Envoy AI Gateway Is Now Agent Router: Alternatives Compared
Envoy AI Gateway is now Agent Router. Compare routing alternatives with identity-aware controls that authorize AI requests and log each decision.
Read post →Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Envoy AI Gateway is now Agent Router. Compare routing alternatives with identity-aware controls that authorize AI requests and log each decision.
Read post →An AI RFP template should convert risk requirements into scored evidence, controlled demo tests, contractual commitments, and explicit rejection conditions. NIST AI RMF supplies a lifecycle structure, while the Generative AI Profile adds risks and actions specific to generative systems. Procurement should score what a vendor can prove and bind accepted claims into the contract.
Read post →Microsoft Sentinel AI logs work when the team defines source ownership, a stable event contract, table placement, correlation IDs, analytics rules, and retention before ingestion. Azure OpenAI resource logs provide service telemetry. Independent gateway decisions add authenticated identity and policy evidence. Raw prompts and responses should follow a narrower content path.
Read post →RBAC for LLM systems should decide which identity may use a model route, with which data class, for which action. Cloud roles establish access to provider resources, but an enterprise policy matrix still has to govern prompts, tools, routes, and responses at request time. Denial tests and independent decision records make that matrix enforceable.
LLM cost management needs a normalized request ledger before it needs another dashboard. Provider usage reports expose different identity, model, cache, and service-tier fields. A useful control loop joins those records to application identity, applies versioned prices, enforces budgets before requests run, and reconciles estimates against provider billing data.
Read post →LLM API key rotation is a controlled credential replacement, not a console click. The runbook starts with inventory, creates a restricted replacement, deploys it through a dual-key window, validates every workload, revokes the old key, and preserves a tested rollback path. This guide also separates provider credential controls from HTTP model-request policy and states the stolen-key boundary plainly.
RAG supplies retrieved content to a model at request time, while fine-tuning adapts model weights through additional training. That difference changes the security plan for poisoning, authorization, deletion, provenance, evaluation, rollback, and runtime inspection. This comparison maps each control to the corpus, training pipeline, model registry, retrieval service, or HTTP model boundary that can actually enforce it.
Zscaler and Netskope now describe broad AI security portfolios spanning discovery, user access, data controls, runtime protection, and agent traffic. A useful comparison starts with the traffic path and the evidence each control produces. This guide maps browser and SSE coverage, SaaS discovery, DLP, AI gateways, agent controls, identity context, and audit evidence without treating every module as interchangeable.
Read post →Singulr combines AI discovery, risk assessment, onboarding, policy enforcement, and reporting. This guide maps replacement options module by module and keeps endpoint, shadow-AI, SaaS, and local-agent controls outside the narrower DeepInspect HTTP request boundary.
Read post →Tyk AI Gateway combines authentication, model routing, rate limits, policy, and analytics, while Tyk AI Studio extends into agent, RAG, tool, and cost governance. This guide shows how to replace those functions without dropping a control during migration.
Straiker combines agent discovery, adversarial testing, runtime defense, and containment. This guide separates those jobs so buyers can replace one module while retaining another, or pair a request-layer control with endpoint and tool security without pretending that one product covers every route.
Read post →AI audit trail requirements for the EU AI Act, HIPAA, DORA, NIST, and mortgage decisions. See the decision-record fields, retention questions, and evidence boundary each regime brings to an AI audit and regulatory review.
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