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Shortlist enterprise AI assistants by checking how they handle organizational data and permissions, what administrators can control, how well they fit your existing systems, and whether they perform reliably on your real tasks. Then verify that the required safeguards are included in your exact plan and contract, and test the product in a bounded pilot before expanding access.
Start with the work the assistant will do
Define the intended users, tasks, and data before comparing product features. An assistant used to summarize internal documents has different exposure and failure risks from one used only to draft generic text. Specify the sensitive data classes it may encounter and the outcomes the organization considers unacceptable.
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Use those decisions to set pilot boundaries and acceptance criteria. For example, decide in advance how you will assess answer quality, whether responses are grounded in permitted sources, how the assistant behaves when it lacks an answer, and what latency or spending limits are acceptable. These are organization-specific evaluation choices, not universal model benchmarks.
Check data handling and source permissions
Ask the vendor to document how the exact product and plan use inputs and outputs, how retention and deletion work, where data is processed and stored, which subprocessors are involved, and what contractual security and incident-notification commitments apply. Confirm audit access and applicable assurance evidence with security, privacy, legal, and records owners. A general product page alone does not establish that a product meets a particular compliance requirement.
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Review the access controls on repositories before connecting them. An assistant can make overshared or poorly governed source material easier to surface in answers; it does not fix the underlying permissions. Microsoft’s Copilot security and governance guidance likewise emphasizes assessing oversharing, limiting access, and applying data-security controls.
Ask specifically how connector scope maps to existing user permissions, what happens when source access changes, and whether administrators can restrict or disable integrations. Validate the answers in the pilot rather than assuming that a connector automatically preserves every intended boundary.
Evaluate administrative controls across the lifecycle
Administration is more than initial setup. Identify who owns the workspace and who can administer it; how users are authenticated and provisioned; how groups and roles are assigned; and how access is removed when a user changes role or leaves. Also establish ownership for approved apps and connectors, security settings, monitoring, and spending controls.
OpenAI’s ChatGPT Enterprise admin quickstart lays out these setup areas, including workspace owners and admins, identity and provisioning, groups and roles, approved apps/connectors, security controls, monitoring, and spending controls. Treat them as a practical checklist to assign named owners, not as a substitute for validating your own requirements.
Before broad onboarding, confirm that the administrative interface and your identity provider support the access model you need. Test both granting and revoking access, including access to connected data and apps.
Compare controls at the exact license and contract level
Security, compliance, retention, and residency capabilities may differ by plan, license family, organization eligibility, geography, and configuration. Record each required control and ask the vendor to confirm its availability and prerequisites for your specific purchase. Do not treat a capability described on a general feature page as a universal guarantee.
Microsoft groups Copilot controls across data security, AI security, compliance and privacy, management, and measurement/reporting, and identifies some controls as foundational and others as optimized for particular license families. OpenAI states that business inputs and outputs are not used to train models by default and describes encryption, administrative controls, retention options for qualifying organizations, and regional residency eligibility. These are vendor descriptions of their products; verify current scope, contract terms, and geographic eligibility directly with the vendor. See Microsoft’s control documentation and OpenAI’s business data information.
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Map the assistant against your current identity, endpoint, collaboration, and data platforms. Consider which repositories and business applications it can connect to, how those connections will be approved and maintained, and whether the integration scope matches the pilot’s security boundaries.
Include unsanctioned and third-party AI apps in the same governance program. Microsoft describes capabilities to discover, monitor, and manage Microsoft and non-Microsoft generative AI applications through its security stack. Its AI app management documentation is a vendor description, not an independent efficacy finding. For your own environment, define which apps are sanctioned, what browser and device coverage exists, who owns policy decisions, what audit records are available, and how incidents will be handled.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Run a representative, documented pilot
Evaluate the assistant in the deployment context you intend to use, rather than relying on 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 can be inadequate or mismatched to the deployment context. Read the NIST Generative AI Profile.
Build a test set from realistic user tasks and data, and agree on pass/fail criteria before testing. Include security, privacy, legal/compliance, records, and end-user stakeholders. Record both results and limitations; a successful demonstration on a few convenient prompts is not evidence that the assistant is ready for broad use.
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- Whether answers cite or otherwise ground themselves in sources the user is permitted to access.
- Permission behavior when users have different access, or when access to a source changes.
- Failure handling, including unsupported answers, sensitive-data exposure attempts, and harmful or misleading outputs.
- Latency and spend under the expected usage pattern.
NIST also notes that third-party generative AI use can raise intellectual property, privacy, and information-security risks. Its profile points to procurement due diligence, service-level agreements, software bills of materials, and attestation reports as possible controls. Decide which evidence is relevant to your vendor and deployment, and review it with the appropriate owners.
Use a consistent comparison rubric
When two or more products remain viable, score them against the same requirements. Separate mandatory controls from preferences, and distinguish verified evidence from vendor statements or pilot observations.
Quick Recap
| Comparison area | What to establish |
|---|---|
| Data handling | Use of inputs and outputs, retention and deletion, processing and storage regions, subprocessors, and contractual commitments. |
| Identity and administration | Authentication, provisioning and deprovisioning, role granularity, ownership, and lifecycle administration. |
| Permissions and integrations | Source permissions, connector scope, approved apps, and governance of third-party AI tools. |
| Audit and response | Available audit records, monitoring, legal-hold needs, incident handling, and escalation ownership. |
| Plan entitlements | Whether required controls are included in the exact license, contract, and region being considered. |
| Environment fit | Compatibility with current identity, endpoint, collaboration, and data platforms. |
| Pilot evidence | Results against representative tasks, documented limits, and agreed acceptance criteria. |
| Spend oversight | Visibility into usage and the ability to set or enforce appropriate limits. |
Move from pilot to rollout deliberately
- Define scope: Document users, tasks, sensitive data classes, and unacceptable outcomes.
- Inventory the environment: Map identity, collaboration, repositories, endpoints, and existing AI apps; review source permissions before connecting data.
- Complete vendor due diligence: Obtain applicable documentation for data use, retention and deletion, regions, subprocessors, security assurance, incident notification, audit access, and contract terms.
- Configure a bounded pilot: Set up identity, groups, least privilege, provisioning and deprovisioning, approved integrations, usage limits, and logging.
- Test and document: Run representative and adversarial or failure cases with relevant stakeholders; record outcomes and limitations against the agreed criteria.
- Assign ongoing ownership: Set rollout scope, monitoring responsibility, usage and spend review, and a way for users to report errors, sensitive-data exposure, or harmful outputs.
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