Evaluate an enterprise AI assistant as a governed system—not just a chat interface. Before choosing one, check how it handles organizational data and permissions, whether administrators can control identity and usage, how it fits your existing applications, what the relevant license actually includes, and how it performs on representative tasks in a bounded pilot.
What should an enterprise AI assistant be able to do safely?
Start with the work employees need it to perform and the information it will be allowed to use. An assistant connected to company repositories can make existing access problems more consequential: overshared or poorly governed content may become easier to surface in answers. Microsoft’s guidance recommends assessing oversharing, limiting access, and applying data security controls before deployment. Review repository permissions and sensitivity labels before connecting sources, rather than assuming the assistant will correct them for you. Microsoft’s Copilot security and governance guidance describes its own product controls; it is not independent evidence that a particular configuration will be effective in your environment.
- Identify intended users, tasks, and the data classes those tasks require.
- Decide which information must remain out of scope, and verify the underlying repository permissions.
- Define unacceptable outcomes, such as exposing restricted information or presenting an unsupported answer as certain.
- Test whether access controls behave as intended for users with different roles and permissions.
Which administrative and governance controls matter?
Administration should cover the assistant’s full lifecycle: onboarding, least-privilege access, approved integrations, monitoring, changes in employment or role, and offboarding. Assign owners for each control before a pilot expands. For a concrete example, OpenAI’s ChatGPT Enterprise admin quickstart recommends setting workspace ownership and administrators, configuring identity and provisioning, groups and roles, workspace settings, approved apps and connectors, security controls, monitoring, and spending controls before broad onboarding. That is vendor guidance for its product, not a substitute for checking your own required controls.
- Identity and access: Confirm supported sign-in and provisioning methods, role granularity, group management, and how access is removed when a person changes role or leaves.
- Integrations: Establish who can approve apps and connectors, what data each can reach, and how connector access is reviewed.
- Audit and response: Ask what activity can be logged and reviewed, who can access audit records, and how incidents involving sensitive data or harmful outputs are handled. Confirm any legal-hold or records requirements with the vendor for the exact product and contract.
- Usage and cost: Determine whether administrators can monitor usage, set or enforce limits, and identify spending before wider rollout.
- Third-party AI: Include unsanctioned and non-Microsoft AI applications in the same governance program. Microsoft describes capabilities in its security stack for discovering, monitoring, and managing Microsoft and non-Microsoft generative AI apps. Establish which apps are sanctioned, what browser and device activity is covered, and who owns policy, audit, and incident response. See Microsoft’s AI app management documentation for the scope it describes.
How should you assess data handling and license terms?
Request written answers for the specific product, plan, geography, and contract being considered. A general feature page does not establish that a control is available to every customer or meets your legal obligations. Ask the vendor to document:
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- Whether inputs and outputs are used to train models, and any applicable exceptions.
- Retention, deletion, and any retention options available to your organization.
- Where data is processed and stored, including regional residency eligibility.
- Subprocessors, security assurance evidence, incident-notification terms, and contractual commitments.
- Audit access and any legal-hold or records capabilities needed by your organization.
- Which controls are included in the quoted license, and what prerequisites or additional licenses apply.
Microsoft organizes its documented Copilot controls around data security, AI security, and compliance and privacy, alongside management and measurement/reporting. It distinguishes foundational from optimized controls associated with different license families, so confirm the exact eligibility and setup prerequisites for your environment using Microsoft’s controls documentation.
OpenAI states that business inputs and outputs are not used to train models by default, and describes encryption, retention options for qualifying organizations, regional residency eligibility, and administrative controls on its business data page. These are current vendor statements, not universal guarantees: verify scope, eligibility, geography, and contract terms for the precise ChatGPT Enterprise offering under review.
Do not infer that a product satisfies a compliance requirement simply because its product page lists security or privacy features. Have security, privacy, legal, and records owners review applicable assurance evidence and contractual commitments, including their scope and region.
How do you compare viable assistants?
Use the same questions and evidence standard for each candidate. Record whether each item is confirmed in documentation or contract, requires configuration, depends on another license, or remains unresolved.
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|---|---|
| Data handling | Use of inputs and outputs, retention and deletion, processing and storage regions, and contractual commitments. |
| Identity and administration | Sign-in, provisioning and deprovisioning, role granularity, groups, and lifecycle ownership. |
| Permissions and integrations | Whether source permissions are respected, connector scope, approval processes, and app governance. |
| Audit and response | Available logs, monitoring, incident response, and any required legal-hold or records controls. |
| License entitlements | Which required features are included in the actual plan, with prerequisites and eligibility confirmed. |
| Environment fit | Compatibility with the organization’s identity, endpoint, collaboration, and data platforms. |
| Pilot results | Measured performance on the organization’s representative tasks and failure cases. |
| Spend oversight | Usage visibility, limits, and the organization’s ability to manage costs. |
This framework is a procurement synthesis, not a ranking of products or independent validation of vendor claims. A feature that exists but is not enabled, included, or contractually applicable should not count as a confirmed control.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you run a meaningful pilot?
Test the intended deployment and users, not an abstract model score. NIST’s 2024 Generative AI Profile says that robust test, evaluation, validation, and verification processes can be applied iteratively and documented early in the AI lifecycle, informed by representative AI actors. It also cautions that existing pre-deployment testing may be inadequate or mismatched to deployment context. Read the NIST AI 600-1 Generative AI Profile.
- Define the pilot boundary. Specify users, tasks, data classes, connected sources, and outcomes that would make the pilot unacceptable.
- Check the environment first. Inventory identity, collaboration, repositories, endpoints, and AI apps; review source permissions before connecting data.
- Complete vendor and contract due diligence. Obtain applicable documentation for data use, retention and deletion, regions, subprocessors, assurance, incident notification, audit access, and contractual terms. NIST also identifies procurement due diligence, service-level agreements, software bills of materials, and attestation reports as possible controls for third-party risks.
- Configure a bounded deployment. Set up identity, groups, least privilege, provisioning and deprovisioning, approved integrations, usage limits, and logging before inviting pilot users.
- Test representative and failure cases. Include realistic tasks and permission scenarios. Evaluate answer quality, source grounding, access behavior, handling of uncertainty or failure, latency, and cost against criteria written in advance. These are practical pilot measures, not reported NIST test results.
- Review results with affected stakeholders. Include security, privacy, legal or compliance, records, and end-user representatives; document limitations as well as successful cases.
- Set rollout and monitoring ownership. Name who reviews usage and spend, receives reports of errors or sensitive-data exposure, and decides when a problem requires restricting or stopping use.
NIST notes that third-party generative AI use can raise intellectual-property, privacy, and information-security risks. A pilot should therefore assess not only usefulness but also whether the organization can govern the data flows, commitments, and operational response that come with the deployment.
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