Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
CJIS AI audit evidence should let a reviewer reproduce the population, sample selection, request decision, protected record, and retrieval result. This guide turns CJIS audit requirements into an evidence package for authenticated HTTP AI traffic while keeping IAM, endpoint, records, and incident duties with their proper owners.
Read post →AI governance for real estate should separate tenant screening, leasing communications, brokerage work, property operations, valuation, and lending. Each use needs an owner, permitted data, exact model route, review requirement, and evidence tied to the property or applicant record. Request-layer controls can govern authenticated HTTP model traffic while housing decisions, consumer-report duties, fair-housing review, and vendor-managed AI remain with their assigned owners.
Read post →AI governance for municipal government should connect every approved use to a department, public service, record system, data class, model route, and accountable official. Citywide policy supplies common rules, while service owners decide how generated work enters permitting, benefits, public safety, procurement, and resident communications. Request-layer evidence can support that program for authenticated HTTP model traffic without replacing records management or official decisions.
Read post →AI gateway latency benchmarks in 2026 report figures from microseconds to tens of milliseconds, but almost all of them measure proxy forwarding against a mock upstream, which deletes the largest real-world variable. This article defines the metrics that matter, reads the LiteLLM, Bifrost, Kong, and Portkey numbers with their methodology caveats, and shows why gateway overhead is a small fraction of provider TTFT and how to keep policy evaluation off the critical path.
Read post →AI governance for mortgage lending should connect every approved use case to a loan-stage owner, permitted borrower data, the exact model route, and review evidence. ECOA and Regulation B require specific adverse-action reasons that reflect the factors actually considered. Federal automated valuation model rules add quality controls for covered mortgage valuations. A lender also needs separate controls for vendor-managed AI and decisions that never cross an LLM gateway.
Read post →AI governance for medical devices should connect each AI function to its intended use, regulatory status, quality-system owner, change-control path, field monitoring, and retained evidence. FDA guidance on AI-enabled device software and predetermined change control plans makes lifecycle risk management concrete. EU MDR adds quality management and post-market surveillance duties. Routed LLM calls need a control record without confusing request evidence with device validation.
Read post →AI governance for media and publishing needs an editorial evidence chain that connects source material and model use to human review, publication authority, provenance, and correction records. Request-layer policy can govern authenticated HTTP model calls routed through it. Editorial judgment and rights clearance, plus archive integrity and publishing approval, remain with the newsroom or publisher.
Read post →AI governance for law firms needs matter-level approval instead of one firm-wide permission for a named model. The operating record should connect each legal use to the responsible lawyer, client terms, permitted information, model route, review duty, and retained evidence. Request controls support that program for authenticated HTTP calls routed through an enforcement point.
Read post →AI governance for higher education needs a campus operating model that separates admissions, learning assessment, research, student services, and administrative assistance. Each approved LLM route should carry an originating identity, purpose, education-record classification, and policy decision. Request-layer evidence supports the program without replacing academic judgment or institutional review.
Read post →AI governance for health payers needs distinct controls for prior authorization support and claims operations. Member service, fraud review, and internal assistance need their own controls. The program should bind each LLM request to an approved purpose and originating identity, along with its PHI class and model route, then retain evidence of the policy decision without confusing a request log with a coverage determination.
Read post →AI governance for energy and utilities needs separate approval paths for operational technology, engineering analysis, customer operations, and workforce assistants. A defensible program identifies each use case, assigns an accountable owner, constrains the data and model route, and preserves evidence of each policy decision. HTTP request controls cover one defined part of that program.
Read post →AI governance for defense contractors should decide which work may use a model before FCI or CUI enters a prompt. The operating program needs a use-case register, system-boundary decision, approved endpoint, originating identity, review owner, and assessment evidence. CMMC and NIST SP 800-171 already govern the systems that process, store, or transmit covered information; AI requests have to enter that same control record.
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