Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Obsidian Security 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.
Read post →Use this 12-item NIST 800-53 AI compliance checklist to prepare endpoint, identity, policy, and audit evidence an assessor can test. Every item maps to a control identifier and defines a concrete completion check for the next assessment.
Read post →Detect prompt injection in production by inspecting inbound prompts, model outputs, and tool requests. Each layer catches a distinct path to an unsafe action, and the model-request and tool-authorization boundaries need separate evidence.
Read post →A Swiss FADP checklist for live AI deployments: eleven tests for inventory, purpose, processor terms, records, foreign disclosure, transparency, automated decisions, DPIA, security, rights and incidents. Every test names its owner, evidence and completion condition.
Read post →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.
Read post →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.
Read post →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.
Vectara provides a platform for retrieval-augmented generation and grounded answer delivery. Teams evaluating Vectara alternatives should separate retrieval quality, application observability, provider routing, and policy enforcement on live LLM API traffic. This guide maps those jobs to the evidence each buyer should require.
Read post →Galileo provides observability, evaluation, and agent-control capabilities for GenAI applications. Teams looking for Galileo alternatives should separate application-quality work from an enforcement decision on live LLM API traffic. This comparison explains the architectural categories, names the buyer fit for each, and shows where DeepInspect belongs.
Read post →Aporia alternatives split across six distinct jobs: AI observability, model evaluation, prompt-injection protection, routing, and identity-bound request enforcement. This buyer evaluation names the architecture each product operates in, the evidence it can produce, and the deployment profile it serves.
Read post →Model-provider safety training reduces jailbreak success rates but does not eliminate them. Production deployments layer three defenses around the model: input-side classifiers that flag adversarial prompts, output-side classifiers that flag policy-violating responses, and identity-aware policy at the request boundary that limits what a successful jailbreak can accomplish. The layered pattern, the residual failure modes, and the audit record each layer produces.
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