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Evaluate an enterprise AI platform against a defined workload, explicit risk tolerances, and evidence from a deployment configured like the one you plan to run. Set mandatory security and governance requirements first; then compare finalists using the same tests for quality, capacity, reliability, latency, and cost. A feature list, framework reference, or vendor assurance is not proof that a particular configuration is secure, compliant, or suitable.
Start with the system you intend to build
Before comparing platforms, write down what the AI system will do and who or what it may affect. A platform choice that fits an internal drafting assistant may not fit a system that recommends consequential actions or acts on sensitive records.
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- Business task and users: State the intended use, user groups, affected people, and decisions or actions the system can influence.
- Data and dependencies: Identify data classes, sources, retention needs, models, connected tools, retrieval sources, and third-party software or data.
- Human oversight: Specify which outputs require review, who is accountable, and what happens when a person disagrees with an output.
- Deployment context: Define the regions, network boundaries, identity systems, integrations, and operating environment the system must use.
- Failure consequences: Describe likely harms, operational disruption, and recovery needs; set risk tolerance and business requirements before scoring providers.
NIST’s AI Risk Management Framework (AI RMF) Map function calls for documenting context, tasks, methods, knowledge limits, human oversight, risk tolerances, and risks across system components, including third-party software and data. Use that framing to make the proposed workload concrete, not to imply that a framework exercise certifies a platform. NIST AI RMF Core
Use a risk framework to organize evidence
NIST’s AI RMF is voluntary guidance for managing AI risks through design, development, use, and evaluation. NIST says AI RMF 1.0 is being revised; check its current framework page for status. Its four functions are useful categories for an evaluation, not a platform certification or a mandatory procurement checklist.
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- Govern: Establish ownership, policies, risk tolerance, accountability, workforce responsibilities, and ongoing oversight.
- Map: Describe intended use, context, stakeholders and impacts, system components, limitations, dependencies, and third-party risks.
- Measure: Evaluate performance, uncertainty, privacy, bias, security, resilience, and other risks; define monitoring and evidence.
- Manage: Prioritize risks, select responses and controls, monitor them after deployment, and improve or stop the system when warranted.
The NIST AI RMF Playbook offers suggested actions rather than required steps. For generative AI, NIST’s Generative AI Profile adapts the framework to risks across the AI lifecycle. These sources can help structure questions and records; they do not establish that a vendor’s product meets your requirements.
Build a security review around verifiable controls
Ask each provider for evidence tied to the exact service, model, region, configuration, contract, and support boundary under consideration. Separate what the provider operates from what your organization must configure, monitor, or control. NIST calls for AI system security and resilience to be evaluated and documented, and addresses privacy risk and third-party components; the appropriate evidence depends on the system and its use. NIST AI RMF Core
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- Identity and administration: Request details on authentication, role separation, administrative access, tenant or project boundaries, and audit records. Verify how your intended roles and service identities will be provisioned and reviewed.
- Network and integration: Establish whether private connectivity or network isolation is available for the specific service and workflow. Ask about outbound traffic, connections to enterprise data, and the controls that prevent unintended access.
- Data handling: Confirm residency, retention and deletion behavior, encryption, customer-managed key options, provider access, and any transparency or audit mechanisms. Get the applicable terms for the exact feature rather than inferring from a general service page.
- Prompts, outputs, and monitoring: Ask what is logged, who can access logs, how abuse is monitored, whether customer data is used for training, how incidents are disclosed, and how records can support an investigation.
- AI-specific safeguards: Test protections for prompt attacks, sensitive information, and harmful or disallowed output. Document what each safeguard covers, how it can fail, and what controls remain your responsibility.
- Supply chain and change: Map model, software, tool, retrieval, and data dependencies. Ask how changes are announced, assessed, and recorded, and what incident response and recovery support applies.
Official product documentation can suggest concrete questions, but feature descriptions are not interchangeable and do not amount to independent security verification. Microsoft documents virtual network support for evaluation and private networking in Foundry-related services; AWS documents configurable Guardrails in Bedrock AgentCore policies; Google Cloud lists controls for specific Vertex AI generative AI features, including data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency. Confirm current availability and scope for the exact feature, model, and region you intend to use.
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- Microsoft Foundry evaluation with virtual networks
- Microsoft Foundry private link configuration
- AWS Bedrock AgentCore policy Guardrails
- Google Cloud generative AI security controls
Assess governance as an operating model
A platform can expose controls, but your organization still needs to decide who owns the use case, approves changes, oversees outputs, and responds when things go wrong. Put those responsibilities into the evaluation rather than treating governance as a product checkbox.
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- Assign a business owner and technical owner, and identify who approves deployment and material changes.
- Define who may access models, prompts, source data, tools, and logs, and how those permissions are reviewed.
- For outputs with meaningful consequences, document human review, escalation, appeal, and override procedures.
- Keep traceable evaluation results, risk decisions, exceptions, and incident records.
- Reassess when the model, data, prompt, tool, or workflow changes, and revisit metrics and controls as knowledge and risks evolve.
The AI RMF is intended to help organizations manage risks that could affect people, organizations, society, or the environment, and NIST describes work across design through use and evaluation. NIST AI RMF FAQs ISO/IEC 42001 has a different role: it specifies requirements and guidance for an organizational AI management system. It can inform governance planning, but neither that standard nor the AI RMF alone proves that a particular platform configuration is suitable for your use case. ISO/IEC 42001
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test scalability with your own workload
“Scalable” is not a useful pass/fail label without a workload and operating target. Build a pilot that represents expected use, including peaks and failure conditions, and evaluate quality and operations together. NIST recommends testing before deployment and regularly during operation, using conditions similar to deployment. NIST AI RMF Core
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Define the workload and service objectives
- List the model mix, request volume, expected concurrency, and peak patterns.
- Set representative input and output sizes, including context length and any retrieval or tool calls.
- Specify required regions and data constraints, latency objectives, availability needs, and acceptable degraded behavior.
- Record the quality criteria that matter for the task; a fast response is not a success if it is inaccurate or unusable.
Measure performance and operational behavior
Run the same representative test cases and traffic patterns for each finalist. Measure response quality alongside latency, throughput, queueing, rate limits, and cost under the tested usage. Exercise retries, fallback behavior, monitoring, recovery, and the operational effort needed to keep the service healthy. Record model and region choices because capacity and behavior may depend on them.
Microsoft’s rollout guidance specifically calls for planning around model, region, and capacity. Microsoft Foundry planning guidance The available documentation does not establish a neutral performance benchmark or a platform that scales best; use results from your controlled, workload-specific evaluation rather than provider claims or incomparable published figures.
Compare finalists with gates, evidence, and a repeatable pilot
First agree on mandatory requirements and scoring weights with security, risk, procurement, platform engineering, and business owners. A weighted scorecard is useful only after a candidate has cleared non-negotiable gates. The following axes can be adapted to your RFP or pilot plan.
| Evaluation axis | Evidence or test to request |
|---|---|
| Data protection and network/identity controls | Configuration-specific evidence for access, boundaries, connectivity, residency, retention, encryption, and provider access. |
| Governance, logging, and audit | Ownership and change workflows, available records, and evidence that your intended users can trace decisions and exceptions. |
| Evaluation, monitoring, and incidents | Repeatable tests for quality and risk, monitoring coverage, incident handling, and recovery procedures. |
| Model choice and change control | Supported model options for the workload, how model changes are managed, and the consequences of switching or fallback. |
| Integration and operations | Fit with identity, data, development, and operational systems; required implementation effort and support boundaries. |
| Region, capacity, reliability, latency, and cost | Results from the representative workload in the intended region and configuration, including peaks and failure behavior. |
For a proof of concept, run the same workload and acceptance tests on each finalist. Keep a record of assumptions, configuration, test data, region, model version, dates, results, and exceptions so another team can reproduce the comparison. Treat this as a buyer’s evaluation method, not a rubric required by NIST.
Turn the evaluation into an RFP or pilot checklist
Use these prompts to make responses specific enough to verify. Ask vendors to identify the exact service, feature, model, and region covered by each answer, and distinguish generally available controls from configuration choices and buyer responsibilities.
Quick Recap
- What are the mandatory use-case, data, region, network, identity, and oversight requirements, and which are pass/fail gates?
- What evidence will demonstrate each security and data-handling requirement for the proposed configuration?
- Which safeguards are relevant to the planned prompts, outputs, tools, and retrieval sources, and what limitations or failure modes should the pilot test?
- Who owns approvals, access reviews, monitoring, incident response, and reassessment after a model, data, prompt, tool, or workflow change?
- What workload, acceptance criteria, peak conditions, reliability expectations, and cost assumptions will be used for each finalist?
- What results, configurations, exceptions, and change records will be retained so the decision can be audited and repeated?
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