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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesBuild an enterprise AI pilot around one defined workflow, approved users, and a measurable benefit—not an open-ended invitation to “try AI.” Before anyone submits company information, decide what data the system may use, how access is enforced, which safeguards are required, and what evidence would justify expanding the pilot. The sequence below gives security, privacy, legal, product, and operations teams a practical way to set those boundaries.
1. Define the pilot before choosing a model
Start with a one-page charter that makes the intended use concrete. Identify the workflow being improved, the people who will use the system, the decision or task it supports, and the expected benefit. State what is out of scope and name who can approve, pause, or stop the pilot.
- Workflow: describe a specific task rather than a broad goal such as “use AI across the company.”
- Users: specify the roles and groups permitted to participate.
- Decision boundary: explain whether the system drafts, summarizes, recommends, or takes an action—and what remains a human responsibility.
- Success evidence: decide how the team will measure benefit and acceptable quality for this task.
- Authority: name the pilot sponsor and the people responsible for approval and shutdown.
NIST’s AI Risk Management Framework (AI RMF) is a voluntary, use-case-agnostic framework for incorporating trustworthiness into AI design, development, use, and evaluation. NIST published version 1.0 on January 26, 2023, and says the framework is being revised; check its current status and any sector-specific obligations when establishing a program. Its Generative AI Profile, released July 26, 2024, applies the framework’s risk-management approach to generative AI. These frameworks guide risk management; they do not supply a universal pass score for a pilot.
2. Decide what data may enter and where it goes
Map the complete information path: user prompt, application, retrieval system or connected tools, model provider, logs, and generated output. For each part, identify which organization or system receives the information and who can access it. Inventory relevant data classes, including confidential business information, personal or employee information, regulated records, customer data, and third-party material.
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Write explicit data rules
- List permitted data sources and the information users must not submit.
- Define who may submit prompts, view outputs, access connected data, and administer the system.
- Set retention, deletion, and backup requirements for prompts, outputs, indexes, and logs.
- Identify geographic, contractual, and regulatory constraints that apply to the workflow.
- Confirm whether the specific provider service and configuration permit use of prompts or outputs beyond delivering the service.
Do not infer service-specific data handling from general marketing language. Confirm it in current product documentation and the applicable contract. NIST’s Generative Artificial Intelligence Profile identifies third-party data collection and use, privacy, intellectual-property, and information-security risks as areas for risk management. It does not establish the terms of any particular vendor service.
Enforce permissions in retrieval
For a retrieval-augmented system, permission checks must apply when content is retrieved, not only when a document is first added to an index. Keep the user’s identity and document permissions connected to retrieval; use metadata filters or equivalent authorization controls; and limit who can write to or change indexes. Where supported, show users the sources behind retrieved answers.
Prompts, retrieved documents, memory, and tool results should be treated as untrusted input: any of them could contain instructions that conflict with the application’s rules. Separate trusted system instructions from retrieved text, and test whether hostile or misleading content can alter the system’s behavior. Microsoft’s Prompt Shields guidance describes relevant prompt-injection risks and control patterns; it is vendor-authored guidance, not proof that a particular deployment is secure.
3. Assign owners and put controls around the system
Make accountability explicit across governance and risk, security architecture, product engineering, privacy and legal, and operations. The organization’s existing acquisition, cybersecurity, and privacy processes can be adapted where they fit. Review the provider and exact service, applicable terms, security evidence, incident responsibilities, subprocessors, and data flows before exposing company data.
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NIST’s Generative AI Profile recommends due diligence and established risk controls for third-party generative AI. Microsoft’s AI governance guidance places AI risk management within broader organizational risk, cybersecurity, and privacy governance. Use such guidance to identify control areas, then verify actual capabilities and contractual commitments for the service being considered.
Apply controls at the application boundary
- Grant least-privilege access to users, service identities, connected data, and tools.
- Constrain available tools and actions to the pilot’s stated purpose.
- Require human approval for consequential or externally visible actions.
- Keep trusted system instructions separate from untrusted user and retrieved content.
- Keep logs sufficient to investigate use and incidents—such as user identity, model and version, references to retrieved context, tool calls, decisions, and outputs—subject to the organization’s privacy and retention requirements.
These are implementation recommendations, not a guarantee that a single product feature or configuration makes a system secure. Logging itself needs boundaries: collect what is useful for oversight and incident reconstruction, and align access, retention, and deletion with the pilot’s data rules.
4. Test the risks before users rely on it
Before user exposure, create a baseline and an evaluation set based on real tasks in scope. Use privacy-safe or otherwise approved representative data. Include reviewers and users who reflect the intended population and operating conditions, and record the test plan, failures, mitigations, and approvals.
Test quality alongside security and privacy failure modes, including:
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- Whether answers are useful and sufficiently accurate for the defined task.
- Whether users can retrieve content they are not authorized to see.
- Whether user prompts or retrieved documents can inject instructions that override safeguards.
- Whether prompts or outputs expose sensitive information.
- Whether tools can be misused or take actions beyond the pilot’s purpose.
- How the system behaves with malformed, ambiguous, or adversarial inputs.
NIST’s Generative AI Profile calls for iterative, documented testing, evaluation, validation, and verification (TEVV) early and across the AI lifecycle. A single pre-launch test is not a substitute for continuing evaluation as usage and system components change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Monitor the pilot and set expansion gates
During the pilot, monitor usage, quality, failures, complaints, and security events against the charter. Make reporting channels, incident ownership, and the shutdown path clear to participants and operators. Retain enough evidence to review how the system behaved and to reconstruct incidents, while following the established limits on data retention and access.
Agree in advance what must be true before the pilot can grow: the intended benefit is demonstrated, quality is acceptable for the task, access and privacy controls work in the target context, and the team can operate and support the system. These are organization-set gates, not universal numerical thresholds prescribed by NIST. Reassess risk when the model, provider, connected data, tools, user population, or use case changes.
How to compare enterprise AI options
“Enterprise-ready” is not a substitute for checking the requirements of your workflow. Compare real platform or architecture options against the same criteria, and verify service-specific claims in current documentation and contracts.
| Area | Questions to answer |
|---|---|
| Data handling | What are the service’s use and retention terms? How do deletion, regional processing, encryption, and contractual protections apply to the exact service and configuration? |
| Authorization | Can the option integrate with organizational identity and enforce document-level permissions during retrieval? How does it prevent one user from accessing another user’s content? |
| Control and audit | Can administrators limit tools and actions, require human approval, isolate deployment, review logs, and obtain evidence needed for incident response? |
| Evaluation | Can the team run representative tests, test relevant failure modes, track model and version changes, and monitor behavior during use? |
| Operational fit | Can the responsible team maintain the controls and integrations? What are the reliability, ownership, cost, and exit considerations for the intended pilot? |
The criteria above are questions to investigate, not comparative findings about particular vendors. The available guidance does not establish vendor-specific data terms, capabilities, or contract protections; verify those for the exact service under consideration.
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