AI risk management for Chief Risk Officers

AI Model Audits

Model audits give you a structured review of model behavior, risk profile, and readiness for production, before deployment, not after incidents.

AI Red Teaming

Structured adversarial testing to find prompt injection vulnerabilities, guardrail bypasses, and unsafe behaviors, before they reach production.

LLM Evaluations

Define quality rubrics, score every interaction, and catch regressions before users do, with automated evaluators that run on every trace or on a schedule you control.

Model Risk Management for AI — SR 11-7 Revised Guidance

The interagency Revised Guidance on Model Risk Management supersedes SR 11-7 and SR 21-8. The three-pillar discipline carries forward, scaled to each bank's model risk profile. Prism produces the evidence at every tier.

NIST AI Risk Management Framework: Prism Compliance Mapping

Each NIST AI RMF function has subcategories that demand evidence. Prism produces it: from MEASURE-2.7 trace logs to MANAGE-2.1 adversarial test results.

NAIC AI Model Governance

The NAIC Model Bulletin sets expectations for insurer use of AI. Prism produces the documentation, monitoring, and audit artifacts each pillar requires.

Prism for compliance officers

Designed for compliance reviewers, not engineers. Sessions render as readable transcripts. Evidence packs export with one click. Frameworks map to Prism out of the box.

Prism for CISOs

AI is now an attack surface and a data-egress channel. Prism Guardrails handle application-side risk. Prism X handles browser-side employee risk.