Blog

Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.

AI Vendor Risk in Clinical Research Starts With the Trial Data Flow

FDA guidance expects sponsors to document electronic systems, data flows, access controls, contracts, audit trails, and service-provider responsibilities across a clinical investigation. An AI vendor that receives protocol text or participant records becomes part of that evidence chain. This article maps vendor review onto each authenticated model request.

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AI Vendor Risk in Aerospace Follows the Contract Data

FAR 52.204-21 requires basic safeguarding wherever Federal Contract Information resides or transits and flows the clause into relevant subcontracts. Aerospace teams using external AI providers create another processing route for contract data. This article shows how vendor diligence becomes request-level authorization and evidence.

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AI Vendor Risk for Defense Contractors Starts at the CUI Route

DFARS 252.204-7012 requires an external cloud provider that stores, processes, or transmits covered defense information to meet FedRAMP Moderate-equivalent security and support incident obligations. CMMC also brings external service providers into the assessment scope when their assets handle CUI or security protection data. This article maps those duties onto authenticated model traffic.

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AI Vendor Risk for Credit Unions Is a Per-Request Control Problem

NCUA rules require credit unions to perform service-provider diligence, contract for appropriate safeguards, monitor providers when risk calls for it, and adjust their security programme as technology and outsourcing change. AI usage adds a member-data route that annual reviews cannot see. This article maps NCUA expectations onto the authenticated model request.

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AI Vendor Risk in Capital Markets Requires Runtime Supervision

FINRA says its rules apply when a member firm uses generative AI directly, through a third party, or through an embedded feature. Regulation S-P adds incident-response and customer-notice duties for covered institutions. A capital-markets vendor assessment therefore needs to connect approved use, customer-information handling, supervision, and model routes to evidence from each authenticated request.

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AI Vendor Risk in Biotech Extends the Validated System Boundary

A biotech AI vendor can create, modify, or transmit an electronic record used under an FDA predicate rule. When that happens, vendor review has to test validation evidence, access controls, audit trails, record retrieval, and change handling under 21 CFR Part 11. The operational proof lives in the authenticated model request as well as the supplier file.

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AI Vendor Risk in Behavioral Health Starts With the Treatment Record

A behavioral health AI vendor can receive psychotherapy notes, substance use disorder records, or other ePHI inside an authenticated model request. HIPAA requires written assurances and ongoing activity review, while 42 CFR Part 2 adds protections for substance use disorder records. Vendor review therefore has to connect the contract to real request traffic.

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AI Vendor Risk in Automotive Needs a Model-Route Record

NHTSA tells automotive organizations to set clear cybersecurity expectations for suppliers and support verified implementation across the supply chain. Auto-ISAC publishes a dedicated third-party cybersecurity risk management guide. This article turns those supplier expectations into policy and evidence for authenticated AI model traffic.

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AI Vendor Risk for Airlines Lives at the Operational Interface

EU Part-IS requires aviation organizations to identify interfaces with other organizations that can create mutual information-security exposure and to manage risks in contracted activities. NIS2 adds direct supplier and service-provider relationships to required supply-chain security measures. This article applies both duties to authenticated airline model traffic.

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AI Vendor Risk for Accounting Firms Starts With the Client Record

FTC safeguards requirements make accounting firms responsible for selecting service providers that can protect customer information, putting safeguards in contracts, and overseeing how providers handle the data. This article applies that duty to AI requests carrying tax records, payroll files, client correspondence, and engagement notes.

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Prompt Injection Monitoring: What to Watch For in Production Traffic and Where the Signals Live

Prompt injection monitoring is the operational layer above detection. The detector fires on a single request. The monitor watches the population of requests over time and surfaces trends, drift, and emerging attack patterns. This article walks through the signals worth watching, the cadence on each, and the runtime evidence the monitor depends on.

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LLM Security Monitoring: Five Vantage Points, and What Each One Can Physically See

An LLM security monitoring program is decided by where the telemetry is collected, because each vantage point has a physical limit on what it can observe. DNS and proxy logs see domains. CASB sees applications. Provider dashboards see usage without corporate identity. Application logs see everything and are written by the system under investigation. This compares all five, states the fidelity limit of each, and gives the event schema worth sending to a SIEM.

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