237 posts on industry verticals.
SEC cyber disclosure AI controls mapping should connect each Item 1.05 and Item 106 disclosure objective to an owner, control point, test, retained evidence, result, and open gap. This guide keeps legal materiality and disclosure decisions separate from the narrower incident evidence available for routed authenticated AI HTTP traffic.
Shadow AI in defense contractors can move CUI, engineering changes, proposal data, and program details into model services outside the assessed environment. This article maps the exposure to NIST SP 800-171, DFARS 252.204-7012, and CMMC, then defines what contractors can enforce and prove across authenticated HTTP AI traffic.
Shadow AI in accounting firms moves taxpayer data, payroll files, audit workpapers, and transaction details into AI services outside the firm''s approved workflow. This article maps that exposure to professional confidentiality and Internal Revenue Code Section 7216, plus the FTC Safeguards Rule, then sets out an enforceable control pattern for routed HTTP AI traffic.
This SEC cyber disclosure AI compliance checklist turns Form 8-K Item 1.05 and Regulation S-K Item 106 into eight owner-led tests. It covers incident populations, materiality escalation, filing timing, annual disclosures, third parties, and evidence, while limiting AI HTTP records to support for cyber incidents and materiality analysis.
SEC cyber disclosure AI audit evidence should connect a complete incident population to the registrant's materiality process, filing clock, annual risk disclosures, and retained source records. This guide shows where routed AI request evidence can support that chain without treating the SEC cyber rules as AI-specific requirements.
This OCC model risk AI controls mapping connects the current 2026 interagency guidance to control objectives and bank owners; repeatable tests and retained evidence; gaps and retests. It keeps the scope boundary explicit: OCC Bulletin 2026-13 excludes generative and agentic AI models, so a bank applying these disciplines to an LLM must anchor that decision in its own policy or another applicable source.
A NYDFS Part 500 AI compliance checklist grades LLM traffic against the amended 23 NYCRR 500, including the asset inventory at 500.13(a), access privileges at 500.7, monitoring of authorized user activity at 500.14(a)(1), the 72 hour incident notice at 500.17(a), and the April 15 certification at 500.17(b). Each check carries an owner, a pass condition, evidence fields, and a boundary line.
NYDFS Part 500 AI audit evidence has to support two readers: an examiner testing the monitoring duty at 500.14(a)(1) and access privileges at 500.7, and a CISO assembling a 72 hour notice under 500.17(a)(1). This guide covers population definition, sample binding, integrity testing, asset inventory alignment under 500.13(a), retention, and the April 15 certification file.
OCC model risk AI audit evidence should connect a bank-approved LLM use case to the routed request population and reviewer-selected samples, then connect model and policy changes to monitoring results, exceptions, and third-party oversight. This guide reflects OCC Bulletin 2026-13, which replaced the 2011 model-risk guidance and excludes generative and agentic AI from its direct scope.
This FFIEC AI compliance checklist turns technology-neutral examination themes into eight gradable checks for AI governance, inventory, risk, lifecycle controls, access, logging, providers, and assurance. Each check names an accountable owner, pass criteria, retained evidence, and the condition that sends the item to remediation.
This FINRA AI compliance checklist gives broker-dealers seven gradable checks for use-case scope, written supervision, communications review, books and records, vendor oversight, access and routing, and evidence testing. Every check names an owner, pass condition, evidence, and remediation trigger while preserving the firm’s responsibility for each regulatory judgment.
This GxP AI controls mapping connects FDA 21 CFR Part 11 and EU GMP Annex 11 requirements to a control point, accountable owner, repeatable test, retained evidence, explicit gap, and retest state. It separates regulated-system validation and quality decisions from the narrower routed request decision evidence an authenticated HTTP LLM gateway can produce.