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Running a retrieval-augmented generation (RAG) system or large language model (LLM) on premises does not, by itself, keep its data private. Prompts, documents, embeddings, retrieved passages, logs, caches, backups, and tool calls can each cross a security boundary. A private design defines those boundaries, enforces access before data reaches the model, and tracks information through its full lifecycle.
What an on-premises privacy boundary must cover
“On premises” should describe a specific, documented boundary, not serve as a general privacy assurance. Map where each kind of data is processed and stored, including any exceptions for updates, telemetry, support, or external services. An on-premises model can still expose information through an overly permissive retrieval path, a shared index, a log, or an outbound connection.
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Start with an inventory of source systems, data owners, sensitivity classes, users, tenants, model endpoints, vector stores, caches, logs, backups, and downstream services. Decide which data classes may enter the RAG corpus, who may approve that use, and what handling rules apply. AWS guidance recommends classifying data at ingestion and maintaining a data catalog; those are useful practices independently of whether an organization uses AWS services.
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- Trace source documents and user prompts through extraction, chunking, embedding, storage, retrieval, generation, and any tool or application that receives the answer.
- For each step, record the system, identity, data types, access rules, retention period, and network connections involved.
- Document which operations are local and which depend on an external service, including inference, embedding generation, telemetry, updates, and support.
- Identify owners for the corpus, model deployment, keys, network controls, logs, and privacy-risk decisions.
Protect each stage of the RAG and LLM lifecycle
RAG does not remove risk; it distributes it across a pipeline. OWASP’s RAG Security Cheat Sheet describes attack surfaces from ingestion through generation and output. A useful design review follows the data itself rather than stopping at the model host.
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| Stage | Data or exposure to consider | Privacy and security controls |
|---|---|---|
| Ingestion | Source files, extracted text, connector credentials, and changes to approved content | Approve connectors and ingestion identities; record provenance; validate integrity; scan for malicious content and adversarial instructions; classify and, where justified, redact sensitive information. |
| Chunking and indexing | Derived text, embeddings, metadata, and cross-user or cross-tenant access | Carry access metadata to each chunk or enforce equivalent isolation at the index boundary; authenticate storage consumers; restrict ingestion identities. |
| Retrieval | Queries and passages selected for the caller | Apply the caller’s current permissions before retrieved text enters model context; use default-deny behavior and verify tenant separation. |
| Generation and output | Prompts, retrieved passages, generated responses, and possible malicious instructions | Constrain context; separate trusted instructions from retrieved data; validate output before another system uses it. |
| Tools and downstream systems | Model-generated arguments, actions, and data sent to other applications | Authorize each action independently; limit tools to what the task needs; validate arguments and use safe, parameterized interfaces. |
| Operations and retention | Conversation history, response caches, audit records, backups, and derived data | Set retention and access rules; propagate deletion and permission changes; monitor the pipeline without broadly exposing sensitive content in logs. |
Make authorization part of retrieval
The application and retrieval path—not the model—must decide whether a caller may see a document. Each chunk needs the source’s relevant classification, owner, tenant, and permitted-role information, or equivalent isolation must be enforced at the index boundary. At query time, the application should filter candidate results using the caller’s current permissions before any passage is added to the model’s context.
This check must reflect permission changes made after ingestion. If access was revoked in the source system, stale metadata must not leave the chunk available to the former user. OWASP recommends chunk-level access metadata, retrieval-time enforcement, tenant isolation, and cascading deletion. AWS describes metadata filtering in a managed architecture and notes that the application or agent must supply the right metadata on each call; a filter is not protective if the application constructs it incorrectly.
Verify the authorization path
- Test that a user with no permission gets no restricted passages in the retrieval results or model context.
- Test each role and tenant boundary, including mixed-permission queries and attempts to change tenant or role identifiers.
- Check that missing, malformed, or unavailable authorization metadata fails closed rather than returning unfiltered results.
- Confirm that retrieval uses current permissions and that access changes reach derived chunks and indexes.
- Record which authorized sources were retrieved and who made the query, while protecting those records from unnecessary access.
Secure ingestion and preserve provenance
Ingestion is a trust boundary: a file that enters the corpus can influence future answers, even if its instructions are malicious. Approve source connectors and their identities, and keep a record of each item’s source, owner, upload time, approval, and transformations. Validate content against an approved baseline and review baseline changes separately from routine writes.
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OWASP cautions that a matching digest shows consistency with an approved baseline; it does not establish that the content is safe or free of prompt injection. Integrity checks should therefore sit alongside content scanning and review, not replace them. Classify documents before indexing and redact sensitive information when the data policy calls for it. AWS describes scanning and PII detection or redaction options in its managed design; the equivalent controls in an on-premises environment depend on the organization’s tools and operating model.
Protect indexes, keys, networks, and identities
Vector databases and caches can contain sensitive derived data, and their location does not make them inherently safe. Authenticate every consumer, grant application and ingestion identities only the access they need, and separate responsibilities for model deployment, corpus changes, key administration, and audit review. OWASP’s LLM Verification Standard 2.0 calls for authenticated storage, least privilege, and segregation of long-term user data.
Specify how keys are held and rotated, how backups are encrypted, which internal network segments can communicate, what outbound traffic is allowed, and how physical access is controlled. AWS guidance for its managed reference architecture includes customer-managed keys for stored data, TLS 1.2 or higher for transit, protected secrets, and private connectivity where supported. These are examples from AWS guidance, not a universal description of every on-premises system or a substitute for defining the organization’s own topology.
Treat documents, prompts, and outputs as untrusted
A passage can contain instructions intended to manipulate the model, even when it came from a normally trusted source. Validate documents before indexing, keep retrieved content clearly separate from system instructions, and limit the context supplied to the model to what the caller is authorized to see and the task requires. Retrieved text should be handled as data, not as a command to the model or application.
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Build prompts on the server side, and use prompt or completion guards where they are appropriate to the threat model. Validate generated content for its expected shape and allowed use. If a response reaches another system, treat it as untrusted input: do not concatenate it into SQL or shell commands. Use parameterized, validated interfaces, restrict available tools to the minimum needed, and check tool arguments before execution. A model’s answer is not authorization to perform an action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Control retention, deletion, logs, and incident evidence
Set distinct retention rules for source documents, extracted text, chunks, embeddings, indexes, conversations, response caches, and logs. A deletion request or permission revocation should trigger the corresponding removal or invalidation in derived stores, not just deletion of the original file. OWASP recommends cascading deletion and audits for orphaned chunks.
Monitor access, retrieval, ingestion, configuration changes, and unusual model interactions. Keep enough evidence to investigate incidents, but do not make full sensitive prompts, secrets, or responses broadly available in routine logs by default. OWASP calls for observability across the pipeline and cautions against exposing sensitive prompts or diagnostics through logs. Protect audit records with access controls and retention limits of their own.
Use governance to assess privacy risk
Assign accountable owners and document intended use, affected people, data flows, threat scenarios, safeguards, and residual risks. The NIST AI Risk Management Framework (AI RMF) is voluntary and is intended to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. NIST released its Generative AI Profile on July 26, 2024, and says AI RMF 1.0 is under revision.
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There is also narrower NIST guidance for identity systems: SP 800-63-4 says organizations using AI/ML systems, or relying on services that use them, shall perform and document privacy risk assessments for personal information processed. That identity-specific guidance should not be treated as a universal legal requirement for every RAG deployment. Applicable legal duties depend on the jurisdiction and the organization’s use case.
Compare designs against the same decision criteria
When evaluating architecture options, compare the data paths and control behavior rather than relying on labels such as “local” or “private.” For each design, establish where prompts, source data, embeddings, and telemetry are processed; whether permissions are enforced before retrieval reaches the model; and how isolation, deletion, audit, and operations work.
- Processing location: Where are source files, prompts, embeddings, and telemetry handled? Which services or support paths leave the defined boundary?
- Authorization: Are permissions applied before retrieved passages enter context, and do checks fail closed?
- Isolation: How are users, roles, and tenants separated in indexes, caches, and logs?
- Keys and network: Who controls encryption keys, what is encrypted in transit and at rest, and what outbound connections are allowed?
- Retention and deletion: Do source changes propagate to chunks, embeddings, caches, and backups under documented policies?
- Auditability: Can investigators understand access and retrieval events without routine over-collection of sensitive content?
- Operational resilience: Who maintains the stack, reviews access, handles failures, and responds to incidents?
- Workload fit: Does the design meet the chosen model’s needs for throughput, latency, and concurrency?
Size local compute for the workload, not the label
Local inference is a viable architecture path, including on a GPU workstation, but “on premises” does not imply a particular GPU, memory capacity, or server configuration. Requirements depend on the model and the intended workload, including latency, throughput, concurrency, and deployment constraints. Establish those requirements before selecting hardware; there is no single minimum configuration established for all RAG and LLM projects.
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