229 posts on platform & architecture.
Cursor teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
CrewAI teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
Cohere teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
Cohere teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
Canva AI teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
Amazon Q teams need evidence that connects each AI request to an authenticated actor, policy decision, and timestamp. This article separates provider administration records from independent request-layer evidence and maps the HTTP controls that regulated enterprises can verify during a security review.
Box announced prompt injection detection and MCP guardrails for AI agents on July 22, 2026, plus audit trails covering more than 300 admin actions. The controls secure what happens inside Box and what external agents may do to Box content. They do not evaluate outbound calls an application makes from Box data to an external LLM API.
Microsoft Entra ID, private endpoints, and RBAC secure three separate layers of an Azure AI Foundry deployment: authentication, network isolation, and resource-management permissions. None of the three evaluates whether a specific authenticated end user should be allowed to send a specific prompt to a specific model right now.
Azure AI Foundry traces LLM calls, tool invocations, and agent decisions through OpenTelemetry integrated with Azure Monitor Application Insights. The tracing captures the Entra ID identity or managed identity making the call, not necessarily the end user a multi-tenant application serves. This piece maps what Foundry observability records and where the identity gap sits.
Atlassian Intelligence operates inside Atlassian cloud products and acts on the content and permissions available in those products. A sound security review maps the identity, data, feature, audit, and connector boundaries for the selected use case. This article distinguishes SaaS application controls from direct LLM API request policy.
Bedrock Guardrails, PrivateLink, and IAM resource policies cover three real but separate security jobs: content filtering, network isolation, and API authorization. None of the three evaluates whether a specific authenticated caller should be allowed to send a specific prompt right now. This piece maps the three controls and the request-authorization gap between them.
Amazon Bedrock model invocation logging records the full request and response body plus the IAM ARN of the calling principal, but it is disabled by default and the identity field it captures is a role, not a person. This piece breaks down the log schema, the CloudTrail split, and what an auditor still cannot get from either.