Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Shadow AI in clinical research can move participant narratives, blinded treatment information, protocol text, and unreleased study results into model services outside sponsor and site controls. This article follows the unauthorized HTTP request path across sponsor, CRO, and site workflows, then defines the identity, trial context, content class, destination, and decision evidence needed before transmission.
Read post →Shadow AI in behavioral health can send psychotherapy notes, intake narratives, substance use disorder records, and crisis details to model services outside approved clinical workflows. This article isolates the unauthorized HTTP request path, shows where HIPAA and 42 CFR Part 2 classifications disappear, and defines request evidence for traffic routed through an authenticated AI enforcement point.
Read post →Shadow AI for grid operators can move outage narratives, asset identifiers, substation notes, network diagrams, and maintenance findings into model services outside approved utility routes. This article connects NERC CIP information-protection and supply-chain concerns to authenticated HTTP AI traffic, while keeping operational technology, local models, and safety decisions outside the DeepInspect boundary.
Read post →Shadow AI in real estate can move tenant applications, screening reports, owner records, access instructions, transaction documents, and wire details into unapproved model services. This article maps those HTTP requests to fair-housing and consumer-report workflows, then defines the identity, property context, content policy, and evidence needed for managed AI routes.
Read post →Shadow AI in oil and gas can move SCADA context, controller handovers, alarm details, well and pipeline data, maintenance findings, and emergency procedures into unapproved model services. This article isolates the unauthorized HTTP request path, uses NIST OT guidance and PHMSA control-room rules to identify sensitive operating context, and defines what inline policy can enforce without claiming control over OT protocols or field actions.
Read post →Shadow AI in medical-device companies can move design records, complaint narratives, test failures, cybersecurity findings, and patient-linked service data into model services outside approved quality and supplier controls. This article isolates that unauthorized request path, maps it to FDA quality-system and cybersecurity expectations, and defines an enforceable boundary for authenticated HTTP AI traffic without treating a gateway record as device validation.
Read post →Shadow AI in K-12 education can move student work, individualized education program details, behavior notes, assessment data, and parent communications into unapproved LLM services. This article maps FERPA and COPPA to those request paths and defines enforceable controls for authenticated HTTP AI traffic while preserving district ownership of consent and instruction.
Read post →Shadow AI in higher education can move education records and research material, financial-aid data and donor records, plus employment files into unapproved LLM services. This article defines the campus-specific data paths, maps FERPA and applicable safeguards duties to those paths, and sets out request-level controls for authenticated HTTP AI traffic.
Read post →Compare eight Vercel AI Gateway alternatives by deployment: self-hosted routing, VPC controls, hosted routing, and identity-bound audit evidence. The guide separates routers, API platforms, and enforcement layers so teams outside Vercel can select the constraint they need to solve.
This OCC model risk AI compliance checklist gives banks eight gradable checks for current-source scope, governed use cases, inventory, validation, monitoring, changes, third parties, and routed LLM evidence. It preserves the decisive limit in OCC Bulletin 2026-13: generative AI and agentic AI sit outside the revised guidance, so any use of its disciplines for those systems must come from bank policy.
Read post →FDA CDRH has opened a discussion about risk assessment, premarket evaluation, and postmarket monitoring for GenAI-enabled medical devices. The paper creates no guidance or policy change. It does expose the lifecycle evidence problem: manufacturers need records that connect the deployed device configuration and real-world interaction to clinical evaluation, monitoring, change control, and safety decisions.
Read post →Shadow AI in credit unions can place member records, loan files, fraud cases, and call-center transcripts into AI services outside approved vendor oversight. This article maps the exposure to NCUA Part 748 safeguards, the agency's AI supervision guidance, and third-party risk duties, then defines enforceable controls for managed HTTP AI traffic.
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