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Start with a comparable decision, not a platform checklist
Enterprise AI platforms bundle model access, retrieval, vector search, agent tooling, networking, identity, logging, safety controls, and operations. Those components do not necessarily inherit the same protections. A platform may offer customer-managed encryption keys for one service while a related feature has different commitments, regions, or configuration requirements.
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Write a one-page workload definition before asking suppliers for scores. Include:
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- Data classification: public, internal, confidential, regulated, or a more granular scheme used by your organization.
- Model path: pretrained model, fine-tuned model, retrieval-augmented generation (RAG), agent workflow, or a combination.
- Criticality: what happens if the system is unavailable, produces a wrong answer, exposes data, or takes an unauthorized action.
- Geography and residency: approved processing and storage regions, cross-border restrictions, and disaster-recovery locations.
- Service targets: expected requests per minute, peak concurrency, latency, availability, retention, and recovery objectives.
- Governance: who approves the model, owns the data, operates controls, reviews incidents, and accepts residual risk.
AWS Prescriptive Guidance captures the reason for this discipline: “A robust security and governance framework is essential for scaling generative AI adoption across the enterprise.” Treat that sentence as a design principle, not as evidence that any particular deployment is secure by default.
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Compare security in seven control areas
For each control, record the exact service or component, supported region, configuration dependency, evidence supplied, operating owner, and result from your workload test. A feature listed in a product overview is not proof that it applies to every model, endpoint, agent, or data path.
1. Identity, roles, and least privilege
Map every human and machine identity that can discover models, invoke them, change guardrails, read prompts or outputs, export logs, alter network settings, or approve production deployment. Separate evaluation, general use, fine-tuning, and specialized-model duties. Use short-lived administrative access where possible and require an approval trail for privilege elevation.
AWS guidance recommends granular IAM roles for evaluation, general use, fine-tuned models, and specialized models. Microsoft Azure guidance calls for least-privilege role-based access control (RBAC), managed identities, and just-in-time administrative access. These are recommended patterns; they still require correct policy design, separation of duties, and periodic access reviews in your tenant.
- Can a developer invoke an approved model without being able to change its safety policy?
- Can a data scientist fine-tune a model without reading production customer records?
- Can an operations engineer view health metrics without viewing sensitive prompt content?
- Are service identities scoped to one application, environment, and data store?
2. Private connectivity and network boundaries
Determine whether traffic can stay on private links from your application network to the model or retrieval service. Document ingress and egress paths, DNS, firewall rules, proxy inspection, administrative access, and any unavoidable public control-plane calls.
AWS describes private model access using AWS PrivateLink. Microsoft describes network isolation and private endpoints. Ask each supplier to identify which specific model APIs, agent components, vector stores, and management operations support private access in your required region. A private endpoint does not automatically prevent data exfiltration by an over-permissioned workload, so pair it with egress policies, identity controls, and logging.
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3. Data location, retention, and encryption
Separate three questions that are often conflated: where data is processed, where it is stored, and how long copies remain in prompts, outputs, caches, backups, indexes, and logs. Obtain written terms for provider use of customer content, training, abuse review, support access, and deletion.
Check encryption in transit and at rest, key ownership, rotation, revocation behavior, and recovery when a key is disabled. If your policy requires customer-managed encryption keys, verify the exact service and storage layer rather than assuming the platform-wide statement applies to every component.
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Google’s machine-learning security table illustrates why feature-level checking matters. It lists data residency, customer-managed encryption keys, VPC Service Controls, and Access Transparency for Gemini Enterprise Agent Platform, while listing fewer controls for RAG Engine and Vector Search. The same documentation states that Vertex AI Feature Store and Vertex Data Labeling do not meet data-at-rest commitments. The page was last updated 2026-08-25 UTC. Treat those distinctions as architecture constraints, not footnotes.
4. Model eligibility and deployment guardrails
Define which models may process each data class and which capabilities are prohibited. Your allow-list should cover model family, version, region, fine-tuning status, tool access, maximum context, and whether outputs can trigger external actions.
Guardrails should address harmful content, prompt injection, sensitive-data leakage, unsafe tool calls, and policy-specific terms. AWS documents Bedrock Guardrails as one control option. Microsoft guidance refers to deployment guardrails. Test controls with adversarial prompts, indirect instructions in retrieved documents, multilingual inputs, and attempts to make the model reveal system prompts or secrets.
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Record what happens when a guardrail blocks content: is the event logged, is a safe response returned, can an authorized reviewer inspect it, and can the application distinguish a policy block from a service outage? A guardrail that is available but disabled, bypassed by a second endpoint, or omitted from an agent tool path does not protect the workload.
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5. Monitoring, logging, and audit evidence
Build an evidence trail that can answer who invoked which model, with what application identity, in which region, under which policy version, and what decision followed. Decide whether prompts and outputs may be logged; if not, log hashes, classifications, metadata, and redacted samples instead.
AWS examples include invocation logs stored in Amazon S3 or CloudWatch Logs and API auditing through CloudTrail. Microsoft guidance includes Defender for Cloud threat monitoring. Verify retention, immutability, time synchronization, access to logs, alert latency, and the ability to export records for an investigation or regulator.
- Test detection of unusual volume, privilege changes, disabled guardrails, suspicious tool calls, and repeated policy violations.
- Confirm that logs cover failed requests and blocked requests, not only successful completions.
- Assign an owner for triage, escalation, evidence preservation, and customer notification.
6. Application and data-layer isolation
Platform controls cannot compensate for a shared application that sends one tenant’s documents to another tenant’s retrieval index. Test tenant isolation, metadata filters, row-level permissions, deletion propagation, and index rebuild procedures. Check whether embeddings, cached context, and evaluation datasets inherit the source record’s classification.
For RAG systems, require an authorization check before retrieval and again before displaying a citation or taking an action. For agents, constrain tools to an explicit allow-list, validate arguments server-side, and require human approval for irreversible operations.
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7. Resilience and recovery
Security includes continued operation and safe failure. Document quotas, throttling behavior, regional outages, model retirement notices, dependency failures, backup restoration, and rollback to a known-good model or prompt policy. Test a provider outage and a revoked encryption key, not only a normal completion.
Governance is an operating process, not a product toggle
Governance determines who may use AI, for what purpose, with which evidence and escalation path. Create a register for every production and pilot system containing the owner, purpose, data classes, model and version, region, tools, vendors, risk rating, approval date, monitoring plan, and retirement trigger.
Use a documented risk workflow
Microsoft’s AI governance guidance aligns its process with the NIST AI Risk Management Framework (AI RMF) and its Playbook. Example risks include data-protection noncompliance, lack of transparency, inaccurate predictions, adversarial attacks, insider threats, unexpected costs, and underutilization. Example mitigations include compliance assessment, model documentation, operational-metric monitoring, red-team testing, role-based access, and resource-use tracking.
NIST describes AI RMF as voluntary, usable by designers, developers, users, and evaluators, and scalable across organization sizes, sectors, and borders. It is not a certification or a mandatory legal requirement merely because you adopt it. NIST also notes that trustworthiness characteristics involve tradeoffs and can matter differently by context. Your review should therefore state which qualities—such as validity, safety, security, resilience, accountability, transparency, explainability, privacy, and fairness—are material for the particular use case and how conflicts are resolved.
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Set approval gates
- Intake: describe the user outcome, data, model, tools, regions, and business owner.
- Classification: assign data sensitivity and impact levels; prohibit unapproved data classes until controls are proven.
- Design review: verify identity, private connectivity, encryption, guardrails, isolation, logging, and human oversight.
- Evaluation: run accuracy, robustness, bias, prompt-injection, data-leakage, and cost tests using representative samples.
- Approval: record residual risks, compensating controls, accountable owners, and an expiration or re-review date.
- Production monitoring: alert on quality drift, policy violations, latency, availability, spend, and abnormal access.
- Change control: reassess model versions, prompts, retrieval indexes, tool permissions, regions, and provider terms before release.
- Retirement: archive required evidence, delete data and indexes according to policy, revoke credentials, and document rollback or replacement.
Plan for lifecycle and continuity
Model APIs change. Include a model-eligibility policy, deprecation notice period, compatibility tests, rollback artifacts, and a second approved path for critical workloads. Define who can freeze a deployment when monitoring detects unsafe behavior. Microsoft guidance also recommends lifecycle policy, model retirement and rollback planning, continuity, monitoring, and alerting; implement those controls in your operating procedures rather than treating them as optional platform features.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare cost using a normalized workload
Do not rank platforms from a single per-token figure. Total cost includes inference, storage, networking, logging, safety and evaluation runs, retrieval or vector services, gateways, support, engineering labor, and the cost of meeting availability and recovery targets. A lower model rate can be outweighed by verbose prompts, high output limits, duplicated logs, or a separate data path.
Write the pricing assumptions first
| Assumption | What to specify | Why it changes the bill |
|---|---|---|
| Region and currency | Processing and storage regions, billing currency, and tax treatment | Rates, residency architecture, and cross-region transfer can differ |
| Model and version | Exact model, context limit, modality, and fallback model | Capabilities and unit rates vary by model and version |
| Traffic | Requests per minute, daily volume, peak concurrency, and seasonality | Determines capacity, throttling, and possible reserved or provisioned resources |
| Token shape | Average and p95 prompt tokens, retrieved context, output tokens, and maximum completion | Long context and generous output limits increase consumption |
| Data services | Index size, reads and writes, storage class, backup retention, and rebuild frequency | RAG and vector components add non-model charges |
| Operations | Log volume, retention, evaluations, red-team runs, alerting, and support tier | Observability and assurance are recurring operating costs |
| Availability | Service-level target, multi-region design, recovery point and recovery time objectives | Redundancy and failover add infrastructure and transfer costs |
No workload-normalized price comparison for AWS, Microsoft, and Google was established in the available official guidance, so a “cheapest platform” conclusion would be unsupported. Request a quote for identical assumptions and require suppliers to separate one-time migration work from recurring usage.
Control consumption without damaging quality
Microsoft guidance recommends maximum tokens and maximum completions for pay-per-use services, batching where appropriate, concise prompts, and gateway controls. Apply these measures deliberately:
- Set a maximum output based on the task, then measure truncation and rework.
- Remove repeated instructions and irrelevant retrieval context; preserve the policy text the model actually needs.
- Batch offline classification or embedding jobs when latency is not user-facing.
- Use a gateway to meter tokens by team and application, throttle bursts, trip circuit breakers, and route eligible requests to lower-cost endpoints.
- Alert on spend rate, anomalous token growth, repeated retries, and idle provisioned capacity.
- Price evaluation, safety testing, and failure retries; they are part of operating the system.
Measure cost per successful business outcome, not only cost per request. If a shorter answer creates manual rework or a cheaper model increases escalation, the apparent saving is not real.
Build a scorecard that produces evidence
Use one row per requirement and do not award a point until the control is confirmed for the intended architecture.
| Scorecard field | Entry to capture |
|---|---|
| Control or requirement | For example, private model invocation, customer-managed key, or blocked tool call |
| Exact component | Model API, agent runtime, RAG engine, vector store, logging service, or management plane |
| Region and configuration | Supported geography, endpoint type, key mode, retention setting, and dependencies |
| Evidence | Official documentation, test record, audit artifact, or contractual commitment |
| Operating owner | Named team, review frequency, escalation path, and required runbook |
| Workload result | Pass, conditional pass, or fail with test data and residual risk |
| Cost assumption | Volume, token shape, storage, network, logging, evaluation, and availability inputs |
Use a common test pack: normal requests, sensitive records, malicious retrieval content, prompt injection, unauthorized tool attempts, deleted documents, regional failure, quota exhaustion, and model-version change. Keep the prompts, datasets, thresholds, and scoring rubric identical across candidates.
Questions to resolve with each supplier
- Which exact services support the required region, private connectivity, customer-managed keys, residency, and audit export?
- Are prompts, outputs, embeddings, fine-tuning files, and logs used for provider training or retained for abuse review?
- What are the default and maximum retention periods, and how is deletion verified across backups and indexes?
- Which models and features are eligible for regulated or confidential data, and what contractual commitments apply?
- How are guardrail policies versioned, tested, bypass attempts detected, and rolled back?
- What notice is provided for model retirement, price changes, quota changes, or regional availability changes?
- Which logs are available to the customer, in what format, with what retention and tamper protection?
- How are outages, throttling, safety blocks, and provider-side incidents communicated and escalated?
- Can the service export prompts, configurations, evaluations, indexes, and policy artifacts for migration?
- What assumptions are included in the quote for inference, storage, networking, logging, safety evaluation, support, and high availability?
Common comparison mistakes
- Scoring the logo: a platform-level security label can hide different controls for each component.
- Confusing availability with configuration: a private endpoint or key feature may require a particular region, tier, or deployment mode.
- Ignoring the application: weak tenant isolation, broad tool permissions, or unfiltered retrieval can defeat strong provider controls.
- Counting tokens only: data stores, logs, gateways, evaluations, engineering, and redundancy affect total cost.
- Calling a framework a certification: NIST AI RMF is voluntary guidance, not a certificate of compliance.
- Testing only happy paths: security and governance failures appear during prompt injection, privilege changes, deletion, outages, and model updates.
- Using stale documentation: record access dates and revalidate claims when a model, region, or service changes.
A practical decision rule
Choose the platform and service combination that meets every non-negotiable control for the defined workload, produces verifiable evidence, and remains operable through model changes and outages. Among solutions that pass those gates, compare measured quality, latency, resilience, staff effort, and normalized total cost. If a control is “available” but cannot be configured in the required region, cannot be evidenced contractually, or fails the common test, mark it conditional rather than awarding credit.
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