AI compliance for asset managers

AI Model Audits

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

Agent Observability

Trajectory evaluation decomposes multi-step agent runs into ordered steps and scores each run on goal adherence, tool compliance, efficiency, and safety, automatically on ingest.

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.

SOX AI Controls and Financial Reporting Models

AI involved in earnings, forecasting, or estimation is in scope for Section 404. Prism produces the change-management and operating-effectiveness evidence external auditors expect.

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.

AI compliance and risk management for banks

From underwriting copilots to fraud-screening agents, banks need the same model risk discipline they have for traditional models. Prism is built around it.

AI risk management for fintech

Sponsor banks expect SR 11-7 hygiene. CFPB and state AGs care about ECOA. Prism makes both legible without slowing your release pace.