Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
AI governance for behavioral health has to distinguish treatment support, documentation, outreach, crisis workflows, and administrative work. The control model should bind every approved LLM call to a person, purpose, data class, and provider route while preserving the stricter handling that applies to substance use disorder records.
Read post →This Singapore MAS AI controls mapping assigns the proposed AI risk management and SAFR runtime outcomes to accountable owners. Each row connects a source position to an objective, operational control point, test, evidence contribution and open gap, while keeping an HTTP policy gateway inside its actual boundary.
Read post →This Singapore MAS FEAT AI compliance checklist gives financial institutions eight gradable actions for agent identity, delegated authority, governance envelopes, runtime decisions, human escalation, audit records, route testing, and governance ownership. It uses the Monetary Authority of Singapore SAFR paper and August 2026 parliamentary reply as its current sources while keeping the checklist focused on execution rather than audit packaging or control mapping.
Read post →Workday AI security spans the Workday tenant and business-process permissions, along with identity controls and encryption. It also spans audit evidence and responsible-AI review. Current product pages foreground Sana, Sana AI agents from Workday, and Agent System of Record. Embedded interactions remain inside Workday''s control boundary. A separate HTTP policy layer applies only to customer integrations whose AI traffic is configurable and explicitly routed through it.
Slack AI security starts with workspace permissions, Slack AI Guardrails, data protection, and platform administration inside Slack. Custom apps and agents create a separate route when they call external models. Security teams should identify which path each feature uses, test access with named accounts, and apply request policy only to customer-controlled authenticated HTTP LLM traffic.
Read post →Singapore MAS AI audit evidence for agentic finance should preserve three linked artifacts: an authorization decision record, proof that a defined human oversight trigger fired and reached an accountable reviewer, and a consequential decision record tied to the business outcome. This article turns the SAFR framing into a practical evidence package for authenticated HTTP traffic between financial-institution users or agents and LLM endpoints.
Read post →Shadow AI in energy and utilities can transmit grid operations material, outage plans, engineering records, and vendor data to unapproved LLM services. This article maps that exposure to NERC CIP information-protection and supply-chain requirements, distinguishes regulated BES Cyber System Information from other sensitive utility data, and defines enforceable controls for authenticated HTTP AI traffic.
AI governance for the public sector needs an enterprise control plane and program-level accountability. Federal agencies can use OMB M-25-21 to assign Chief AI Officer, governance-board, inventory, and high-impact risk duties while M-25-22 connects acquisition to testing, data rights, monitoring, and exit terms. State agencies need the same operating chain under their own authorities: a use-case owner, risk decision, production evidence, appeal path, and reassessment trigger.
Read post →AI governance for dental practices should connect every approved use case to its PHI exposure, individual user, exact model service, Business Associate Agreement, reviewer, and retained evidence. HIPAA already requires risk analysis, access management, activity review, business-associate safeguards, and audit controls. Dental AI programs need to apply those duties to clinical notes, radiology assistance, claims, patient messages, and vendor-managed features.
Read post →Zendesk AI security spans third-party LLM processing, AI agent workspace roles, conversation records, account-change logs, and actions against connected systems. A production review should test each boundary with its own evidence. Native Zendesk inference stays inside the vendor-managed path, while an independent gateway applies only to a customer-controlled authenticated HTTP model request deliberately routed through it.
Shadow AI in law firms can move privileged drafts, discovery excerpts, deal terms, and client instructions into model services outside matter approval. This article isolates the unauthorized HTTP request path, shows how matter identity and ethical-wall context disappear during copy and paste, and defines decision evidence for authenticated AI traffic without treating a gateway log as proof of legal judgment.
Read post →Shadow AI in dental practices can send periodontal notes, insurance narratives, prescription details, and patient messages to model services outside approved HIPAA workflows. This article isolates the unauthorized HTTP request path, connects dental context to HIPAA risk analysis and audit controls, and defines what an authenticated AI enforcement point can prove without taking over clinical or billing judgment.
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