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Industry Verticals

73 posts on industry verticals.

Retail AI Pricing Compliance: Governing the Model Calls Behind a Price

Retailers now route parts of the pricing workflow through LLMs: generating pricing rules, analyzing competitor and demand signals, and powering merchandiser copilots. When those steps call a model over HTTP, regulators and plaintiffs will ask what data drove the decision and whether prohibited attributes were involved. This walks the compliance patterns for AI-assisted pricing: binding each model call to an identity, classifying the inputs in the prompt, and a per-decision audit record of what reached the model, with an honest boundary on what a gateway does not cover.

ai-securityai-governancecomplianceregulationauditidentity-and-authorization
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AI Security for Legal Research: Governing What Reaches the Model

Lawyers use AI to research case law, draft, and review documents, which sends client facts, privileged analysis, and confidential material to an LLM over HTTP. The duty of confidentiality and the risk of a privilege waiver both attach to that request. This walks the security patterns for legal research AI: binding each call to a user and matter identity, classifying privileged and confidential content in the prompt, egress control, and a per-decision audit record of what was disclosed to the model.

ai-securitycomplianceidentity-and-authorizationauditshadow-airegulation
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AI Security for SOC Teams: Governing What Analysts Send to the Model

SOC analysts use AI copilots to triage alerts, enrich indicators, and summarize incidents, which sends logs, IOCs, and internal telemetry to an LLM over HTTP. The tools the SOC runs to watch everything else, the SIEM and EDR, cannot read that prompt traffic. This walks the security patterns for a SOC using AI: binding each call to an analyst identity, classifying internal telemetry in the prompt, egress control, and a per-decision audit record of what left for the model during triage.

ai-securityshadow-aiidentity-and-authorizationpolicy-enforcementcybersecurityaudit
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HIPAA and AI Access Logging: Recording Who Sent PHI to a Model

HIPAA has required access logging on systems that touch protected health information for two decades. AI put a new door in the wall: clinicians and applications now send PHI to model APIs, often under a shared key and without a business associate agreement. This piece covers what the HIPAA audit and access requirements mean for AI traffic, and the request-layer logging that keeps model calls inside the same evidentiary standard as any other PHI system.

hipaahealthcare-aiai-auditphicompliance
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An AI Gateway for Insurance: Governing Model Calls in Underwriting and Claims

Insurers run models across underwriting, pricing, claims, and fraud, and regulators from the NAIC model bulletin to state algorithm rules now expect governance and records for those decisions. This piece covers why an AI gateway, the enforcement kind that binds identity and writes a per-decision record, fits the insurance control environment, and where its boundary sits against actuarial and model-risk work it does not replace.

insurance-aiai-gatewayai-governanceunderwritingcompliance
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AI Gateway for Legal Firms: Keeping Privileged Data Inside Policy

Lawyers paste privileged client material into AI tools to draft and summarize, and a firm carrying a duty of confidentiality usually has no record of which matter that data belonged to or where it went. This piece shows how an AI gateway puts model traffic under matter-aware policy and produces the per-decision record a firm needs to show client data stayed inside its obligations.

legal'ai-security''confidentiality''ai-gateway''compliance'
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AI Gateway for Government Agencies: Identity and Audit for Public-Sector AI

Public-sector AI use carries obligations most agency deployments cannot yet meet: zero-trust access, a defensible record of automated decisions, and evidence for oversight and public-records requests. This piece shows how an AI gateway extends identity-aware access control and per-decision logging to model traffic, so an agency can govern AI use and produce the record its accountability rules assume.

government'public-sector''zero-trust''ai-audit''compliance'
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HIPAA-Compliant AI Agent Platforms: BAAs, Access Control, and Audit Evidence

HIPAA compliance for an AI agent platform starts with a signed Business Associate Agreement, but the Security Rule also demands access control under 45 CFR 164.312(a) and audit controls under 164.312(b). This guide names the model platforms and healthcare tools that sign BAAs in 2026, then explains why a vendor BAA alone does not produce the identity-bound access-control and audit evidence a covered entity needs across its own clinicians and agents.

hipaahealthcareai-complianceai-securityidentity-and-authorizationauditpolicy-enforcement
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HIPAA-Compliant AI Tools: The Criteria That Decide Whether a Tool Qualifies

A vendor badge that reads "HIPAA compliant" answers one question and stays silent on the two that an OCR review probes. This guide gives healthcare compliance teams the three-part Security Rule test for any AI tool (a signed BAA under 45 CFR 164.502(e), access control under 164.312(a), and audit controls under 164.312(b)), walks the tool categories a covered entity actually assembles, and shows where the residual access-control and audit obligation stays with the covered entity after every BAA is signed.

hipaahealthcareai-complianceai-securityidentity-and-authorizationauditpolicy-enforcement
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NYDFS AI Compliance: Applying Part 500 Access, Audit, and Third-Party Controls to LLM Traffic

The NYDFS Cybersecurity Regulation, 23 NYCRR Part 500, requires covered financial entities to enforce least-privilege access, maintain audit trails, monitor authorized user activity, and govern third-party service providers. When employees and AI agents send data to LLMs, those requirements apply to the prompt traffic. NYDFS issued specific guidance in October 2024 on AI-related cybersecurity risks, and it maps to controls already in Part 500. This piece walks the Part 500 sections that land on AI inference traffic and shows how to enforce access and produce the audit trail the regulation requires.

nydfscompliancefinancial-servicesai-securityidentity-and-authorizationai-audit-loggingthird-party-risk
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GLBA AI Compliance: How the Safeguards Rule Applies When Customer Data Reaches an LLM

The FTC Safeguards Rule under GLBA requires financial institutions to run a written information security program with access controls, monitoring of authorized user activity, and oversight of service providers. When an employee or an AI agent pastes customer nonpublic personal information into an LLM prompt, that data becomes AI traffic to a third party, and the Safeguards Rule follows it there. This piece walks the specific Safeguards Rule provisions that land on AI inference traffic, where standard controls miss it, and how to enforce access and produce the audit record the rule expects.

glbacompliancefinancial-servicesai-securityidentity-and-authorizationai-audit-loggingdata-protection
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CMMC AI Compliance: Applying the Access Control and Audit Families to LLM Traffic Handling CUI

The CMMC Program, established by the DoD final rule at 32 CFR Part 170 effective December 16, 2024, requires defense contractors handling Controlled Unclassified Information to meet the NIST SP 800-171 controls, with Level 2 assessed against those requirements. The Access Control and Audit and Accountability families apply directly when a contractor employee or agent sends CUI into an LLM prompt. This piece walks the 800-171 control families that land on AI inference traffic, where standard controls miss it, and how to enforce access and produce the audit record an assessor samples.

cmmccompliancegovernmentai-securityidentity-and-authorizationai-audit-loggingcontrolled-unclassified-information
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