AI SOC Platform RFP Checklist for Security Teams and MSSPs

Reviewed by ContraForce team · Updated 2026-09-04

An AI SOC platform RFP should test production behavior, not presentation quality. Require each vendor to demonstrate evidence handling, tenant isolation, action authority, failure behavior, and a reproducible operating outcome using the same scenarios.

1. Scope and definitions

• Define whether the product is an assistant, AI SOC analyst, automation platform, agent platform, MDR service, or delivery platform.
• List every workflow stage the product supports: triage, investigation, response, ticketing, reporting, and tuning.
• Define what “autonomous,” “resolved,” “closed,” and “response time” mean.
• Identify which capabilities are generally available, preview, roadmap, or partner-delivered.

2. Evidence and investigation

• Which data sources can the agent query?
• Does the final verdict include the evidence used?
• Can an operator see failed queries, missing evidence, and contradictory observations?
• How does the system avoid unsupported conclusions?
• Can investigation requirements differ by incident class and customer?

3. Actions and human control

• Separate read, recommend, approve, and execute permissions.
• List every supported response action by integration.
• Demonstrate an approval gate for a consequential action.
• Explain how confidence thresholds relate to action authority.
• Show what happens when an approver does not respond.
• Preserve the identity and time of every approval.

4. Multitenancy

• Demonstrate two customer tenants with different procedures and permissions.
• Explain how credentials, prompts, retrieved context, and evidence remain isolated.
• Show customer-specific service boards, SLAs, and reports.
• Describe how shared procedures receive customer exceptions without uncontrolled forks.
• Explain offboarding and access revocation.

5. Integrations

• Identify whether each integration is read-only, bidirectional, or action-capable.
• Document required roles and delegated access.
• State API-rate, data-volume, and event-delay constraints.
• Show retry, deduplication, and dead-letter behavior.
• Identify who maintains the connector when an upstream API changes.

6. Security and data architecture

• Where are prompts, evidence, logs, credentials, and model inputs stored?
• Is customer telemetry copied into a vendor-controlled data lake?
• Which model providers process data and under what retention terms?
• How are secrets stored and rotated?
• What audit certifications and penetration-test evidence are available?
• Can the platform support regional or customer-specific data requirements?

7. Model governance

• Which decisions use deterministic logic versus a model?
• How are model and prompt versions tracked?
• Can a customer pin or review a model change?
• How are evaluations constructed and regression-tested?
• What happens when the model is unavailable?
• Are outputs used for model training, and can customers opt out?

8. Service workflow

• Does the product create a customer-ready ticket or only an investigation summary?
• Can it preserve internal versus external notes?
• Does it update reports and service metrics?
• Can it feed incident classification into detection tuning?
• How are exceptions assigned and escalated?

9. Commercial model

• Identify platform, workspace, user, data, model, and per-incident charges.
• Define what counts as a billable incident or run.
• Model cost under quiet, typical, and surge volumes.
• State minimum terms, overages, implementation fees, and exit costs.
• Explain whether customers can export their procedures and evidence.

10. Performance claims

• Require the event boundaries, population, measurement window, exclusions, and statistic type for every claim.
• Distinguish vendor telemetry, customer-reported outcomes, independent testing, and modeled estimates.
• Ask for median and percentile values, not only a mean or best case.
• Reproduce key claims during the proof of value.

Required proof-of-value scenarios

• Routine benign incident with sufficient evidence
• Confirmed threat requiring a human approval
• Ambiguous incident with missing evidence
• Upstream API or credential failure
• Duplicate or updated incident event
• Two tenants with different action authority
• Ticket synchronization and customer-ready closure