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The right enterprise AI platform is the one whose security boundary, data terms, integration model, governance controls, and commercial commitments fit each workload you intend to run. There is no universal winner: a drafting assistant, a retrieval system over confidential records, and an agent that can change business systems require different controls and tolerances.
Use this guide to define workloads first, verify how data moves and is retained, test identity and permissions end to end, and score shortlisted platforms against the same evidence. Treat vendor demonstrations and marketing statements as starting points; require configuration-specific documentation, tests, and contract language before approval.
What an enterprise AI platform must do
An enterprise AI platform combines models with the services needed to develop, deploy, secure, monitor, and govern AI applications. Depending on the product, those services can include model access, prompt and application management, retrieval over enterprise data, fine-tuning, connectors, tool execution, identity integration, content controls, logging, evaluation, and operational support.
The label “enterprise AI platform” hides materially different architectures. A low-risk assistant that drafts internal text may need limited read access and human review. A retrieval application over personnel or financial records needs permission-preserving search, strict data handling, and provenance. An autonomous agent that can approve payments or modify production systems needs narrowly scoped tools, stronger approvals, comprehensive audit trails, and a tested recovery path.
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Start with the workload rather than a feature list. NIST’s Generative AI Profile is a cross-sector companion to AI RMF 1.0 and covers contexts such as large language model use, cloud services, and acquisition. It is voluntary and intended to help organizations tailor practices to their goals, risk tolerance, resources, and legal obligations.
1. Define each workload before comparing platforms
Create a one-page record for every proposed use case. Do not score a platform until the record identifies who will use the system, what it can do, and what happens when it is wrong.
Workload definition template
| Question | What to document |
|---|---|
| Purpose and users | The business objective, user groups, administrator groups, and whether people outside the organization receive outputs. |
| Information handled | Prompts, uploaded files, retrieved passages, outputs, embeddings, feedback, fine-tuning data, and any regulated or confidential content. |
| Source systems | Repositories, databases, APIs, identity directories, and other systems that provide context or receive actions. |
| Business impact | What decisions or operations depend on the output, the acceptable error rate, and the consequence of delay or unavailability. |
| Possible harms | Privacy loss, unauthorized disclosure, discrimination, unsafe advice, fraud, operational damage, or other foreseeable effects. |
| Decision authority | Whether the model informs a person, recommends an action, or can execute an action without additional approval. |
| External dependencies | Model providers, connectors, subprocessors, hosted indexes, monitoring services, and support personnel. |
| Risk tolerance | Required review, segregation, residency, retention, availability, and incident-response thresholds. |
Microsoft’s AI governance guidance similarly recommends understanding a workload’s function, data sources, outcomes, limitations, and third-party dependencies. Record the assumptions behind each answer; they become acceptance criteria for the proof of concept and contract review.
Classify the deployment pattern
Control depth should match both the data and the architecture. AWS distinguishes consumer-facing applications, enterprise applications, applications using pretrained models, fine-tuned models, and self-trained models. Its security and governance guidance notes that data sensitivity and business criticality influence the controls required. Use this classification to avoid applying a light control set to a high-impact agent or over-engineering a low-risk drafting tool.
2. Evaluate the security boundary with evidence
Ask the vendor and your internal platform team to demonstrate each control in the exact service, model, region, and configuration you would buy. A slide describing a capability is not evidence that it applies to your SKU or deployment path.
Identity and access
- Restrict access by user, group, role, workload, model, data source, and action. Confirm whether permissions are evaluated at query time rather than only when a connector is configured.
- Separate platform administrators, application developers, evaluators, operators, and end users. Test that experimentation credentials cannot reach production data or tools.
- Use workload identities for applications and agents, with short-lived credentials where supported. Confirm how service accounts, delegated user identity, and privileged escalation are logged.
- For tools that change business systems, grant only the operations required for that use case. Require a human approval step for consequential actions when the risk assessment calls for it.
- Ask how access is revoked, how group changes propagate, and what happens to cached results and indexes after a user loses permission.
AWS uses granular IAM roles as one implementation example in its security guidance. The specific role design, federation method, and entitlement model still need to be tested in your environment.
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Network and data boundaries
- Determine where prompts, retrieved context, files, and outputs are processed and stored, including cross-region inference or failover.
- Confirm whether private network paths are available for sensitive workloads and whether administrative interfaces use the same boundary. AWS documents PrivateLink as an option for invoking models from a VPC or on-premises network.
- Document encryption in transit and at rest, key ownership, rotation, customer-managed key support, and what metadata remains visible to the provider.
- Check egress controls for connectors, tool calls, telemetry, support sessions, and exported logs.
Input, output, and tool controls
- Identify filters for secrets, personal data, unsafe requests, malicious files, and policy-violating outputs. Ask what can be configured, what is fixed by the provider, and what bypass conditions exist.
- Test prompt-injection resistance, including instructions embedded in retrieved documents, web pages, spreadsheets, and tool responses.
- For multimodal systems, include images, audio, and documents in abuse and data-leakage tests if the workload accepts them.
- Verify whether guardrails apply before retrieval, after model generation, before a tool call, and before an output reaches a user or downstream system.
Logging and incident response
- List identity events, configuration changes, model invocations, retrieval requests, tool calls, administrative actions, policy decisions, and failures that are logged.
- Distinguish content logs from metadata logs. Determine whether prompts and outputs are captured, redacted, encrypted, searchable, exportable, and subject to separate retention rules.
- Connect logs to your existing security information and event management, detection, ticketing, and legal-hold processes. Establish who can access sensitive content in an investigation.
- Request incident-notification timelines, evidence preservation procedures, abuse escalation routes, and support responsibilities for provider and customer components.
AWS identifies invocation logs and API audit trails as possible controls. Treat that as an architectural example, not proof that every service records the same fields or retains them for the period your policy requires.
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- Obtain availability, capacity, latency, quota, fallback, recovery, and support commitments for the exact contracted service. Public guidance can identify considerations such as redundancy and scalability, but it does not establish your contractual service level.
- Ask how model versions, safety policies, connectors, embeddings, and system prompts change; require advance notice and a way to test or pin versions where the workload needs it.
- Define behavior during provider outages, throttling, regional failures, dependency failures, and degraded model quality. Include a manual or alternate-process fallback.
Pre-release and continuous testing
Set acceptance criteria before a proof of concept. Evaluate representative tasks, adversarial inputs, retrieval permissions, refusal behavior, latency, cost, and human-review effort. Repeat tests after model, prompt, index, connector, policy, or application changes. Consequential outputs need documented human review and an escalation path rather than an assumption that a general model score guarantees safety.
3. Verify data handling for the exact service configuration
Never reduce the data question to “Is customer data used to train models?” That answer can differ by service, model source, feature, region, retention setting, and contract. Build an inventory and obtain a written answer for every data category.
Data categories to inventory
| Data category | Questions to answer |
|---|---|
| Prompts and system instructions | Are they stored, reviewed for abuse, cached, used for service improvement, or accessible to support staff? |
| Completions and generated files | When are they retained, who can retrieve them, and how are deletion and export handled? |
| Embeddings and indexes | Where are vectors and source chunks stored, how are tenant boundaries enforced, and how are deleted or reclassified records removed? |
| Uploaded and retrieved content | What processing region, malware scanning, caching, logging, and downstream-provider access apply? |
| Fine-tuning and evaluation sets | Can the provider or subcontractors access them, are they used for training, and what happens when a model or project is deleted? |
| Feedback and telemetry | What content and metadata are collected, for what purpose, for how long, and with what opt-out or exception conditions? |
Retention, regions, access, and training use
For each category, document collection and storage, abuse monitoring, session features, deletion timing, processing and storage regions, cross-region inference, provider and subcontractor access, human review, encryption and keys, and customer-controlled logging. Record whether the behavior changes for preview, optional, or separately activated features.
Microsoft’s Foundry data-privacy documentation states that prompts, completions, embeddings, and training data for models sold by Azure are not used by model providers to train foundation models without permission or instruction and are not available to other customers or model providers. The same documentation describes stored completions as a feature, so the actual setting and service behavior must be reviewed for your deployment.
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Put the resulting commitments in the data-processing agreement, order form, security addendum, or other binding document. A general statement about training use does not answer questions about cached sessions, abuse monitoring, support access, embeddings, logs, or deletion.
4. Treat integration as an end-to-end permission and data-flow problem
Map the complete route from each source of truth to the model and back to the user or business system. Your diagram should show connectors and APIs, identity propagation, synchronization, indexing, metadata filters, prompt construction, model calls, tools, output destinations, and logs.
Permission-preserving retrieval
Retrieval-augmented generation can give a model current enterprise information, but it adds security questions. AWS’s secure data-access guidance discusses knowledge-base data protection, access controls, network security, logging and monitoring, metadata filtering, and provenance. These are design considerations, not evidence that a connector is secure by default.
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Test whether a person receives only records that person is entitled to see. Include permission revocation, deleted records, changed group membership, stale indexes, duplicate records, inherited folder permissions, and records with conflicting metadata. Verify that citations or provenance identify the source used, without exposing restricted snippets in an error message or audit view.
Connector and API review
- List every permission requested by each connector, including read, write, search, administrative, and delegated scopes.
- Confirm synchronization frequency, change detection, deletion propagation, rate limits, pagination behavior, and failure recovery.
- Check where connector credentials and indexed content reside, which tenants share infrastructure, and whether data can be exported or deleted independently.
- Verify that API responses are validated before they become model context or tool parameters.
- Ensure logs record the user, service identity, source, query, retrieved items, action, and result at a level compatible with privacy policy.
Agents and downstream actions
An agent that can send messages, create records, approve transactions, deploy code, or alter configuration has a larger blast radius than a read-only assistant. Separate planning from execution, allow only an explicit tool list, validate parameters, enforce transaction limits, and require confirmation for irreversible or high-impact actions. Test replay, duplicate requests, partial failures, timeouts, and recovery after a tool succeeds but the model fails to record the result.
Proof-of-concept test plan
- Load representative sources, roles, documents, and workflows rather than synthetic examples alone.
- Measure answer correctness, retrieval coverage, citation quality, latency, repeatability, refusal behavior, and human-review time.
- Attempt unauthorized retrieval with direct queries, indirect references, stale indexes, and prompt injection in source documents.
- Delete records and revoke access; verify the index, cache, logs, and generated answers reflect the change within the stated window.
- Simulate malformed files, malicious content, connector outages, model throttling, and downstream API errors.
- Inspect logs and alerts for every test, then document residual risks, compensating controls, and release conditions.
5. Make governance an operating model, not a checklist
Assign named owners for business purpose, data, model and application, security, privacy, legal review, release approval, monitoring, and incident handling. Maintain an inventory of use cases and systems, risk assessments, evaluation records, policies, limitations, change history, monitoring results, and retirement plans.
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NIST’s AI Risk Management Framework organizes work into four functions:
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| Function | Operational work |
|---|---|
| Govern | Set roles, policies, accountability, risk culture, documentation, and lifecycle oversight. |
| Map | Describe context, intended use, stakeholders, data, dependencies, limitations, and potential harms. |
| Measure | Evaluate and monitor performance, security, privacy, reliability, and other trustworthiness characteristics. |
| Manage | Prioritize risks, select responses, assign owners, track incidents, and control changes and retirement. |
Governance cuts across the lifecycle rather than occurring once at procurement. NIST cautions in its AI RMF Core: “Actions do not constitute a checklist, nor are they necessarily an ordered set of steps.” Tailor the functions to the workload and your existing control framework instead of treating framework adoption as a box-ticking shortcut.
NIST’s COSAiS FAQ says, “Organizations are not required to use the provided overlays.” The FAQ describes overlays as an optional way to customize security controls for a particular AI technology, mission, or operating environment. You can combine the AI RMF with established security controls and your regulatory obligations without adopting an overlay wholesale.
Microsoft’s governance guidance recommends risk assessment, third-party dependency review, integration policies, automated and manual enforcement, ongoing monitoring, documentation, and independent review. Use those recommendations as operational questions and map the answers to your own policies and evidence requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Compare platforms on shared evidence
Build a weighted scorecard from the workload records. Weight security, data, integration, quality, resilience, governance, cost, and architecture fit according to business impact; do not let a polished demonstration compensate for a failed non-negotiable control.
| Evaluation axis | Evidence to collect |
|---|---|
| Use-case quality and reliability | Representative-task results, failure modes, repeatability, latency, and human-review burden. |
| Security boundary | Identity and role controls, private connectivity, encryption and keys, filtering, logs, and incident support. |
| Data terms | Training use, retention, processing region, provider access, deletion, and feature-specific exceptions. |
| Integration | Connector and API scopes, identity propagation, permission preservation, freshness, provenance, and exportability. |
| Governance | Inventory, evaluations, policy enforcement, monitoring, approvals, audit evidence, and incident handling. |
| Resilience and operations | Availability commitments, capacity, fallback, recovery, support, quotas, and change notices. |
| Cost and exit | Current quote for representative usage, integration and support costs, usage controls, portability, export, and transition plan. |
| Architecture fit | Compatibility with existing cloud, identity, data, security operations, procurement, residency, and deployment requirements. |
Score only capabilities demonstrated in your tests or backed by written commitments. Mark generally available features separately from previews, optional add-ons, and roadmap items. Obtain current prices, service levels, residency statements, connector availability, certifications, and contract clauses directly from each shortlisted vendor; this guide does not provide comparable pricing or independent performance benchmarks.
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7. Use a staged procurement process
Stage 1: Set non-negotiables
Translate policy and workload requirements into pass/fail gates. Examples include a required processing region, private connectivity, permission-preserving retrieval, customer-controlled keys, maximum retention, exportable audit logs, human approval for actions, or a specific incident-notification term.
Stage 2: Issue a configuration-specific questionnaire
Ask vendors to identify the exact model, service tier, region, feature flags, subprocessors, retention behavior, support access, and contract documents that answer each requirement. Require them to distinguish provider defaults from customer-configurable settings.
Stage 3: Run the same proof of concept
Use identical representative tasks, data classes, roles, adversarial cases, outage scenarios, and acceptance criteria. Keep a record of configuration, model version, prompts, index state, test data, and results so scores remain comparable.
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Estimate the people and processes needed for access reviews, evaluations, content review, incident response, index maintenance, model updates, and user training. A platform that meets technical requirements but demands unplanned manual work may not fit the operating model.
Stage 5: Contract and approve
Attach the approved architecture, data inventory, control matrix, service commitments, change-notice rules, deletion procedure, audit rights, exit assistance, and incident process to the procurement record. Obtain sign-off from business, security, privacy, legal, data, and operations owners before production use.
Stage 6: Monitor after launch
Track access anomalies, retrieval errors, unsafe outputs, policy violations, drift, latency, cost, provider changes, and incidents. Reassess the workload when its data, users, model, tools, or business impact changes. Define retirement criteria and verify deletion of prompts, indexes, fine-tuning data, logs, and credentials when the application is decommissioned.
Common buying mistakes and practical fixes
- Choosing by model leaderboard alone: Replace generic benchmarks with representative tasks, permission tests, reliability measures, and human-review effort.
- Accepting “no training on your data” as the whole data answer: Ask separately about prompts, outputs, embeddings, caches, abuse monitoring, support access, logs, regions, retention, and deletion.
- Assuming a connector preserves permissions: Test revoked access, deleted records, inherited permissions, stale indexes, and restricted citations.
- Granting agents broad service credentials: Use separate workload identities, least-privilege tools, parameter validation, transaction limits, and approval gates.
- Treating governance as a one-time approval: Maintain inventory, evaluations, monitoring, change control, incident handling, and retirement activities throughout the lifecycle.
- Relying on preview features or roadmap promises: Score only what is available and contractually committed for the intended region and service tier.
- Ignoring exit planning: Confirm export formats, index and prompt portability, deletion evidence, replacement procedures, and assistance after termination.
- Comparing different configurations: Hold model, region, retention settings, connectors, guardrails, and test data constant when comparing shortlisted options.
Questions to resolve before approval
Before signing, your review file should answer these questions for every workload:
- Which identities can invoke which models, retrieve which sources, and execute which actions?
- Where are prompts, outputs, embeddings, indexes, files, and logs processed and stored?
- What is retained, for how long, and how is deletion verified across caches, indexes, backups, and support systems?
- Can the provider, model provider, subcontractor, or support staff access content, and under what documented conditions?
- What controls prevent prompt injection, unauthorized retrieval, unsafe output, and unintended tool execution?
- What evidence will an investigator receive for a specific user, request, retrieved source, model version, policy decision, and action?
- What happens during quota exhaustion, model change, connector failure, regional outage, or a security incident?
- Which controls and data terms are contractual, which are configurable, and which are merely described in public documentation?
- How will quality, security, privacy, cost, and operational performance be measured after launch?
- How can the organization export its data and application configuration, and how will it verify deletion when the service ends?
Bottom line
Buy an enterprise AI platform only after the workload, data boundary, permissions, operating controls, and exit conditions are explicit. A defensible shortlist is built from repeatable tests and written commitments: identity and network isolation, configuration-specific data terms, permission-preserving integration, lifecycle governance, resilient operations, and a costed plan to run and replace the system. The platform that fits those requirements for your highest-value workloads is a better choice than a nominally superior model that cannot meet them.
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