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Neither open-source nor proprietary AI is automatically safer for sensitive data. Choose the deployment that gives you a verifiable answer to five questions: where data is sent, how it is processed, how long it is retained, whether it can be used to improve a model, and who can access the data and logs. Then confirm that your organization can operate the chosen system securely.
A public API usually reduces infrastructure work but sends prompts and other information to a provider. A privately hosted open or open-weight model can keep data inside an environment you control, but your team becomes responsible for configuration, patching, monitoring, model provenance and incident response. Managed hosting sits between those choices: it can reduce infrastructure work while preserving model choice, but it remains a third-party service that requires due diligence.
Start with the data boundary, not the model label
Classify the information your AI system will handle before comparing model families. “Sensitive” can include personal data, health or financial records, confidential business information, source code, legal advice, credentials, customer content and regulated records. The right architecture for anonymized public material may be unacceptable for identifiable records.
Write down what must never leave a controlled boundary. That boundary might be a particular cloud account, a national jurisdiction, an on-premises network or a system with a defined accreditation. Include more than the text typed into a prompt. Retrieved documents, uploaded files, conversation history, telemetry, error traces, evaluation examples, feedback and backups can all contain sensitive information.
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Use these questions from the UK Government Artificial Intelligence Playbook for the UK Government as a practical starting point:
- “where is your data being sent and how is it being processed?”
- “is your data being used to train future models?”
- “how long is your data being retained?”
- “is your data being logged and who has access to those logs and for what purpose?”
If a supplier cannot answer those questions for the exact service, region, edition and account configuration you intend to use, treat the data path as unverified.
What “open-source” and “proprietary” actually describe
Proprietary or closed models
A proprietary model is controlled by an organization that generally does not publish all of its source code, model weights or training-data information. You may access it through an API or a hosted application. The provider operates much of the model infrastructure, which can be an advantage when your team lacks specialist machine-learning operations skills. It does not, by itself, establish that prompts are private, that logs are inaccessible to provider staff or that inputs are excluded from model improvement.
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“Open-source” is often used loosely. A model may publish code, weights, documentation and some information about training data under a license that permits specified uses. Another model may publish only its weights. The latter should be described as open-weight, not assumed to be fully open-source.
Open availability can make inspection and independent testing easier. It can also give attackers the weights or training-data clues needed to tailor attacks. Even an openly released model remains complex and difficult to analyze completely. Review the license, restrictions, provenance, release process, cryptographic integrity and known vulnerabilities rather than treating the label as a security rating.
Compare the three practical deployment patterns
| Deployment | Potential advantage | Questions and trade-offs |
|---|---|---|
| Proprietary model through a public API | Provider operates model infrastructure, scaling and many security functions. | What leaves your environment? Is input or output used for training? What is retained, where, and for how long? Who can access logs? Which filtering, audit, deletion and contractual controls apply? API privacy techniques can reduce exposure but have limitations. |
| Open or open-weight model hosted privately | More direct control over network paths, storage, identity and the data the model can consume; isolation or limited network access may be possible. | Your organization must secure and update the model, operating system, containers, accelerators, storage and network. You must verify model provenance and license terms. Local capability may not match a larger hosted model, and specialist skills may be required. |
| Managed hosting for an open or commercial model | A service provider can supply GPUs, orchestration, patching and scaling without requiring you to build all hosting infrastructure. | Prompts and retrieved data still enter a third-party service boundary. Assess retention, training use, administrator access, region, provenance, service-level commitments and the division of security responsibilities. |
These are deployment choices, not universal rankings. A private model can be badly secured; a hosted API can have strong contractual and technical controls. The decision depends on your data classification, jurisdiction, task, latency, budget, staff capability and risk tolerance.
Map every data flow before selecting a service
Draw a data-flow diagram for the complete request lifecycle. At minimum, identify:
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- prompt text, files, images, audio and structured fields;
- pre-processing such as redaction, tokenization, translation or chunking;
- retrieval stores, embedding services and document indexes;
- the model endpoint and any subprocessors;
- prompt and response logs, traces, analytics and abuse-monitoring systems;
- human review queues, feedback pipelines and evaluation datasets;
- caches, backups, disaster-recovery copies and deletion workflows; and
- outputs sent to downstream systems or shown to users.
For each step, record the owner, geographic location, encryption state, identity used, retention period, deletion method and whether the data can be used for training or service improvement. Include failure paths: an application error may write a full prompt to a debug log, and an evaluation job may copy production conversations into a less-controlled workspace.
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Evaluate privacy and security questions in writing
Location and processing
Ask where prompts, files, embeddings, logs and backups are processed and stored. “The provider uses secure data centers” does not answer whether data crosses a border or enters a subcontractor’s environment. Confirm the region and service tier in the contract and verify that routing, support access and disaster recovery follow the same requirements.
Retention and deletion
Establish whether data is held temporarily for abuse detection, troubleshooting, billing, analytics or human review. Determine the retention period for raw inputs, outputs, metadata and logs separately. Ask how deletion requests propagate to replicas, backups and derived indexes, and what evidence of deletion is available.
Training and model improvement
Obtain an explicit statement about whether your prompts, files, outputs or feedback may be used to train or improve future models. Do not infer the answer from a consumer-facing privacy page: API and enterprise offerings can have different terms. Make the setting contractual or technically enforceable where possible, and check that it applies to every endpoint and subprocessors used by your account.
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Access and logging
Identify provider personnel, support teams, subcontractors, administrators and automated systems that can access content or logs. Require role-based access, strong authentication, just-in-time elevation, immutable audit records and a documented process for investigating access. Decide what your own application logs should capture; mask or omit sensitive prompts and responses unless retaining them is necessary and authorized.
Security controls and assurance
Request current security documentation that matches the service: architecture descriptions, independent assurance reports, vulnerability-management practices, incident-notification terms and relevant certifications. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, July 26, 2024) identifies software bills of materials (SBOMs), service-level agreements (SLAs) and assurance reports such as SSAE reports as examples of procurement and transparency aids.
What private hosting gives you—and what it makes your job
Hosting an open or open-weight model inside infrastructure you control can keep prompts and retrieved documents within a chosen network and region. You can restrict outbound traffic, integrate existing identity controls, choose storage locations and inspect the software stack. Those benefits are real only if the controls are implemented and maintained.
Your team must harden the host, container, orchestration layer, model server, operating system, accelerator drivers, storage, secrets and network. Plan for vulnerability scanning, signed artifacts, patch windows, key rotation, backup protection, capacity management, red-team testing and incident response. Keep a current inventory of model files and dependencies so you know what must be replaced when a vulnerability or license issue is discovered.
A local model can run on one machine for ad-hoc or one-off work where performance is not paramount. That does not make a single workstation a suitable production service. Production use normally needs redundancy, controlled access, monitoring, update procedures, secure disposal and a tested recovery plan. The government playbook notes that specialist machine-learning operations skills may be needed.
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Use least privilege when the model can retrieve private data
Retrieval-augmented generation (RAG) can preserve user-level permissions by retrieving only documents the requesting user is authorized to see and placing those documents in the model context. Enforce authorization before retrieval, not after the model has received the content. Keep service accounts narrowly scoped and prevent the model from querying an unrestricted index.
RAG is not a complete security boundary. Indirect prompt injection in a retrieved document can instruct the model to ignore rules, disclose context or call tools. Retrieval can also reduce answer quality in some applications. Test malicious documents, conflicting permissions, stale indexes and attempts to induce data exfiltration. Log retrieval decisions without exposing the retrieved content unnecessarily.
How to reduce exposure when using a proprietary API
A public API necessarily passes request data to the provider, even when its retention policy differs from a consumer-facing application. Treat the API integration as a control point rather than a transparent pipe.
- Minimize inputs: send only fields required for the task. Remove unused identifiers, metadata and entire documents.
- Tokenize or redact: replace names, account numbers and other sensitive values with reversible tokens held in a separate protected store when the workflow requires re-identification.
- Separate identity from content: use an internal reference instead of a direct identifier, and keep the mapping outside the model service.
- Constrain outputs: validate structured responses, block unsafe tool calls and prevent generated text from becoming an authorization decision without independent checks.
- Protect transport and storage: use authenticated encrypted connections, encrypt local queues and caches, and avoid writing raw prompts to application logs.
- Set provider controls: select the correct region and retention setting, disable training use where offered, restrict support access and configure deletion controls.
- Monitor leakage: scan prompts, responses and logs for secrets or regulated identifiers, with a documented process for handling detections.
Privacy-enhancing techniques at an API boundary can be useful, but the UK government playbook cautions that they have limitations. Do not present redaction or tokenization as proof that the underlying service is safe for every data class.
Managed hosting: less infrastructure, not less due diligence
Managed hosting may be appropriate when you need a chosen model but cannot operate GPU infrastructure. Clarify whether the host supplies the model, merely runs your artifact or can replace it during maintenance. Confirm who patches the base image, model server and accelerator stack, and who investigates a compromise.
Review tenant isolation, administrator access, network controls, region, retention, training use, subprocessors, backup handling, availability targets and incident-notification deadlines. Ensure the service-level agreement covers the capabilities your application actually needs, such as latency, throughput, maintenance windows and recovery objectives. Keep a tested exit plan: export prompts and configuration in a usable format, identify a replacement endpoint and know how to delete copies held by the host.
Compare options on the dimensions that affect your decision
| Decision dimension | Questions to answer | Evidence to collect |
|---|---|---|
| Data flow and geography | What leaves the boundary? Which regions and subprocessors handle it? | Architecture diagram, routing configuration and contract terms. |
| Retention and training | Are prompts, outputs, files, embeddings or feedback retained or used for improvement? | Service-specific terms, settings and deletion procedure. |
| Identity, access and logs | Who can view content or logs, and can your team audit access? | Role model, administrator controls, audit-log samples and support-access policy. |
| Model and software provenance | Who created the artifact? What license, dependencies and release history apply? | License text, hashes or signatures, SBOM and vulnerability notices. |
| Task quality and latency | Does the model meet accuracy, language, context, throughput and response-time needs on your data? | Representative evaluation results, failure analysis and capacity tests. |
| Operating cost | What are compute, storage, network, support, licensing, monitoring and staff costs? | Workload-based forecast, not a headline price. |
| Staff capability | Who patches, tunes, monitors and responds at 02:00? | Named owners, on-call coverage, runbooks and training plan. |
| Testing and monitoring | How will you detect leakage, prompt injection, drift, abuse and outages? | Test plan, metrics, alert thresholds and review cadence. |
| Contract and incident response | Can you audit, obtain evidence, receive timely notification and terminate safely? | SLAs, assurance reports, notification clauses and exit plan. |
Do not rank an option using only model accuracy or only data residency. A slightly weaker model with enforceable controls may be preferable to a stronger model that your organization cannot monitor or constrain.
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A practical selection process
- Classify the data. Separate public, internal, confidential, personal, regulated and mission-critical information. Mark categories that must not leave a controlled environment.
- Define the use case. Record users, tasks, acceptable errors, required latency, context size, languages, tool access and whether outputs influence high-impact decisions.
- Map the lifecycle. Diagram prompts, retrieval, model calls, logs, telemetry, human review, feedback, backups and deletion.
- Set non-negotiable requirements. Specify location, retention, training use, access, encryption, deletion evidence, auditability, availability and incident-notification limits.
- Shortlist deployment patterns. Compare public API, managed hosting and private hosting against both requirements and your team’s operating capacity.
- Verify the artifacts and terms. Check model license, provenance, signatures or hashes, dependencies, SBOM availability, provider terms, subprocessors and responsibility boundaries.
- Build controls before production. Implement minimization, redaction or tokenization, least privilege, network restrictions, secret management, logging policy, output validation and backup protection.
- Evaluate representative workloads. Use realistic data or carefully controlled surrogates. Measure quality, latency, cost, leakage, unauthorized retrieval, prompt injection and failure behavior.
- Document residual risk. Record limitations, affected people, mitigations, owners, acceptance authority, monitoring signals and shutdown criteria.
- Approve and review. Obtain security, privacy, legal and business approval where required. Reassess after model, provider, data, software or regulatory changes.
Test the system as an integrated service
Model benchmarks alone do not establish that your deployment protects sensitive data. Test the complete application, including preprocessing, retrieval, permissions, provider settings, logs and downstream actions.
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- Submit prompts containing realistic secrets and verify that minimization, masking and logging behave as designed.
- Attempt to retrieve another user’s documents through direct requests, ambiguous wording and manipulated metadata.
- Place indirect instructions in documents and test whether the model reveals context or invokes unauthorized tools.
- Inspect provider and application logs to confirm that sensitive values are absent or access-controlled.
- Test deletion, account closure, backup restoration and incident-containment procedures.
- Measure quality and latency under expected load, degraded dependencies and rate limits.
- Repeat tests after model updates, prompt changes, index rebuilds and provider-term changes.
NIST recommends documented, iterative test, evaluation, validation and verification (TEVV). It also notes that current TEVV approaches may be inadequate or mismatched to a particular context, so record what your tests do not establish.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Procurement and governance controls
Make privacy and security requirements part of procurement rather than informal assurances. Ask for the exact service terms that apply to your account and region, a list of subprocessors, retention and deletion commitments, incident-notification obligations, audit rights, availability targets and an exit process.
For private or managed deployments, request an SBOM and artifact provenance information. For any deployment, define who owns prompt templates, retrieval indexes, evaluation data, model updates, vulnerability remediation and user complaints. Require change notification when a provider changes the model, routing, subprocessors, terms or logging behavior.
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Common mistakes and how to correct them
“Open-source means secure by default.”
Open availability may improve inspection, but public weights and training-data clues can help attackers. Correction: verify provenance, license, dependencies, update process and threat model.
“Local hosting guarantees privacy.”
A privately hosted model can leak through misconfigured logs, unrestricted retrieval, compromised hosts, backups or excessive administrator access. Correction: implement least privilege, egress controls, monitoring, patching and tested deletion.
“The API provider does not train on prompts, so the risk is solved.”
Retention, support access, subprocessors, jurisdiction, compromise and output leakage remain relevant. Correction: assess the full lifecycle and obtain service-specific terms.
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“RAG automatically enforces permissions.”
RAG can preserve access checks only when retrieval enforces them correctly. Indirect prompt injection and stale or misindexed documents remain risks. Correction: authorize before retrieval and test adversarial documents.
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“A benchmark proves production suitability.”
Benchmarks rarely test your data path, permissions, logs or failure modes. Correction: run integrated TEVV on representative tasks and threat scenarios.
“The cheapest option is the least risky.”
Infrastructure, security staff, monitoring, downtime, legal review and incident response can dominate model charges. Correction: compare total operating responsibility and cost.
How the choice changes by scenario
Confidential internal knowledge assistant
If documents are confidential but the organization can enforce identity and retrieval permissions, managed hosting or a private deployment may work. The decisive controls are document-level authorization, index isolation, prompt and log minimization, and a test that one employee cannot obtain another employee’s material.
Regulated personal records
Start with the strictest location, retention, access and deletion requirements. A provider API is viable only when its exact terms and controls satisfy those requirements. If they cannot be verified, keep processing inside an approved environment and budget for the additional operational duties.
One-off local analysis
A single-machine model may be reasonable for an ad-hoc task when performance is not paramount and the machine is controlled. Remove temporary files, protect swap and backups, restrict network access and document the model license. Do not silently turn the workstation workflow into a production service.
High-volume customer application
Evaluate latency, availability, scaling, abuse monitoring and incident response alongside privacy. A privately operated system may provide a desired data boundary but require substantial reliability engineering; a managed service may reduce that burden while adding a third-party boundary.
Keep the threat model current
NIST’s AI security guidance identifies confidentiality, integrity and availability risks affecting systems, training and output data, and underlying software and hardware. NIST also notes that existing guidance does not comprehensively cover attacks such as evasion, model extraction, membership inference and availability attacks. Treat controls as evolving, monitor new attack techniques and revisit assumptions after significant model or infrastructure changes.
A useful review asks whether an attacker could extract training or prompt information, manipulate retrieved content, cause unauthorized tool actions, exhaust capacity, tamper with model artifacts or exploit a vulnerable dependency. Assign each risk an owner and a measurable detection or response action.
Final decision checklist
- The data classes and prohibited destinations are documented.
- Every prompt, file, retrieval result, log, backup and feedback path is mapped.
- Location, retention, training use, access and deletion are verified for the exact service or deployment.
- Model license, provenance, artifact integrity and software dependencies are recorded.
- Identity, least privilege, network restrictions, encryption, secret management and logging are implemented.
- RAG authorization is enforced before retrieval, and indirect prompt injection has been tested.
- Representative quality, latency, cost, leakage and failure tests are documented.
- Procurement includes SLAs, assurance evidence, incident terms, audit rights and an exit plan where applicable.
- Named staff own patching, monitoring, updates, user support and incident response.
- Residual risks and acceptance authority are recorded, with a review date.
The defensible choice is the one whose data path, controls and operating responsibilities you can demonstrate—not the one whose label sounds safer. Open or proprietary models can both be appropriate when matched to the data boundary, verified through testing and supported by an organization capable of maintaining the required controls.
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