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Evaluate enterprise AI platforms against a defined workload and documented acceptance criteria—not a vendor feature list. Start by specifying the system you intend to deploy, set non-negotiable security and governance requirements, then test finalists with the same representative workload. Controls and capacity depend on the exact product, feature, configuration, region, and organizational context; a platform description alone does not establish that your deployment is secure, compliant, or scalable.
Define the AI system before comparing platforms
“Enterprise AI platform” can refer to a model service, development environment, managed agent, retrieval system, or a combination of components. Establish what you are evaluating by documenting the intended system and use case first. NIST’s AI Risk Management Framework (AI RMF) recommends mapping the business context, system tasks, limitations, human oversight, and risks across system components, including third-party software and data. NIST AI RMF Core
- Business task and users: Identify what the system will do, who will use it, and who may be affected by its outputs.
- Data and decisions: List the data classes it can access, the decisions or actions it may influence, and the consequences of incorrect, incomplete, or unavailable output.
- System components: Record the model types, prompts, retrieval sources, connected tools, integrations, and deployment settings in scope.
- Human oversight: Specify when a person must review an output or approve an action, and what that reviewer needs to make a meaningful decision.
- Operating conditions: Set region and data constraints, expected usage patterns, latency and availability objectives, and recovery expectations.
- Risk tolerance: Agree in advance which failures are unacceptable, which can be mitigated, and who has authority to accept residual risk.
This description becomes the shared basis for security review, pilot design, and procurement requirements. Without it, candidates may be assessed against different assumptions and their results will not be meaningfully comparable.
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NIST’s AI RMF is voluntary guidance for incorporating trustworthiness into the design, development, use, and evaluation of AI systems. NIST says AI RMF 1.0 is being revised; check its current status before relying on a particular edition. Its four functions provide a useful structure for organizing evidence, not a platform certification or mandatory procurement checklist. The Playbook offers suggested actions. NIST AI RMF FAQs · NIST AI RMF Playbook
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- Govern: Define ownership, policies, risk tolerance, accountability, workforce responsibilities, and ongoing oversight.
- Map: Document intended use, context, stakeholders and impacts, system components, limitations, dependencies, and third-party risks.
- Measure: Test and evaluate performance, uncertainty, security and resilience, privacy, bias, and other risks; establish monitoring.
- Manage: Prioritize risks, select responses and controls, monitor the deployed system, and improve or stop it when warranted.
For generative AI, NIST’s AI RMF Generative AI Profile adapts the framework to generative AI risks across the AI lifecycle. NIST AI RMF Generative AI Profile
Assess security using deployment-specific evidence
Ask each provider for documentation and configuration evidence that matches the precise service, feature, model, region, and deployment you plan to use. NIST calls for AI system security and resilience to be evaluated and documented, and addresses privacy risk and third-party components. Use those principles to shape the review; they do not prescribe one control set for every system. NIST AI RMF Core
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- Identity and isolation: Request details on authentication, role separation, administrative access, and boundaries between tenants, projects, or workloads.
- Network paths: Establish whether private connectivity is available for the exact features in scope, how traffic is isolated, what outbound connections are possible, and how the system reaches enterprise data.
- Data handling: Confirm residency, retention and deletion behavior, encryption, customer-managed key support, and what provider personnel can access or make visible to customers.
- Logging and data use: Ask what prompts, responses, and administrative events are logged; whether data is used for training; how abuse is monitored; and what audit records and incident disclosures are available.
- AI-specific safeguards: Evaluate protections for prompt attacks, sensitive information, and harmful or disallowed output. Ask for limitations, configuration dependencies, and known failure modes—not just a list of safeguards.
- Supply chain: Inventory dependencies involving software, models, tools, retrieval systems, and data, and establish how changes to them are communicated and reviewed.
- Response and recovery: Document incident escalation, recovery responsibilities, service restoration expectations, and change notification processes.
Official product documentation can help identify controls to verify, but examples are not a comparative security assessment. Microsoft documents virtual network support for evaluation and private networking; AWS documents configurable Guardrails for requests and responses; and Google Cloud lists controls such as data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency for specific Vertex AI generative AI features. Confirm current scope and availability for the exact service, model, feature, region, and configuration. Microsoft Foundry evaluation virtual network documentation · Microsoft Foundry private link documentation · AWS Bedrock AgentCore policy guardrails documentation · Google Cloud Vertex AI generative AI security controls
Check governance as an operating model
A platform can provide technical controls, but your organization still needs to decide how AI use is authorized, supervised, and reassessed. Specify who owns the use case, approves changes, manages access to models and data, reviews high-impact outputs, records risks, and handles incidents, appeals, or user feedback. Define who can pause or retire the system if its risks become unacceptable.
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Require traceable evaluations and a reassessment process when the model, data, prompt, tool, or workflow changes. NIST’s framework spans AI design through use and testing, and its guidance calls for metrics and control effectiveness to be revisited as knowledge and risks evolve. NIST AI RMF FAQs · NIST AI RMF Core
Keep organizational management-system requirements separate from product-level controls. ISO/IEC 42001 specifies requirements and guidance for an AI management system within an organization. It is a governance reference, not proof that a particular platform is suitable for your use case. ISO/IEC 42001
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Test scalability with a representative workload
Scalability is not a single capacity figure. It depends on the workload, model and region choices, service limits, reliability needs, and the behavior of the complete system under load. NIST recommends evaluating performance under conditions similar to deployment; Microsoft’s rollout guidance includes planning for models, regions, and capacity. NIST AI RMF Core · Microsoft Foundry planning guidance
For each pilot, write down the expected request volume and concurrency, peak patterns, model mix, context and output sizes, region and data constraints, latency objectives, availability requirements, and what the system should do when a dependency fails. Then measure the same workload on each finalist. Track:
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- Output quality against use-case-specific acceptance tests, alongside latency.
- Throughput, queueing, and behavior at expected peaks.
- Applicable rate limits and the effect of retries, timeouts, or fallback paths.
- Cost under the representative usage pattern and the operational effort required to run it.
- Monitoring, degradation, recovery, and failure behavior.
Test before deployment and regularly during operation; a successful pilot does not establish future performance if the workload, model, configuration, or operating conditions change. The available documentation does not provide a neutral benchmark that establishes one platform as the scalability winner.
Compare candidates with gates, evidence, and a shared pilot
Set mandatory requirements before scoring preferences. Have security, risk, procurement, platform engineering, and business owners agree on the gates and weights so that a high score in one area cannot conceal a failure in a non-negotiable area.
- Data protection, network isolation, and identity controls.
- Governance evidence, logging, and audit support.
- Evaluation, red-teaming, monitoring, and incident workflows.
- Model choice, portability, and change control.
- Integration with data, identity, development, and operations.
- Region coverage, capacity, reliability, latency, and recovery.
- Implementation effort, support boundaries, and total operating cost.
Run the same representative workload and acceptance tests on every finalist. Record assumptions, configuration, test data, region, model version, test date, and exceptions so the results can be reproduced. This is a practical procurement approach informed by NIST’s testing and documentation guidance, not a NIST-mandated rubric. NIST AI RMF Core
What to put in the RFP or pilot plan
Use the following requests to turn broad assurances into items your team can verify:
Quick Recap
- Identify the exact service, features, models, regions, and deployment configuration proposed for the workload.
- Provide current documentation for identity, network, data handling, logging, safeguards, and third-party dependencies relevant to that configuration.
- State the evidence available for retention and deletion, training use, provider access, incident disclosure, audit records, and change notification.
- Describe control limitations, dependencies, failure modes, and any customer responsibilities needed to make the controls effective.
- Provide the capacity, service-limit, region, support, and recovery information needed to plan the stated workload.
- Agree on shared pilot inputs, acceptance tests, measurements, failure scenarios, and result-recording requirements.
- Document ownership, approval and oversight responsibilities, reassessment triggers, and the process for pausing or changing the system.
- List exceptions, unresolved risks, assumptions, implementation work, and operating costs separately from mandatory requirements.
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