An AI Governance Platform RFP Checklist

A vendor evaluation for an AI governance platform needs more structure than a general software RFP because the failure modes are different. A misconfigured identity provider causes user lockouts; a misconfigured AI governance layer causes silent cross-tenant data exposure, uncontrolled agent spend, and compliance gaps that surface during audits rather than in production monitoring. The checklist below covers the six requirement domains that matter most, with the specific questions to put to any vendor — or to your own build-vs-buy assessment.

AI governance platforms vary considerably in scope — some focus narrowly on content safety, others address the full control plane. Before sending an RFP, decide which scope you need. The sections below assume full scope — narrow them if your requirements are more focused — across six domains: identity and access management, policy and permission enforcement, guardrails and content controls, budget and spend governance, audit logging and evidence, and compliance and regulatory alignment.

Identity and Access Management

Identity is the foundation. A governance platform that cannot authoritatively answer "who is this principal and what are they allowed to do?" cannot enforce anything else reliably.

One revealing test: ask how a departing employee's access is revoked across all principal types — human accounts, API keys they created, and agents they registered. The answer should be automated and auditable. For a deeper look at automating user lifecycle management, see automating user lifecycle with SCIM 2.0.

Policy and Permission Enforcement

Policies are only useful if they are enforced at the point of action, not just stored in a configuration panel.

Are policies enforced at the control plane, or delegated to the agent itself? An agent that enforces its own restrictions is not governed — it is trusted. The governance layer must intercept and enforce independently of the agent's cooperation.

Can you express permissions at the level of individual actions, not just resources? Granular permission models (read vs. write vs. delete vs. configure) are meaningfully different from coarse role assignments. Ask for a worked example: granting an agent read access to one integration without write access to any other.

Is policy inheritance and override supported in a multi-tenant context? Enterprise deployments need organization-level baselines that teams can tighten but not loosen. Ask how conflicts between organization and team policies are resolved.

How quickly are policy changes applied? A change that requires a deployment or a long cache flush is effectively a delayed control. Enforcement should propagate within seconds.

Guardrails and Content Controls

Guardrails sit in the data path between your applications and the AI models or agents they call. The relevant questions are about coverage, latency, and configurability.

What content inspection is applied to both inputs and outputs? Unidirectional guardrails — applied only to model outputs — miss prompt injection attacks arriving in inputs. Both directions should be inspectable.

What categories of content policy can be defined? At minimum: PII detection and redaction, topic blocking, output format enforcement, and injection-pattern detection. Ask whether these are fixed rule sets or configurable per use case.

How is PII handled in logs and traces? A guardrail that redacts PII from agent responses but writes the raw unredacted content to its own audit log has solved the wrong problem. Ask explicitly where redaction is applied and whether logs themselves are scrubbed.

What is the latency impact of content inspection? Guardrails in the hot path add latency. Ask for p50 and p99 numbers at your expected request volumes, and whether inspection can be applied asynchronously for use cases that tolerate post-hoc enforcement.

Can guardrail policies be tested and versioned before deployment? A policy you cannot test in staging is a policy you cannot safely change.

Budget and Spend Governance

Uncontrolled agent spend is a real operational risk — agents running in loops, misconfigured workflows, or malicious requests can generate significant LLM API costs in minutes.

Are spend budgets enforced as hard stops, or only as alerts? An alert-only budget is not a control; it is a notification. You need the ability to halt or throttle a workflow when a budget threshold is reached, not just send an email.

At what granularity can budgets be applied? Useful granularities: per agent, per workflow, per team or cost center, per time period (daily, monthly), and per LLM provider. A single global budget limit is too coarse for most organizations.

How is spend attributed when multiple agents collaborate? A single task may invoke several agents in sequence. Ask how token consumption is attributed across that chain, and whether cost reports reflect the full chain or only the initiating agent.

Is there support for budget preview before a workflow runs? Pre-run cost estimation lets organizations gate expensive operations on human approval rather than discovering the cost after the fact.

Audit Logging and Evidence

Audit logs are the mechanism by which governance claims become verifiable. Three properties matter most: completeness, integrity, and queryability.

Is every significant action logged with a consistent structure? The categories to verify: authentication and authorization events, configuration changes, agent task execution (start, completion, failure), policy evaluation decisions, budget enforcement events, and content guardrail triggers. Ask for a sample log schema.

How is log integrity protected? An audit log that can be silently modified after the fact is not useful as evidence. Ask how the platform prevents or detects tampering — cryptographic chaining, append-only storage, and third-party export are the common approaches. See tamper-evident audit logs with cryptographic proofs for a detailed explanation of these mechanisms.

Can you query logs for a specific principal, resource, or time window? Structured queries by actor, action type, resource, and time range should be first-class. Full-text search over unstructured logs is too slow for evidence gathering under audit pressure.

What are the retention options and how is retention enforced? Documented retention policies that are not technically enforced are not controls. Ask how retention is configured and verified.

Can logs be exported to your SIEM or data lake? Verify support for your existing logging infrastructure and whether export is real-time or batch.

Compliance and Regulatory Alignment

The right frame here is not "does this platform make you compliant" — no software does that — but "does it provide the controls and evidence that support your compliance program."

Which regulatory frameworks does the platform address, and how? Common relevant frameworks: GDPR (data subject rights, erasure, processing records), EU AI Act (risk classification, transparency, human oversight), NIST AI RMF (govern, map, measure, manage), ISO/IEC 42001 (AI management systems). Ask for a controls mapping document, not a marketing claim.

Is there built-in support for GDPR data subject erasure across all data types? Erasure obligations extend to derived data — task outputs, logs, cached responses — not just primary records. Ask how the platform handles cascading erasure and what data types are in scope.

Can you classify AI systems by EU AI Act risk tier and track associated obligations? Per-system risk classification and the ability to associate conformity documentation with specific agents is a practical requirement for organizations subject to the Act.

How does the platform support evidence collection for audits? The difference between logging everything and supporting audits is the ability to answer a specific control question — "show me all access to this agent's configuration over the last 90 days" — without manual data archaeology.

How to use this checklist

Work through each domain in order — identity first, because everything else depends on it. For each requirement, note whether the vendor addresses it today, has it on a documented roadmap, or does not address it. Weight by your risk profile: healthcare teams often prioritize GDPR erasure and audit evidence; financial services firms tend to weight budget controls and RBAC granularity; technology companies deploying agents for customers care most about multi-tenancy and policy inheritance.

The goal is to understand, before you commit, exactly where each capability sits on the spectrum from "enforced by the platform" to "your responsibility to configure" to "not in scope."

Common questions

How is a governance platform different from just adding more logging to our existing stack?

Logging captures what happened. Governance enforces what is allowed to happen before it occurs. A governance platform adds the enforcement layer — policy evaluation, guardrail interception, budget enforcement, identity verification — that logging alone cannot provide. Logs tell you a policy was violated after the fact; a governance platform prevents or stops the violation. Most mature programs need both: enforcement controls plus the audit record that demonstrates the controls are operating.

Does Praesidia address all of these requirement categories?

Praesidia is designed as a full-scope AI control plane — covering identity (SSO/SCIM/MFA/RBAC for users, agents, and MCP servers), policy and permission enforcement, content guardrails, budget controls, cryptographically protected audit logging, and compliance alignment for GDPR and EU AI Act requirements. The platform documentation maps these capabilities to specific requirement categories. As with any vendor evaluation, verify the capabilities that matter most to your use case rather than relying on category claims.

Should we run a proof-of-concept before finalizing vendor selection?

For a governance platform, a proof-of-concept is especially valuable because critical capabilities — policy enforcement latency, guardrail accuracy, log completeness — are difficult to verify from documentation alone. Design the PoC around your actual failure modes: a workflow that a budget limit should stop, a content policy that should detect PII in an agent output, a SCIM deprovisioning event that should revoke all associated access. To understand which gaps to prioritize before you begin, the AI agent governance maturity model provides a sequenced framework for identifying where your program currently stands.