Choose enterprise AI software by testing whether it can perform a defined task safely with your organization’s data—not by comparing feature lists or advertised model capacity alone. Start by documenting the workflow, users, sensitive data, consequences of errors, and required human oversight. Then verify product-specific security evidence, test every important integration and permission boundary, and evaluate answers against realistic, authorized information before procurement.
1. Define the work and its risk before comparing products
Write down the proposed use case in operational terms. A tool that drafts internal summaries has different consequences from an agent that changes customer records or recommends a consequential decision. Specify:
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- Users and workflow: who will use the system, what task it supports, and where its output goes.
- Data: what users may submit and what connected sources may expose, including personal, confidential, or regulated information.
- Impact of error: what could happen if an answer is wrong, incomplete, stale, or disclosed to the wrong person.
- Authority and oversight: whether the system only retrieves or drafts, or can take actions; who reviews consequential outputs and handles escalation.
- Limits: what the system must not do, what evidence is required for an answer, and when it should decline or hand off.
Use that risk description to decide what evidence and safeguards are necessary. Microsoft’s AI workload governance guidance identifies risks including data breaches, unauthorized access, manipulation, misuse, third-party dependencies, and integration problems such as dependency cascades, incompatible data formats, performance bottlenecks, and security gaps.
2. Verify security and privacy for the exact service
Ask the vendor for written answers that apply to the specific product, deployment, region, and features you plan to use. A vendor-wide security page or certification does not by itself establish that every service, configuration, or customer contract has the same coverage.
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- Data use: Are prompts, files, and outputs used to train models or improve services? Which exceptions or settings apply?
- Retention and deletion: How long are inputs, outputs, and logs retained? What deletion controls and timelines apply?
- Protection and access: What encryption is used in transit and at rest? How are identities, roles, groups, administrators, and tenants separated?
- Operations: What is logged, can logs be exported, how are incidents notified, and what controls exist for keys, private networking, and data location?
- Independent assurance: Which audit reports, certifications, and compliance features cover the proposed service boundary, region, and deployment?
- Dependencies and changes: Which models, subprocessors, or external data sources are involved, and how are material changes communicated?
OpenAI states that its business products do not train on organizational data by default and describes encryption, controls, certifications, and compliance features on its business privacy and security page. Treat these as vendor statements to verify against the product, configuration, region, and contract under consideration; do not assume identical coverage across offerings.
3. Treat integrations as both a security boundary and a reliability dependency
Inventory every repository, application, API, and action the workflow needs. For each connector, establish what it can read or change, whose permissions it uses, how access is revoked, and what administrators can inspect. Keep retrieval separate from write access wherever practical.
- Verify that source permissions carry through to retrieval and that users cannot see records they could not access in the source system.
- Check synchronization frequency, freshness indicators, deletion and revocation behavior, and audit trails.
- Test unavailable sources, malformed records, changed schemas, rate limits, source outages, and an outage of the AI service itself.
- For agents that can take actions, require narrowly scoped roles, human approval where appropriate, auditable action records, and a way to halt or contain a faulty workflow.
Microsoft’s governance guidance calls out dependency cascades, format incompatibilities, performance bottlenecks, and security gaps as integration risks. Its Azure AI workload guidance also recommends auditability, role-based access control, and circuit-breaker functionality for agentic workloads.
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“Context” should mean more than the size of a model’s advertised context window. Decide which approved sources the system may use, how current they need to be, and how a user can tell what evidence supports an answer. Microsoft’s workload guidance emphasizes context-specific policies and safeguards when agents access private data and systems.
- Name the authoritative repositories and the acceptable age of information for the task.
- Check whether retrieval respects source permissions, including restricted folders, groups, and recently revoked access.
- Inspect whether answers show useful citations or other provenance that lets a person verify the source.
- Test how the system behaves when evidence is absent, outdated, contradictory, or inaccessible; require an appropriate abstention or escalation rather than a confident guess.
- Score factual support, completeness, sensitive-data handling, and clarity about uncertainty on representative tasks.
No universal context-size measure established here predicts enterprise retrieval quality across vendors. Evaluate the actual retrieval and answer behavior with your own authorized material instead of treating token capacity or a generic model benchmark as a proxy.
5. Run a representative pilot before procurement
Use the same task set, data conditions, and scoring rules for every shortlisted option. Include ordinary work and difficult cases: conflicting sources, missing context, stale content, permission-restricted material, and integration failures. Keep the documents authorized for the pilot and ensure the test environment reflects the intended deployment closely enough to expose meaningful risks.
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- Set acceptance criteria: define minimum factual support, source visibility, permission behavior, response time, escalation, and human-review requirements for the use case.
- Exercise the workflow: test read-only retrieval and any proposed write actions separately; inspect permissions, logs, and failure handling.
- Record results: score outputs against evidence and capture cases where the product invents, omits, exposes, or mishandles information.
- Set rollout and stop conditions: begin with a bounded group, assign owners, monitor issues, and pause or narrow use if agreed thresholds are not met.
6. Compare shortlisted options on the same axes
Use a common evaluation sheet rather than letting different vendors define success with different demos. These are practical comparison dimensions, not a published benchmark or universal ranking.
| Axis | What to compare |
|---|---|
| Security evidence | Scope of audit reports and certifications; identity and access controls; encryption; data use and retention; auditability; and incident commitments for the proposed service. |
| Integration fit | Required connectors and APIs; permission inheritance; administrative control; freshness; resilience; latency; and operational effort. |
| Context quality | Retrieval relevance, source traceability, freshness, permission-aware retrieval, and behavior with missing or conflicting evidence. |
| Governance | Evaluation and logging tools, policy enforcement, configuration, change management, and fit with existing risk ownership. |
| Deployment and commercial fit | Region, architecture, support, service commitments, total cost, contract terms, and exit or data-portability provisions. Verify details directly for each shortlisted product. |
7. Assign ownership and use governance frameworks appropriately
Assign accountable owners across the business, IT, security, privacy, legal, and procurement teams. Document intended use, foreseeable misuse, risk tolerance, evaluation, monitoring, escalation, and how the deployment will change or be retired.
Best Value
NIST’s AI Risk Management Framework (AI RMF) 1.0 is voluntary and intended to incorporate trustworthiness considerations throughout AI design, development, use, and evaluation. Its functions—Govern, Map, Measure, and Manage—can organize a buyer’s work. The companion Playbook offers suggested actions, but NIST explicitly says it is neither a checklist nor a mandatory sequence. NIST says AI RMF 1.0 is being revised, so consult the current framework materials rather than assuming the version or status will remain unchanged.
Quick Recap
Buyer’s checklist
- We have defined the task, users, data, error consequences, limits, and human oversight.
- We have product-specific written answers for data use, retention, deletion, encryption, access, logging, incidents, and location.
- We have mapped independent assurance to the actual service, deployment, region, and features.
- We have tested connector permissions, revocation, freshness, auditability, and failure handling.
- We have validated retrieval and answers against authorized, representative documents, including missing, conflicting, stale, and restricted information.
- We have named owners, acceptance criteria, monitoring, escalation, and rollout or stop conditions.
- We have checked current contract, regional, service, and exit terms directly with each provider.
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