and document-level access labels before chunking. Do not ingest documents merely because they are reachable by a crawler. Record who supplied each item, when it was approved, and when its permissions expire. Remove revoked or superseded content promptly.
2. Embedding creation
Decide whether content is sent to an external embedding service. Minimize fields before embedding, and avoid placing secrets in metadata that may be copied into logs or diagnostic traces. Keep the mapping between a chunk and its authorization attributes intact after transformation.
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3. Vector storage
Protect the vector store with normal identity, network, encryption, backup, and deletion controls. Apply tenant isolation and document-level filters at query time. Do not assume that an embedding is harmless because it is not readable prose; it remains derived information about sensitive source material.
4. Retrieval
Authorize the search before returning candidates. Filter by user, tenant, purpose, document status, and any row- or field-level policy. Limit result count and similarity thresholds to the task. A failed or denied filtered query must not fall back to an unfiltered index.
5. Context construction
Mark retrieved text as data, not instructions. Preserve source identifiers and access labels, and keep system instructions separate from document content. Truncate or reject oversized context rather than silently dropping the controls or provenance that explain why a passage was retrieved.
6. Generation and output validation
Check that the response does not disclose fields outside the user’s scope, invent approval, or reproduce secrets unnecessarily. For structured output, validate the schema and allowed values before any downstream action. Treat citations or source identifiers as evidence to verify, not as permission to reveal the entire source document.
7. Downstream agent use
If generated text becomes a tool argument, apply the same validation and authorization as for a human-entered value. A malicious instruction hidden in a document should not be able to turn retrieval into an email, deletion, export, or external request.
The OWASP guidance also cautions against caching highly sensitive data. Cache only what is necessary, bind cached results to the proper identity and policy context, and define expiration and purge behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review providers and contractual boundaries
Ask each provider precise questions rather than accepting a general privacy statement:
- Which prompts, files, embeddings, metadata, and tool results are retained, and for how long?
- Are customer inputs used to train or improve a shared model? What controls and exceptions apply?
- Where are data and backups processed, and which subprocessors can access them?
- How are tenant isolation, administrator access, encryption, deletion, and incident notification handled?
- Can you obtain audit logs, a current software bill of materials, independent assurance reports, and change notices?
- What happens when a model, connector, hosting region, or subprocessor changes?
Record answers in the integration inventory and set a review owner. NCSC’s secure-development guidance is relevant even when your team is assembling a system from external services; responsibility does not disappear at the API boundary.
Test the real boundary before deployment
Security tests should exercise the paths users and attackers can actually take, not only the happy path.
- Log in as one tenant and attempt to retrieve another tenant’s documents through normal search, crafted filters, and ambiguous identifiers.
- Ask the model to use a function that is not needed for the task, and verify that the function is unavailable or denied.
- Place an instruction-like string in a test document and check that it cannot authorize a tool call or override policy.
- Submit malformed, oversized, or out-of-range tool arguments and confirm that validation fails before downstream execution.
- Revoke a document or user permission, then verify that retrieval, caches, and generated context stop exposing it.
- Force a timeout, empty result, or permission error and check that the system does not fall back to a broader data source or privileged credential.
- Attempt an external send, deletion, or permission change and verify that an independent approval is required.
Monitor tool-selection rates, denied calls, authorization failures, unusual retrieval volumes, cross-tenant attempts, export destinations, and changes to connector scopes. Keep enough request, policy, tool, and result metadata to reconstruct an incident without storing unnecessary sensitive content.
Build change management into the lifecycle
Reassess the boundary whenever a permission, connector, model, provider, embedding method, index, or source repository changes. A model update can alter tool-selection behavior; a connector update can add functions; a new data source can carry different retention or legal obligations.
NIST SP 800-218A, published in July 2024, adds AI-specific practices to the Secure Software Development Framework for system producers and acquirers. Use it as a lifecycle reference: define security requirements, evaluate suppliers, verify releases, manage vulnerabilities, and preserve evidence that controls were reviewed. NIST’s COSAiS control-overlay work is implementation-focused project material rather than a final standard, so treat it as a developing reference, not a mandatory baseline.
Maintain a release gate
- Update the integration inventory and data-flow diagram.
- Recalculate the minimum functions and credential scopes.
- Repeat cross-tenant, prompt-injection, denial, and high-impact-action tests.
- Review provider notices, retention terms, subprocessors, and assurance evidence.
- Approve the change with a named owner, rollback plan, and monitoring updates.
A practical launch checklist
- The task, user identity, data classes, destinations, and permitted actions are written down.
- Every extension and provider has an owner, version, credential scope, and data-flow record.
- Only task-specific functions are exposed; broad shell, URL, write, delete, and send capabilities are disabled unless essential.
- Authorization and argument validation run in application or downstream controls, not only in prompts.
- High-impact actions display concrete targets and require independent approval.
- RAG controls cover ingestion, embeddings, vector storage, retrieval, context, generation, output validation, and agent calls.
- Revocation, deletion, cache expiry, and failed-retrieval behavior have been tested.
- Provider retention, training use, subprocessors, region, incident terms, and assurance evidence are documented.
- Logs support investigation while avoiding unnecessary storage of sensitive prompts and documents.
- Permission, model, connector, provider, and data-source changes trigger a new security review.
What “without exposing sensitive data” really means
No extension architecture can promise that a model will never encounter sensitive information if the task genuinely requires it. The achievable goal is narrower and more useful: disclose only the minimum data to the minimum component for the minimum time, prevent unauthorized retrieval and action even when the model is wrong or manipulated, and make every boundary observable and revocable. That is how an AI system gains useful capabilities without turning each new integration into an uncontrolled path to private data.
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