Build an enterprise AI pilot around one defined workflow, a limited user group, approved data, and controls you can test—not around an open-ended invitation to “try AI.” Before anyone submits company information, document what the system may do, what information may flow through it, who can access that information, how the service handles it, and what evidence is required before the pilot can expand.
This is a practical governance and architecture guide, not legal advice or a guarantee that any vendor configuration is secure by default. NIST describes its AI Risk Management Framework (AI RMF) as voluntary and use-case-agnostic; its Generative AI Profile applies that risk-management approach to generative AI. The AI RMF page says the framework is being revised, so check the current guidance and applicable sector requirements as you plan. NIST AI Risk Management Framework · NIST AI 600-1: Generative Artificial Intelligence Profile
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1. Define the pilot before choosing a model
Start with a specific workflow and a bounded decision the system will support. A focused use case makes it possible to assess risk, measure benefit, and decide whether the pilot should continue. Avoid granting broad access simply to explore what AI might do.
Write a short pilot charter that records:
- The task or workflow being improved and the intended users.
- The decision the system supports—and decisions it must not make on its own.
- The expected benefit and how you will measure it against the current process.
- What is out of scope, including prohibited use cases or user groups.
- Who approves the pilot, who owns day-to-day operation, and who can pause or stop it.
NIST’s AI RMF is voluntary and designed to apply across use cases; its Generative AI Profile adapts the framework’s approach to generative AI. Neither supplies a universal pass/fail threshold for a particular enterprise pilot. Set your own criteria based on the workflow and its risks. NIST AI Risk Management Framework · NIST AI 600-1: Generative Artificial Intelligence Profile
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2. Define the data boundary and map every flow
Answer the question “What data should we allow into an enterprise AI pilot?” with an explicit, written policy—not an assumption that a product’s general “enterprise” positioning settles the issue. Map information from the user’s prompt through your application, retrieval systems or connected tools, provider, logs, and generated output. Include any downstream storage or recipients.
For each data class, record what is allowed, restricted, or prohibited. Consider sensitive, regulated, confidential, customer, employee, and third-party information, as well as intellectual property. Specify who may submit data and who may see retrieved material or outputs. Third-party generative AI services can raise privacy, intellectual-property, and information-security risks; NIST recommends due diligence and risk controls for these systems. NIST AI 600-1: Generative Artificial Intelligence Profile (PDF)
Confirm the exact provider service and configuration, using current service documentation and contract terms, rather than inferring details from general marketing statements. Establish:
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- Whether prompts, outputs, or uploaded information may be used beyond providing the service.
- Retention and deletion rules, including how backups are handled.
- Where processing and storage occur, and any geographic or contractual constraints.
- Encryption and other relevant protections, plus the evidence available to your organization.
- Incident notification, cooperation, and response responsibilities.
- Subprocessors and other services that receive or handle pilot data.
These details depend on the specific service, configuration, and agreement; do not assume a particular provider’s terms or capabilities without verifying them.
For retrieval-augmented generation, enforce permissions at retrieval
If the pilot searches internal documents or other connected content, authorization must be checked when information is retrieved—not only when it is added to an index. Preserve the link between the user’s identity and the permissions on each source document. Use metadata filters or an equivalent authorization mechanism, restrict who can add or change indexed content, and provide source attribution where the product supports it. A user should retrieve only material they are permitted to access.
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Treat retrieved documents, user prompts, memory, and tool results as untrusted input. They may contain misleading or malicious instructions. Keep them separate from system instructions, and test whether the application can resist attempts to override its rules through retrieved content. Microsoft’s guidance describes security control patterns for generative AI applications; it is vendor-authored implementation guidance, not proof that any individual control makes a deployment secure. Microsoft: Secure AI
3. Assign ownership and put controls in the application
A policy is not an access control. Translate the pilot’s data rules into the application, identity system, retrieval layer, and connected tools. Name accountable owners for each area so that approval, implementation, and response do not fall between teams.
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|---|---|
| Governance and risk | Use-case approval, risk review, acceptable-use rules, and expansion decision. |
| Security architecture | Identity, access controls, data flows, tool restrictions, logging, and incident coordination. |
| Product and engineering | Application behavior, permission-aware retrieval, user experience, evaluation, and technical changes. |
| Privacy and legal | Applicable privacy, contractual, intellectual-property, and sector-specific review. |
| Operations | Monitoring, user support, incident escalation, service changes, and shutdown or recovery procedures. |
Use your organization’s existing procurement, cybersecurity, privacy, and third-party risk processes where appropriate. Review the provider, applicable service terms, security evidence, incident duties, subprocessors, and data flows. Microsoft recommends treating AI risk as part of broader organizational risk, cybersecurity, and privacy governance; use such guidance as a control reference and verify actual service capabilities independently. Microsoft Cloud Adoption Framework: Govern AI
Apply least privilege to users, service identities, data, and tools. Limit connected actions to the pilot’s purpose, and require human approval for consequential or externally visible actions. Create a short acceptable-use policy and a clear approval path. Log enough information to investigate failures and incidents—such as user identity, model and version, references to retrieved context, tool calls, decisions, and outputs—while aligning logs with privacy and retention requirements. Decide who may access those logs and how long they are retained.
4. Test risks and usefulness before exposing the pilot
Define a baseline and evaluation set using representative pilot tasks and privacy-safe or otherwise approved data. Include reviewers and users who reflect the actual people and situations in scope. Test whether the pilot improves the target workflow and whether it fails safely when conditions are difficult.
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Include checks for:
- Answer quality, consistency, and unsupported claims on representative tasks.
- Attempts to bypass access controls or expose information across users.
- Sensitive-data disclosure in prompts, retrieved context, logs, or outputs.
- Prompt injection through user input or retrieved documents.
- Unsafe or unauthorized tool use, including actions that should require human approval.
- Malformed, adversarial, or ambiguous inputs and appropriate fallback behavior.
Keep test plans, results, observed failures, mitigations, and approval records. NIST recommends iterative, documented testing, evaluation, validation, and verification (TEVV) early and throughout the AI lifecycle. Testing should continue as the pilot changes, rather than being treated as a one-time launch gate. NIST AI 600-1: Generative Artificial Intelligence Profile (PDF)
5. Monitor, respond, and set expansion gates
During the pilot, monitor use, quality, failures, complaints, and security events against the charter. Make it clear how users report a problem, who investigates it, and who can disable the system or a connected capability. Define recovery steps before a serious incident occurs.
Set explicit expansion criteria before the pilot begins. A decision to expand should depend on evidence that the intended benefit is being achieved and that quality, privacy, security, and operational controls work in the target context. There is no universal NIST numerical threshold for these criteria; choose measures and limits appropriate to your use case, then document the decision and its rationale.
Reassess risk when the provider or model changes, when new data or tools are connected, when the user population grows, or when the workflow changes. A pilot approved for one bounded task does not automatically establish that a broader use is acceptable. The AI RMF takes a lifecycle-wide approach to risk management, while NIST and Microsoft guidance emphasize governance, testing, monitoring, and response. NIST AI Risk Management Framework · Microsoft: Secure AI
How to compare architecture or service options
If you have multiple viable options, compare them against the same pilot requirements rather than relying on a generic “enterprise-ready” label. The following are evaluation criteria, not findings about any particular vendor:
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| Area | Questions to answer |
|---|---|
| Data handling | What do the exact service terms say about provider use, retention, deletion, regional processing, encryption, and contractual protections? |
| Authorization | Can the option integrate with your identity system and enforce document-level permissions during retrieval? |
| Control and audit | Can you restrict tools, require human approvals, isolate deployment as needed, retain useful audit evidence, and coordinate incident response? |
| Evaluation | Can your team run representative tests, exercise relevant failure modes, record model and version changes, and monitor behavior? |
| Operational fit | Can the team maintain the controls? What are the integration, reliability, ownership, cost, and exit-path implications? |
Verify current documentation and contractual terms for the exact service and configuration you intend to use. A comparison is only useful when it tests those options against your data boundary, workflow, and operational capacity.
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