Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
This internal auditor AI risk checklist turns a planned assurance engagement into an authorized sampling memo. It fixes the independence statement, audit population, sample logic, control tests, evidence custody, and escalation criteria before fieldwork begins, while keeping recurring AI risk reporting in a separate process.
Read post →This General Counsel AI risk checklist defines a legal approval gate for a specific AI use and material change. It covers product and public claims, contractual risk allocation, privilege, regulatory posture, litigation holds, exceptions, and a signed legal disposition while keeping recurring executive reporting in a separate process.
Read post →This RAG security checklist defines the evidence required before a retrieval-augmented generation service reaches production. It covers ingestion provenance, permission-aware retrieval, tenant isolation, assembled-prompt policy, response handling, deletion, audit reconstruction, and a signed release disposition without duplicating the runtime architecture review.
Read post →Chatbot security controls work when each exposure has a named owner, a precise enforcement location, and a verification signal that can fail a release or trigger an incident. This guide maps prompt injection, sensitive-data disclosure, excessive access, unsafe output, route bypass, and missing evidence to controls that security teams can operate and test.
AI runtime security controls become operational when each policy decision has a telemetry signal and an enforcement point, with a named owner. This control-to-telemetry map covers identity and prompt data, route authorization and response handling, consumption limits and audit integrity, plus incident response and change state across chatbots and RAG systems, batch jobs and copilots, as well as agents. It also separates HTTP model-call controls from tool, sandbox, and local-execution controls owned elsewhere.
Read post →An AI runtime security checklist should end in a signed go-live decision for every production model route. This checklist tests identity propagation, prompt data policy, model authorization, response handling, quotas, failure behavior, incident evidence, and unmanaged-route exclusions across chatbots, RAG pipelines, background jobs, copilots, and agents. Each check has an acceptance record that a release reviewer can verify before traffic starts.
Read post →This AI egress filtering checklist defines a go-live acceptance record for model traffic. Platform and security teams verify covered workloads, approved destinations, DNS behavior, payload policy, identity context, fail-closed behavior, bypass tests, logging and rollback before enabling production traffic.
Read post →This AI discovery checklist turns network, identity, expense, endpoint and application findings into an accepted post-deployment inventory. Security teams reconcile each observed AI service to an owner, use case, data class, access mode and disposition, then sign the remaining blind spots and exceptions.
Read post →This CTO AI risk checklist defines eight checks for a pre-release or material-change engineering gate. It ends in a signed go/no-go readiness record that binds the release to model routes, connectors, data classes, tested failure behavior, rollback evidence and unresolved technical exceptions. Executive reporting stays outside this gate.
Read post →This CIO AI risk checklist turns AI portfolio governance into eight authorization decisions for service scope, ownership, route and data review, acceptance testing, signed approval, renewal, time-limited exceptions and retirement. The final artifact is a signed CIO service-acceptance record tied to evidence, while dashboard design and recurring executive reporting stay in their separate operating process.
Read post →New York DFS Circular Letter 7 expects insurers using external AI in underwriting and pricing to keep responsibility for compliance, oversee vendors, maintain governance documentation, and seek audit and regulatory-cooperation terms where appropriate. Request-level controls connect those duties to the exact data, model, and user involved.
COPPA treats personal information collected or maintained by an operator’s agent or service provider as collected on the operator’s behalf. The UK Children’s Code explicitly covers games likely to be accessed by children and calls for high-privacy defaults and data minimization. This article maps those duties onto authenticated model requests.