Blog

Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.

LLM Guard Alternatives: What to Evaluate in 2026

Protect AI LLM Guard works for single applications where the team controls the LLM call site. The limits appear once the AI footprint expands or once a regulator asks for an audit record that identifies the natural person behind a specific request. This piece walks through six alternatives across the in-process and out-of-process layers and explains which fits which regulatory and operational profile under the EU AI Act Article 12 and NIST AI RMF identity-and-authorization framework.

Comparisons & Alternativesalternativesllm-guardprotect-aiai-gatewaycomparison
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Japan APPI AI Controls Mapping for LLM Requests

A Japan APPI AI controls mapping should connect each applicable statutory duty to an owner, an enforcement point, a test, and retained evidence. This mapping covers purpose limitation, special care-required information, security measures, employee and processor supervision, domestic and foreign provision, incident response, and individual rights, with applicability nuances for entrusted LLM providers.

Compliance & Regulationai-complianceai-governanceauditpolicy-enforcementidentity-and-authorization
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ISO/IEC 5338 AI Controls Mapping to Runtime Evidence

ISO/IEC 5338:2023 organizes AI system life cycle work into agreement, organizational project-enabling, technical management, and technical processes. This mapping connects those process families to control objectives, owners, implementations, and evidence, while marking the boundary between model and data lifecycle controls and the HTTP request controls DeepInspect can enforce.

Compliance & Regulationai-complianceai-governanceauditpolicy-enforcementidentity-and-authorization
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ISO/IEC 27701 AI Controls Mapping at the HTTP Request Boundary

ISO/IEC 27701:2025 sets requirements for privacy information management by PII controllers and processors. This mapping connects AI inventory, identity, purpose, classification, processor routing, retention, incident response, and control testing to concrete owners and request-level evidence.

Compliance & Regulationai-complianceai-governancecomplianceauditpolicy-enforcementidentity-and-authorization
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Setting Up AI Policy Enforcement: From the First Rule to a Production Deployment

AI policy enforcement is the runtime control point that turns a written policy into a per-request decision. This guide walks through how to set up enforcement: the policy schema, the decision-point placement, the per-route and per-role rules, the audit format that proves the policy was applied, and the deployment sequence that gets a production-ready enforcement layer live in 8 to 12 weeks.

AI Security Solutionsai-policyenforcementimplementation-guideai-governanceinline-policyai-security
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NIST AI RMF Mapping for AI Gateways: How the Four Functions Land on Request-Layer Controls

The NIST AI Risk Management Framework (AI RMF 1.0, released January 2023) organizes AI risk controls into four functions: Govern, Map, Measure, Manage. The framework is voluntary, but US federal procurement, Fannie Mae LL-2026-04, and the GSA AI Acquisition Resource Guide all reference it directly. This guide walks each of the four functions to the request-layer control on an AI gateway that satisfies it.

Compliance & Regulationnist-ai-rmfcomplianceai-governanceai-gatewayauditcontrols-mapping
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EU AI Act News Today: Live Tracker for Enforcement, Guidelines, and Member-State Implementation

Live tracker for EU AI Act enforcement actions, Commission guidelines, AI Office decisions, member-state designations, Code of Practice updates, and major court rulings. Updated as developments land. Current entries through June 2026 cover the GPAI guidelines, the Article 5 prohibited-practices enforcement, and the August 2 high-risk system deadline countdown.

Compliance & Regulationeu-ai-actregulationcompliancenewstrackerai-governance
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DeepInspect vs WhyLabs: Open-Source Observability and Runtime Enforcement

WhyLabs was an ML and LLM observability platform built on statistical profiling, maker of whylogs and LangKit. The company wound down its hosted service in early 2025 and open-sourced the platform. DeepInspect is a proxy that decides whether a model call proceeds based on identity and records the decision. This is an honest read on what WhyLabs is now and where runtime enforcement fits.

Comparisons & Alternativesllm-observabilitycomparisonai-securitypolicy-enforcementauditopen-source
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DeepInspect vs Vercel AI Gateway: Enforcement and Audit vs Routing and Developer Experience

Vercel AI Gateway and DeepInspect both sit between an application and model APIs, and they solve different problems. Vercel AI Gateway gives developers one endpoint for many models with routing, failover, and spend tracking. DeepInspect enforces identity-bound policy and produces per-decision audit records for regulated environments. This compares what each does, where each fits, and how to pick.

Comparisons & Alternativesai-securityllm-securityai-governancepolicy-enforcementinline-enforcement
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DeepInspect vs Vellum AI: LLM App Development and Runtime Enforcement

Vellum is an LLMOps platform for building, evaluating, deploying, and monitoring LLM apps and agent workflows: a prompt playground, evaluation suites, a visual workflow builder, and production monitoring. DeepInspect is a proxy that decides whether a model call proceeds based on identity and records the decision. This walks what each one owns, a feature table, and where a development platform and a security control diverge.

Comparisons & Alternativesllm-securitycomparisonllm-evaluationai-securityauditpolicy-enforcement
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DeepInspect vs TrueFoundry: A Kubernetes AI Platform and a Focused Policy Layer

TrueFoundry bundles an AI gateway, MCP governance, model serving, and GPU orchestration into one Kubernetes-native platform that runs in your own cloud, fronting 1600-plus models with rate limiting, input and output guardrails, RBAC, and audit logging. This piece covers what the platform does well, where its audit sits relative to the systems it also runs, and how a decoupled identity-bound policy and audit proxy fits alongside it.

Comparisons & Alternativescomparisonllm-securityai-securitypolicy-enforcementarchitecturekubernetes
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DeepInspect vs Traceloop: OpenTelemetry Observability and Runtime Enforcement

Traceloop builds OpenLLMetry, an open-source OpenTelemetry instrumentation layer for LLM apps, plus a hosted backend and a Rust gateway. DeepInspect is a proxy that decides whether a model call proceeds based on identity and records the decision. This walks the architecture of each, a feature table, and where tracing and identity-bound enforcement diverge.

Comparisons & Alternativesllm-observabilitycomparisonai-securitypolicy-enforcementauditopen-source
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