Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Claude connectors let Claude call approved remote MCP servers. Audit evidence for that flow must distinguish the Claude request, the connector authorization, the remote tool action, and the application record. This article shows what each system can prove and where an independent request-level policy record fits.
OWASP LLM01:2025 combines direct and indirect prompt injection. This guide maps the two paths, the controls at the HTTP model-request boundary, the adjacent tool-authorization responsibility, and the evidence each decision should retain for review.
Read post →ISO/IEC 42001 certifies an organization’s AI management system. IAPP AIGP credentials people, and NIST AI RMF offers voluntary guidance. This guide separates the programs and explains the runtime evidence none of them automatically produces.
Read post →A HIPAA BAA for an AI vendor needs more than a signature. This guide maps the clauses that govern PHI in an LLM request, training restrictions, subprocessor changes, incident notice, and deployer-side audit evidence.
Read post →An AI audit log needs a demonstrable custody history to serve as evidence. This guide covers the request record, signed write, durable storage, independent verification, access trail, and inquiry package required for forensic integrity.
Read post →AI agent observability needs a trace for each model call, tool handoff, retrieval result, and policy decision. This guide defines the signals, OpenTelemetry fields, correlation IDs, and retention split that make autonomous behavior reviewable.
AI regulatory compliance needs evidence that a named person or agent reached an approved model, a policy decided the request, and a review can reconstruct that decision. This guide maps EU AI Act, US state, and sector rules to those runtime controls.
Read post →Helicone offers an OpenAI-compatible AI gateway with logging, observability, provider routing, and operational features for LLM applications. Teams seeking Helicone alternatives need to distinguish gateway operations from an independent policy decision on live AI traffic. This comparison maps the categories, named architecture, and buyer fit for each.
EU AI Act Article 73 requires providers of high-risk AI systems to report serious incidents to the relevant market-surveillance authority. This guide explains the Article 3(49) trigger, the 15-day outer limit, the two-day widespread-infringement timeline, the 10-day death timeline, and the evidence a provider needs to investigate and report.
Read post →OWASP LLM06 covers sensitive information disclosure: the model emits data the application or the user is not authorized to receive. The disclosure paths split into three: training-data leakage, in-context leakage from RAG and tool outputs, and cross-tenant leakage from shared deployments. The output-side controls live at the gateway, where every response is observable before it reaches the user. This article walks through the LLM06 disclosure paths, the output-side controls that work, and the redaction and policy patterns to enforce.
Read post →Varonis secures enterprise data through discovery and permissions analysis, then monitors activity across cloud, SaaS, and on-premises systems. DeepInspect governs HTTP traffic between authenticated callers and LLM APIs with per-request policy and signed audit records. The comparison separates data-centric security from model API authorization.
TrueFoundry provides an AI Gateway for routing, observing, and managing access to multiple LLM providers. DeepInspect is an identity-aware enforcement proxy for HTTP traffic between authenticated callers and LLM APIs. This comparison helps buyers separate gateway operations from independent authorization and signed audit evidence for every AI request.
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