Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Braintrust alternatives should be chosen by the control surface a team needs. Braintrust is commonly evaluated for LLM evaluation and observability; teams needing request-time AI policy enforcement should separate that requirement from evaluation, observability, and tracing.
Read post →Zscaler AI Guard scans GenAI prompts and responses through the Zero Trust Exchange. DeepInspect applies identity-bound allow, redact, or block policy to HTTP requests between authenticated callers and LLM APIs, then records the decision. This comparison separates browser and configured-application protection from direct API and agent traffic.
Read post →AI governance gives General Counsel a traceable path between a legal obligation, the policy approved to address it, and the evidence produced when employees or agents use an LLM. The legal team should own the interpretation, decision rights, privilege rules, and escalation triggers while security and engineering implement the controls.
AI procurement should define the evidence, control access, incident duties, model-change notice, data-use limits, and exit rights the business needs before vendor selection. The procurement lead owns a repeatable gate that converts AI risk requirements into scored evidence and contract terms, then hands production monitoring to named control owners.
Read post →This SOC analyst AI risk checklist turns an AI alert into a triage and incident decision. It fixes the incident threshold, telemetry join, scope estimate, evidence handling, containment test, recovery proof, and handoff record for governed model traffic while preserving the boundary around endpoint, vendor, and local execution paths.
Australia’s Notifiable Data Breaches scheme applies when an AI incident involves unauthorised access, disclosure, or loss of personal information and serious harm is likely. The operational challenge is preserving enough request-level evidence to assess the event, support remedial action, and explain the notification decision.
Read post →Shadow AI in media and publishing can move confidential source material and embargoed copy into unapproved LLM services. Unpublished manuscripts and licensed assets can follow, as can legal-review notes. This article defines the editorial data at risk, separates input control from output provenance, and sets out request-level enforcement for authenticated HTTP AI traffic.
Read post →Shadow AI in health payers can send member PHI, claims histories, utilization management records, and appeal narratives to unapproved model services. This article maps the exposure to HIPAA Privacy and Security Rule duties, Business Associate Agreement scope, and CMS payer data exchange requirements, then defines enforceable controls for outbound HTTP LLM traffic.
Read post →This FINRA AI controls mapping connects supervisory, communications, recordkeeping, vendor, and routed-request objectives to accountable owners, control points, repeatable tests, retained evidence, and explicit gaps. It separates FINRA source language from implementation choices and gives an HTTP gateway credit only for decision evidence on traffic that actually traverses it.
Read post →AI governance for wealth management should separate research assistance, client communications, personalized advice, portfolio actions, and operations into distinct approval lanes. Each use case needs a business owner, supervisory owner, technical owner, approved client data, model route, authority ceiling, tests, and change triggers. Request-layer controls provide one evidence stream for firm-controlled HTTP traffic, while advisers and principals retain responsibility for advice, review, conflicts, records, and client outcomes.
Read post →Salesforce documents Trust Layer controls for data access, TLS, zero data retention with third-party LLM providers, masking, toxicity scoring, grounding, and audit. Each control answers a different security question. A defensible review maps those functions to Salesforce-managed traffic, CRM permissions, external model routes, application actions, and independent HTTP policy enforcement.
Claude connectors give an application a route to approved remote MCP servers. Secure deployments need a defined server inventory, authorization at the MCP server, narrow tool permissions, trusted connector configuration, and request-level policy for the LLM traffic that begins the flow. This article maps those controls to their actual boundaries.