Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Shadow AI in capital markets can move MNPI and draft research into unapproved model services while leaving the firm without a supervisory record. This article maps the traffic to FINRA''s technology-neutral rules and SEC books-and-records duties, then defines the controls required at the managed HTTP AI boundary.
Read post →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.
Read post →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.
Read post →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.
Read post →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.
Read post →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.
Read post →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.
Read post →Utah SB 149 created the Artificial Intelligence Policy Act in 2024, and SB 226 moved the consumer-protection rules into Chapter 75 in 2025. This guide builds an audit-evidence package around the current disclosure triggers, high-risk regulated-service rule, safe harbor, consumer-protection liability and Division enforcement. It separates statutory proof from useful HTTP operating evidence and keeps notice delivery with the application.
Read post →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.
Read post →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.
Read post →Noma Security maps AI posture across models, agents, MCP servers, datasets, and notebooks. DeepInspect enforces identity-bound policy on live HTTP LLM requests and records each decision, separating discovery and configuration work from access control and audit evidence.
Harmonic Security protects browser and desktop AI use, while DeepInspect applies identity-bound policy to LLM API requests. Compare the traffic each product can control, the evidence it produces, and the coverage gap that remains when a service calls a model outside a browser.