Pillar
What AI compliance means in practice, the frameworks that drive it, and the operational evidence pack regulators expect.
In short
AI compliance is the continuous practice of meeting regulatory obligations on AI systems — across data protection, model risk, third-party usage, and decision transparency. It is not a one-time certification; it is a running operational layer that produces evidence whenever an examiner, internal auditor, or regulator asks.
Why it matters
AI compliance has shifted in two ways. First, regulators stopped treating AI as a frontier-research topic and started applying existing rules — ECOA, GDPR, HIPAA, SOX, SR 11-7 — to AI systems explicitly. Second, AI-specific frameworks have arrived: NIST AI RMF, ISO 42001, the EU AI Act, the NAIC Model Bulletin. Most enterprises now face overlapping obligations and need a single operational layer that satisfies all of them rather than per-framework point solutions.
The organizations that handle AI compliance well share a common trait: they treat the audit trail as something that gets produced as a side-effect of running the system, not assembled at audit time. Prism is built around this principle.
What's covered
Twelve regulatory bundles span the typical enterprise's exposure: NIST AI RMF, EU AI Act, ISO 42001, SR 11-7, NY DFS Part 500, NAIC Model Bulletin, CFPB / Reg B, HIPAA, GDPR, DORA, SOX, PCI DSS. Each has its own obligations; most overlap on the underlying evidence (logging, monitoring, audit, incident response).
An AI inventory tiered by risk; documented model development and review; fairness, accuracy, and robustness testing; ongoing monitoring with thresholds; third-party AI risk management; and an incident response process. Each maps to specific evidence the regulator expects.
Three patterns fail repeatedly: documenting policy without operational evidence; treating the audit as an annual exercise rather than continuous; concentrating compliance work in one team without engineering tooling. The first cannot be fixed at audit time. The second produces evidence gaps. The third makes per-period exports impossible.
Trace logs of every AI interaction. Five-dimension quality scoring on every output. PII redacted at ingestion before storage. Adversarial testing run on a documented cadence. Vendor AI tool register kept live. One-click evidence pack export per period. All five are operational, not policy.
Most enterprises align to one primary framework and treat others as overlay. US banks default to SR 11-7 plus NIST AI RMF. Insurers default to NAIC plus state-specific laws. Anyone selling to enterprise procurement increasingly needs ISO 42001 alignment. EU operations need EU AI Act + DORA + GDPR. Prism produces evidence against all of them from one dataset.
How Prism helps
Every LLM call captured with model, latency, cost, quality score, guardrail status.
Learn moreSensitive content scrubbed before storage; zero exposure window.
Learn moreFive-dimension LLM-as-Judge scoring with regression alerts.
Learn moreModel audits and red-teaming before models ship to production.
Learn moreBrowser-side DLP and audit log for consumer AI tool usage.
Learn moreImmutable, timestamped exports per fiscal period for examiners.
Learn moreRelated
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.
EU AI Act Compliance
The EU AI Act's high-risk category includes credit, employment, and insurance scoring. Prism is built to satisfy the logging, transparency, and oversight articles.
ISO/IEC 42001 AI Management System
ISO 42001 is the first auditable management-system standard for AI. Prism produces the operational evidence each clause asks an internal auditor to see.
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.
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.
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 governance for insurance
Insurers face NAIC's Model Bulletin and a patchwork of state-level AI rules. Prism gives carriers one platform to produce the evidence each one demands.
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.
AI compliance for asset managers
From research copilots to trading-signal models, asset managers face SEC, FINRA, and SR 11-7 expectations on AI. Prism produces the evidence per system.
AI compliance for healthcare payers
PHI cannot reach a model unscrubbed. Prism Guardrails strip 18 Safe Harbor identifiers at ingestion; Prism X blocks employees from pasting PHI into consumer AI tools.
AI Observability
Glossary entry
Shadow AI
Glossary entry
AI Guardrail
Glossary entry
Prism
PRISMtrace is the observability and governance platform for teams running LLMs and AI agents in production. Capture traces, enforce guardrails, evaluate quality, and generate compliance evidence from one platform.
LLM Observability
Structured traces give you the full story of what your AI said, why it said it, how long it took, and what it cost.
LLM Guardrails
Real-time detection and enforcement for PII, PHI, prompt injection, content policy violations, and off-topic responses, scoped per agent, per project, per knowledge base.
Prism X
Prism X enforces data loss prevention policy in the browser, before prompts and uploads reach third-party AI services. Signed policy, real-time enforcement, audit-grade events.
One SDK. Five minutes. Full audit trails, PII redaction, and guardrail enforcement, from day one.