The Tool Desk
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Build a shortlist by comparing four things together: task performance, data handling, security and privacy evidence, and governance. Then test the complete workflow—not just the model—and record why the residual risk is acceptable.
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What you are actually choosing
An AI model is only one component of a system. Sensitive information may enter through a prompt, a document-retrieval connector, fine-tuning data, evaluation datasets, telemetry, or a downstream application. Outputs may be stored, shown to a reviewer, sent to another service, or used to trigger an action. Each path creates a separate security and compliance question.
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National Cyber Security Centre (NCSC) guidance treats the deployment pattern as a design decision. You may train a new model, use an existing model, fine-tune an existing model, or call an external model through an API. The suitable choice is the one that meets the use case while keeping the trust boundaries and controls you can genuinely operate.
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Answer these questions before comparing models
- What is the task? Document classification, summarization, coding assistance, clinical support and automated decisions have different accuracy and explainability requirements.
- Who will use it? Identify employees, contractors, customers, administrators and automated agents, including privileged users.
- What data is involved? List direct identifiers, special-category personal data, financial records, confidential business information, credentials, system logs and derived attributes.
- Where is the data? Include prompts, uploaded files, retrieval indexes, training and tuning sets, evaluation data, outputs, backups and logs.
- What can go wrong? Consider disclosure, incorrect advice, discrimination, unauthorized action, model extraction, prompt injection, data poisoning and service compromise.
- Which laws and contracts apply? Record the countries of data subjects, processing locations, sector rules, cross-border-transfer requirements and your role as controller, processor or another party.
NCSC recommends documenting a threat model and revisiting it as your understanding of threats changes. A model that is acceptable for an internal low-impact assistant may be unsuitable for a system that influences benefits, employment, credit, insurance or medical care.
Compare deployment patterns, not just model names
| Pattern | Control over data and infrastructure | Typical strengths | Questions and trade-offs |
|---|---|---|---|
| Train a new model | Potentially the greatest control over training data, weights and runtime, if the organization operates the full stack. | Can be designed around a narrowly defined domain and internal handling rules. | Requires substantial expertise, security engineering, compute, maintenance and evidence that the resulting model performs safely. |
| Use an existing model in your environment | More control over network boundaries, identity, storage and logging than an external API, subject to the software and model license. | Faster adoption and the possibility of isolating sensitive traffic from public services. | Your team must patch, monitor and secure the infrastructure. The model’s training-data history and memorization risks still require assessment. |
| Fine-tune an existing model | Training examples and resulting artifacts become additional sensitive assets. | Can improve performance or style for a defined task without building a model from scratch. | Check whether examples are retained, copied to other environments, exposed through outputs or included in later training. Fine-tuning does not automatically remove memorization or bias. |
| Managed environment | Some infrastructure controls are delegated to a provider; the exact boundary depends on the contract and configuration. | May provide private networking, identity integration, regional processing and operational support. | Verify administrator access, subprocessors, retention, deletion, incident response and audit evidence rather than relying on a “private” label. |
| External API | Information crosses your organizational boundary to the provider. | Usually the quickest integration and access to a range of capabilities. | Requires provider security due diligence and outbound-data controls. NCSC specifically says choosing an external model provider includes evaluating that provider’s own security posture. |
Do not assume that self-hosting is automatically safer. An isolated deployment with poor patching, excessive administrator access or weak logging can create more risk than a well-controlled managed service. Conversely, an API can be inappropriate when you cannot prevent identifiers or regulated records from leaving your control.
Define the data boundary precisely
Create a data-flow diagram for each candidate. Mark every point at which information is collected, transformed, transmitted, stored, displayed or deleted. Include connectors that retrieve records at inference time; a model can be sensitive because of the data it can access even if its original training set was benign.
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- Raw prompts and system instructions.
- Files attached by users or automatically retrieved from a repository.
- Names, account numbers, dates, location data, health information and free-text descriptions that can identify a person indirectly.
- Fine-tuning, preference-optimization and evaluation examples.
- Prompt-response pairs written to application, provider or safety logs.
- Outputs copied into case-management systems, email, tickets or analytics platforms.
- Metadata such as user IDs, IP addresses, timestamps and document names.
Data minimization should happen before transmission. Remove fields that are not needed, use role-specific retrieval, redact identifiers where the task permits, and prevent the model from receiving an entire record when a small excerpt is sufficient. Record whether redaction is reversible and who holds the mapping.
Verify provider handling instead of trusting labels
For every external or managed candidate, obtain the current contract, data-processing terms, security documentation and service configuration. “Enterprise,” “private,” “secure” or “anonymous” are descriptions to verify, not compliance conclusions.
Questions procurement should resolve in writing
- Are prompts, retrieved content, outputs or feedback retained, and for how long?
- Are any of those materials used to train or improve a shared model? Is that setting different by plan, region or feature?
- Which provider personnel, support teams or subprocessors can access content, and under what approval and logging controls?
- Where are processing, storage, backups and support operations located? Can a selected region be enforced technically?
- How does deletion work for live systems, caches, indexes, backups and derived artifacts? What evidence is supplied?
- What authentication, least-privilege, network-isolation and customer-key options are available?
- How quickly will the provider notify you of a security incident, and what forensic and audit information will be provided?
- Can you inspect independent assurance reports, penetration-test summaries, vulnerability-management practices and business-continuity plans?
- What happens to your data and access when a subscription ends, a feature changes or a subcontractor is replaced?
Require the provider to identify the exact product and configuration covered by each answer. General corporate privacy statements may not describe API logs, retrieval features, fine-tuning jobs or support access.
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Assess GDPR and privacy risk case by case
Whether a model is subject to data-protection obligations depends on the processing and the roles involved, not on a marketing statement that the model is anonymous. CNIL describes a case-by-case analysis that asks whether people could be identified from model parameters or whether their information could be extracted through queries.
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- What is the lawful basis and stated purpose for each collection and use?
- Is the organization a controller, processor or another party for each processing stage?
- Can a user recover an individual’s personal information by making repeated, specially crafted or contextual queries?
- Do training, tuning or retrieval datasets contain excessive or unlawfully obtained information?
- Are data-subject rights, retention limits, correction requests and deletion requests technically supportable?
- Have you documented a data-protection impact assessment where the processing presents a high risk?
CNIL’s approach makes re-identification testing relevant where the risk warrants it. Test extraction attempts using controlled records and realistic attacker access, then preserve the method, results, mitigations and residual risk. Do not claim that weights are anonymous merely because direct identifiers were removed from a dataset.
Understand differential-privacy claims
Differential privacy is a formal type of guarantee, not a synonym for “private.” Ask which mechanism, privacy budget, neighboring-dataset definition, composition accounting and release process are used, and whether the guarantee applies to the model-training procedure, a query interface or only an aggregate report. NIST SP 800-226 is a guide to evaluating differential-privacy guarantees and common implementation pitfalls. If a supplier cannot explain the guarantee and its limits, treat the claim as unsubstantiated.
Apply sector safeguards to the complete system
Sector rules follow the data and the workflow. For electronic protected health information (ePHI), NIST SP 800-66 Revision 2 is a practical resource for implementing the HIPAA Security Rule. A model that appears accurate in a demonstration can still fail because retrieval, prompts, outputs, user access or logs are not protected.
Healthcare-specific review points
- Define whether the system creates, receives, maintains or transmits ePHI.
- Limit access by role and treatment or business need; do not give every user the same retrieval scope.
- Encrypt data in transit and at rest, manage keys separately where appropriate, and verify backup handling.
- Log access to records, prompts and outputs without placing unnecessary clinical content in broadly visible logs.
- Keep a human accountable for clinical decisions and define escalation when the model is uncertain or wrong.
- Assess vendor agreements, incident procedures, availability requirements and contingency access before production.
The NHS buyer’s guide identifies safety, security and robustness, transparency and explainability, fairness, accountability and governance as themes for buying AI. Those themes are useful beyond healthcare: they force a procurement team to examine the complete service rather than a benchmark score.
Evaluate security controls across the AI system
Use a defense-in-depth review. NCSC recommends sanitization, least privilege and controls on information sent to external APIs. The application around the model is often the easiest route to compromise.
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Identity and access
- Require strong authentication and central identity management.
- Separate administrator, developer, analyst and end-user privileges.
- Use per-application credentials and short-lived tokens rather than shared keys.
- Restrict which users can upload data, change system instructions, add retrieval sources, export outputs or trigger actions.
Prompt and retrieval protection
- Validate file types and scan uploads for malware.
- Apply authorization before retrieval, not after the model has received the document.
- Separate trusted instructions from untrusted retrieved text and test resistance to prompt injection.
- Filter secrets, credentials and unnecessary identifiers before sending context.
- Limit context size and retrieval scope to the minimum needed for the task.
Output and action controls
- Check outputs for personal data leakage, secrets, unsafe instructions and prohibited recommendations.
- Require human approval for irreversible, financial, clinical, employment or access-control actions.
- Use allowlists for tools and destinations; do not let a model freely call arbitrary endpoints.
- Record the model version, policy version, source documents and reviewer for consequential outputs.
Monitoring and response
- Monitor unusual query volume, repeated extraction attempts, permission changes and abnormal retrieval.
- Define how to revoke keys, disable a connector, quarantine a model version and notify affected parties.
- Retain enough evidence to investigate incidents while applying the shortest retention period compatible with legal and operational needs.
- Reassess controls after model, prompt, connector, data-source or jurisdiction changes.
A December 2025 initial preliminary draft of NIST’s Cybersecurity Framework Profile for Artificial Intelligence states that model sensitivity generally follows both the data used to train it and the data available at inference. Because that document is preliminary draft guidance, treat it as a useful security perspective rather than a final requirement. Its practical recommendations include minimizing sensitive prompt data, runtime redaction, output filtering, pattern detection and access control.
Compare model suitability on consistent axes
| Axis | Evidence to collect | Decision question |
|---|---|---|
| Task fit | Representative accuracy, error types, robustness, latency and ability to adapt. | Does it meet the required quality without exposing more data or autonomy than necessary? |
| Data boundary | Processing locations, access paths, retention, deletion, subprocessors and export routes. | Can you enforce the boundary required by your contracts and jurisdictions? |
| Security posture | Identity controls, isolation, patching, logging, incident handling and external-API safeguards. | Can your team and the provider prevent, detect and contain misuse? |
| Privacy evidence | Minimization, re-identification analysis, extraction testing and substantiated privacy-enhancing methods. | Can you explain why personal-data risks are acceptable and how they are reduced? |
| Governance | Interpretability, documentation, versioning, auditability, ownership and review procedures. | Can an accountable person understand, challenge and document a consequential output? |
| Operational fit | Integration effort, reliability, maintenance, staffing and recovery procedures. | Can the organization operate the safeguards for the life of the system? |
Prefer a simpler or more interpretable model when it satisfies the task. More parameters or a higher general benchmark score do not compensate for an unmanageable data boundary or an inability to explain an important result.
Use a controlled test before production
- Write the intended-use statement. Specify users, prohibited uses, decisions affected, data categories, jurisdictions and the human role.
- Build a representative test set. Include normal cases, rare cases, ambiguous inputs, adversarial prompts, privacy-sensitive records and known failure modes. Use synthetic or de-identified data where that preserves the test objective.
- Test the whole path. Exercise ingestion, redaction, retrieval authorization, prompting, generation, output filtering, logging, export and any tool calls.
- Measure errors by consequence. A harmless wording error and an incorrect clinical recommendation should not receive the same score. Define acceptance thresholds and mandatory human review.
- Run privacy and security attacks. Try memorization and extraction queries, prompt injection, unauthorized retrieval, poisoned documents, key misuse and excessive tool permissions.
- Check explanations. Determine whether reviewers can inspect source passages, confidence limitations, model version and the reason an output was accepted or rejected. Do not treat fluent explanations as proof that the answer is correct.
- Record residual risk. Document controls, known limitations, owners, review dates, incident triggers and the decision to proceed, restrict or reject.
- Re-test after change. Repeat the evaluation when the model, provider terms, prompts, retrieval corpus, connector, user group or processing location changes.
Check the regulatory timetable, but do not mistake it for certification
The European Commission’s current guidance on general-purpose AI (GPAI) gives these provider-side milestones:
| Date | Milestone | What it means for a buyer |
|---|---|---|
| 2 August 2025 | GPAI provider obligations began applying. | Review whether the provider has identified and addressed obligations relevant to its role and model. |
| 2 August 2026 | Commission enforcement powers begin. | Supervision timing does not replace your own risk assessment or contractual controls. |
| 2 August 2027 | Providers of models placed on the market before 2 August 2025 must comply. | Legacy-model transition dates are not a product-level compliance certificate. |
These dates concern provider obligations under the GPAI framework. They do not prove that a particular configuration is lawful for your data, sector or jurisdiction. Map the model’s role, your organization’s role and the actual data flow to the rules that apply to your use case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A defensible selection worksheet
For each candidate, create a record with the same fields so that a familiar brand or impressive demonstration cannot bias the decision:
- Intended task, users, prohibited uses and decision impact.
- Data categories, sources, jurisdictions and retention requirements.
- Deployment pattern and a diagram of trust boundaries.
- Model version, adaptation method and training-data documentation available to you.
- Provider answers on training use, access, location, deletion, subprocessors and incidents.
- Authentication, authorization, encryption, redaction, retrieval, output and logging controls.
- Test results for quality, robustness, privacy extraction and security attacks.
- Explainability, auditability, human-review and appeal arrangements.
- Residual risks, compensating controls, accountable owner and next review date.
Score each axis only after evidence is collected. Mark an item “not established” when the provider or internal team cannot substantiate it; do not convert missing evidence into an optimistic assumption. A candidate that performs slightly better but cannot meet deletion, residency or audit requirements should be rejected for that use case.
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Common mistakes and how to correct them
Choosing by benchmark or model size
Problem: A general benchmark says little about your records, languages, error costs or workflow.
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Correction: Test representative tasks and weigh errors by their impact. Include the complete retrieval and review process.
Assuming “not used for training” solves privacy
Problem: A provider may still retain data in logs, expose it to support personnel, route it through subprocessors or return it in an output.
Correction: Verify every storage and access path in the contract and configuration, then test deletion and access controls.
Calling de-identified data anonymous
Problem: Free text, rare events and combinations of fields can identify a person; model queries may enable extraction.
Correction: Apply a documented, case-specific re-identification analysis and test realistic attacks.
Securing the model but not the connector
Problem: An authorized user or injected instruction can cause retrieval of records outside the user’s need.
Correction: Enforce authorization before retrieval, minimize context, isolate tools and log source access.
Deploying without an owner
Problem: No one is responsible for monitoring, incidents, updates, appeals or changing legal requirements.
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Correction: Assign accountable owners, review dates and stop conditions before launch.
When an external API can be acceptable
An external API can be reasonable for a low-sensitivity task, or for sensitive work when the provider contract, technical configuration, data minimization, regional processing, access controls, retention limits and monitoring satisfy the documented requirements. Obtain explicit user confirmation before potentially sensitive information is sent when users control the submission, and make the destination and purpose clear.
Do not send regulated records merely because an API is convenient. If you cannot identify where the data goes, who can access it, how long it remains, how deletion works or how an incident will be handled, the evaluation is incomplete.
The bottom line
Choose the smallest, most controllable architecture that meets the task. Compare train-from-scratch, existing, fine-tuned, managed and external options against the same evidence-based axes. Verify provider claims in contracts and technical documentation; analyze re-identification and sector obligations; protect prompts, retrieval, outputs, tools and logs; test realistic failures; and document an accountable decision. The model is suitable only when the entire system can keep sensitive data within an acceptable risk boundary.
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