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Connect enterprise AI to trusted systems with either an identity-aware live connector or a permission-filtered retrieval-augmented generation (RAG) pipeline. Authenticate each request as the end user when the source supports delegated access. If a service identity is unavoidable, restrict it with explicit tenant, dataset, table, document, and row filters. Apply authorization before retrieval and again before answer generation or tool execution; never rely on a prompt instruction to keep data private. Preserve source links and evidence with every answer so users can verify what the assistant used.
This design lets an assistant work with SharePoint, SQL, BigQuery, Salesforce, and internal files while respecting changing permissions. The difficult part is not connecting an API; it is carrying identity, permissions, tenant boundaries, and audit records through every stage of the request.
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What is the safest way to connect enterprise AI to company data?
The safest pattern is identity-aware retrieval: the assistant receives a user-authenticated request, the connector or retrieval service checks that identity against the source system, and only then fetches data for the model. Use a live, federated connector when the source must remain authoritative and current. Use indexed RAG when semantic search or lower latency is more important, but copy access-control metadata into the index and filter on it before any vector or keyword search.
Authorization needs at least two enforcement points:
- Before retrieval: constrain the query to records, chunks, rows, or assets the requester may read.
- Before generation or action: recheck the returned evidence and any requested tool operation against current policy.
Keep the identity, connector, query, document or row identifiers, and decision outcome in an audit record. Return citations or source links with the answer. This makes an AI response inspectable instead of an untraceable summary.
How do live or federated enterprise AI connectors work?
A federated connector does not build a separate copy of the source for every question. It authenticates the request, sends a query to the underlying system in real time, and returns only what that system authorizes.
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Use OAuth 2.1, IAM, Entra ID, or an equivalent token-exchange mechanism to carry the end user’s identity. Google describes Gemini Enterprise federated Data Cloud connectors as authenticating with the user’s IAM permissions or OAuth 2.1 and executing queries directly against the underlying data source. Microsoft describes federated Copilot connectors as using Model Context Protocol (MCP), fetching data in real time, retaining it in the original system, and accessing it with the user’s identity and permissions.
In this model, a permission change in SharePoint, a revoked database role, or a removed Salesforce group can take effect at the source without waiting for an index refresh. The source remains the system of record for authorization.
What federated connectors are good at
- Operational data that changes frequently, such as tickets, inventory, account status, or case records.
- Sources with mature, row-, record-, or document-level authorization.
- Workflows that need a defensible audit trail of the user and query that produced an answer.
- Answers that must cite the current source rather than a snapshot.
What to check before enabling one
Connector reach can be broader than expected. Google warns that Data Cloud connectors are not automatically limited to one dataset or table; they can reach every resource in the connected source that the authenticated user may query. Scope the connector and IAM roles deliberately. Resource-level controls may require independent grants on both the application and the data store, while broad project-level roles can override restrictions that appear narrower in the connector configuration.
Also decide whether the connector may write. Read-only access is easier to reason about. If the assistant can create, update, delete, send, or approve anything, require a separate authorization check, a narrowly defined tool scope, and an explicit confirmation step for consequential actions.
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Indexed RAG ingests content, divides it into chunks, creates embeddings, and stores those chunks in a searchable index or vector store. At question time, the system retrieves relevant chunks and gives them to the model. AWS describes Bedrock Knowledge Bases as a managed way to connect foundation models to organizational data through vector stores, including synchronization from S3. Microsoft Copilot Studio documents indexed sources such as SharePoint, OneDrive, Graph connectors, Dataverse, and Azure AI Search.
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Advantages of an index
- Semantic search: embeddings can find passages that use different wording from the question.
- Predictable latency: a local or managed index can avoid querying several production systems for every turn.
- Source normalization: content from files, wikis, and databases can be searched through one retrieval layer.
- Controlled model context: chunking and ranking can limit how much text reaches the model.
The security condition for indexed RAG
Permissions must be copied into index metadata and used in the retrieval query itself. Store fields such as tenant ID, document ID, ACL group IDs, sensitivity label, owner, and retention state alongside each chunk. At query time, resolve the requester’s groups and tenant, then apply those constraints before vector similarity or keyword ranking.
Filtering after an unrestricted search is unsafe. A forbidden chunk may already have influenced ranking, caching, telemetry, or a downstream reranker. Microsoft notes that Azure AI Search connections in Copilot Studio are not delegated; the application therefore has to supply its own authentication and filtering model.
Where indexed RAG is a poor fit
Copilot Studio states that its RAG approach is best for factual questions, policy and FAQ summaries, and specific facts. It is not intended for full-document comparison or complex reasoning over long, unstructured documents. For those tasks, retrieve narrowly selected sections, use a controlled document-processing workflow, or query the authoritative system directly rather than assuming a general-purpose index will preserve every relationship and exception.
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What is the difference between live connectors and permission-filtered RAG?
Neither approach is universally superior. Choose according to freshness, source authorization, search needs, and the consequences of a stale or incorrect answer.
| Decision factor | Live or federated connector | Indexed, permission-filtered RAG |
|---|---|---|
| Freshness | Queries the source in real time; reflects source changes when the request runs. | Depends on ingestion and synchronization; ACL or content changes can be stale until indexed. |
| Authorization | Usually delegated to the user’s IAM or OAuth permissions. | Application must resolve identity and enforce ACL, group, tenant, and sensitivity filters. |
| Granularity | Can follow source controls at tenant, dataset, table, record, document, or row level when the connector exposes them. | Granularity depends on metadata attached to every chunk or row and the filter expression used. |
| Source breadth | Best where a source offers a reliable query or connector interface. | Can combine files, databases, and SaaS exports in one retrieval layer. |
| Write capability | May support source actions, but each write needs a separate least-privilege tool policy. | Usually read-oriented; writes require an explicit application workflow and source API authorization. |
| Latency | Depends on source availability, query complexity, and network hops. | Often more predictable after indexing, but ingestion and synchronization add delay. |
| Operational complexity | Lower indexing burden, higher dependency on source connectors, tokens, and query limits. | Requires ingestion, chunking, embedding, metadata maintenance, deletion handling, and ACL synchronization. |
| Auditability | Can log the delegated identity and source query directly. | Must log identity, filter, index version, retrieved chunks, and source revision. |
| Citations | Can link to the live record or document returned by the source. | Should retain canonical source URLs and chunk-to-document mappings. |
| Residency and retention | Data can remain in the original system, subject to connector and transient-processing behavior. | Copies exist in embeddings, indexes, caches, and backups; apply residency, deletion, and retention controls to each. |
There is no comparable cross-vendor statistic in the cited documentation for accuracy, breach reduction, latency, or return on investment. Treat performance and security as properties to measure in your own workload, not as a universal vendor number.
How do I pass the user’s identity through retrieval?
Start with a request context that cannot be replaced by text supplied in the prompt. The context should contain a verified subject identifier, tenant, groups or roles, authentication strength, and an authorization token or reference that the retrieval service can validate.
- Authenticate at the entry point. Validate the user’s session or access token, issuer, audience, expiry, and tenant. Do not accept a user ID typed into the question as proof of identity.
- Exchange or delegate the token. Use the source’s supported OAuth, IAM, Entra ID, or equivalent flow. Keep refresh tokens and client secrets in a secrets manager, never in prompts, logs, or index metadata.
- Resolve effective permissions. Obtain current groups, roles, resource grants, and revocations. Cache only for a period justified by the risk of stale access.
- Construct a constrained query. Bind tenant, dataset, table, document, row, and sensitivity predicates as parameters. Do not concatenate them into free-form SQL or a search string.
- Return an authorization decision with the evidence. The retrieval result should include the source identifier, revision or timestamp, and the policy decision that allowed it.
- Recheck before action. If the model asks a tool to modify data or send a message, authorize the operation against the current user and resource, not merely against the earlier read.
A service identity can be used when a source cannot delegate user credentials, but it must be narrowly scoped. Add explicit tenant and record filters, map the end user to an application-side policy, and make the service account unable to read data outside that policy. A broad service account plus a prompt saying “do not reveal confidential data” is not access control.
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How should permissions be enforced in a vector database?
Put authorization metadata in the same retrieval operation as similarity search. Supabase demonstrates this pattern with Postgres row-level security: document permissions are stored in Postgres, and vector similarity search returns only rows the requester may read. The principle applies to other vector databases.
Illustrative query shape
SELECT chunk_id, document_id, content, source_url
FROM document_chunks
WHERE tenant_id = :tenant_id
AND sensitivity_level <= :maximum_sensitivity
AND acl_group_id = ANY(:requester_group_ids)
AND is_deleted = false
ORDER BY embedding <=> :query_embedding
LIMIT :k;The exact operator and policy syntax vary by database. The security properties do not: tenant and ACL predicates must be enforced by a trusted query layer or database policy, and the caller must not be able to remove them by changing the natural-language question.
Metadata that commonly belongs on each chunk
- Canonical document or record ID and source URL.
- Tenant or business-unit ID.
- Allowed user, group, role, or entitlement IDs.
- Sensitivity, legal-hold, and retention labels.
- Source revision, ingestion time, and deletion state.
- Owner and downstream action scope, if tools can write.
When group membership changes, update or invalidate affected index entries. When a document is deleted or access is revoked, remove it from retrieval and from derivative stores such as caches, replicas, and backups according to the retention policy.
How do I prevent prompt injection from bypassing permissions?
Prompt injection is a content problem; authorization is a control-plane problem. Treat every retrieved document, web page, ticket, or comment as untrusted data, even when it comes from an internal system.
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- Keep system policy and tool authorization outside retrieved text.
- Label retrieved content as evidence, not as instructions.
- Allow tools only through typed schemas with server-side validation.
- Require the tool server to reauthorize the user, tenant, resource, and operation.
- Reject attempts by documents to reveal secrets, change policies, disable logging, or call unrelated tools.
- Limit the model to the minimum evidence and tool scope needed for the question.
A malicious document should at most influence the model’s proposed answer. It must not be able to expand the retrieval filter, impersonate a user, or grant itself write access.
What implementation sequence should an enterprise follow?
Use this sequence for a new connector or RAG system. Assign an owner to every data source and record each decision in the architecture and threat model.
- Inventory the estate. List sources, data owners, identity providers, tenants, sensitivity labels, retention rules, query APIs, and write operations.
- Classify each source. Mark whether it is authoritative, how quickly it changes, whether it supports delegated identity, and what granularity its permissions provide.
- Choose live or indexed access. Favor federation for operational truth and source-native authorization. Favor RAG for broad semantic search after confirming that ACL metadata can be synchronized reliably.
- Define the identity contract. Specify token issuer, audience, tenant claim, group resolution, token lifetime, revocation behavior, and the identity presented to every source.
- Design least-privilege scopes. Separate read and write credentials. Limit service identities by tenant, dataset, table, document collection, row policy, and tool operation.
- Build the authorization filter. Make tenant, ACL, sensitivity, deletion, and retention conditions mandatory parameters. Fail closed when identity or policy data is missing.
- Secure ingestion and storage. Validate source ownership, malware and file types, metadata integrity, encryption, key rotation, redaction, backups, and deletion propagation.
- Implement retrieval and generation guardrails. Cap result count and context size, isolate untrusted instructions, verify citations, and block unsupported claims when evidence is absent.
- Log the decision path. Record user identity, tenant, connector or index version, query or tool name, policy result, source IDs, and response correlation ID. Protect logs because they can contain sensitive metadata.
- Test adversarially before launch. Include denied access, group changes, revoked users, cross-tenant questions, stale ACLs, poisoned documents, malformed tokens, deleted records, and unauthorized write attempts.
AWS Prescriptive Guidance recommends multiple security layers, including metadata filtering, access controls, and data redaction at different stages of the RAG pipeline. Apply controls at ingestion, storage, retrieval, and inference rather than expecting one filter to compensate for gaps elsewhere.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should answers expose their evidence?
Return a concise answer plus citations that identify the source document, record, or query result. Preserve the canonical URL or record key, source revision or timestamp, and the passage or fields used. If the system cannot provide evidence, say that the answer is based on no accessible source rather than filling the gap from model memory.
Citations also help security reviews. An auditor can compare the user’s effective permissions with the returned source IDs and determine whether a forbidden item entered the context. Do not expose a citation to a document the requester cannot open; provide a safe description or omit it while retaining the identifier in protected audit logs.
What are the most common failure modes?
A broad service account reads everything
Replace it with delegated access or enforce tenant and record-level policy in a trusted service. Audit the service account’s grants and remove unused roles.
The application filters after vector search
Move ACL predicates into the database or retrieval query so unauthorized chunks are never candidates for ranking, caching, or generation.
Permissions change but the index does not
Implement event-driven ACL updates where possible, short synchronization windows where necessary, and a deny-first check against the source for high-risk data.
A connector is scoped in the UI but broad IAM roles override it
Review effective permissions at the project, tenant, dataset, table, and resource levels. Test with a user who has the intended minimum role, not only with an administrator.
The model performs a write after a read
Separate read and write tools, require a fresh authorization decision, validate the target resource and parameters server-side, and request confirmation for irreversible actions.
Best Value
Users receive confident answers without traceable sources
Make citations and source revisions part of the response contract. Monitor citation coverage and route unsupported questions to a safe refusal or human review.
How can I decide between architectures for a specific source?
Ask these questions in order:
- Is the source the operational system of record, and can its permissions be queried at the required granularity? If yes, start with a live connector.
- Does the source support delegated user identity? If yes, prefer it over a shared credential.
- Do users need semantic search across many files or systems, and can ACL and deletion events be synchronized? If yes, consider indexed RAG.
- Would stale permissions create material harm? If yes, require a live authorization check or deny access when the index is stale.
- Does the assistant need to write? If yes, design a separate action plane with its own scopes, confirmation, and audit trail.
- Must data remain in a particular region or retention boundary? If yes, include embeddings, caches, replicas, logs, and backups in the residency review.
The resulting design is often hybrid: live federation for sensitive operational records, permission-filtered RAG for approved document collections, and a common identity, policy, citation, and audit layer across both.
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Can an enterprise AI assistant use a shared service account safely?
Only with strict compensating controls. The account needs least-privilege scopes and mandatory tenant, resource, and row filters enforced outside the model; high-risk sources should use delegated user credentials instead.
How quickly must access revocations reach an AI index?
There is no universal interval. Set the maximum tolerated exposure for each data class, then combine short synchronization windows with a live deny check for sensitive or recently changed permissions.
Should embeddings be treated as confidential data?
Yes. Embeddings, chunk stores, caches, and backups are derivatives of the source and can reveal sensitive information through retrieval or metadata. Apply encryption, access control, retention, deletion, and residency policies to them.
What should happen when the AI cannot verify a user’s permissions?
Fail closed for protected sources: do not retrieve or generate from the uncertain data. Return a clear access or verification message and log the denied decision without exposing the hidden content.
How do I audit an answer months later?
Store a protected correlation record containing the authenticated subject, tenant, connector or index version, policy inputs and result, source identifiers and revisions, tools invoked, and response timestamp. Keep the record under the same retention and privacy controls as other security logs.
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