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

Sensitive Data AI Detection: Classifying Prompt Content at the AI Request Boundary

Sensitive data AI detection classifies prompt content at the AI request boundary, where the prompt is reconstructed into a structured payload and a classifier surfaces the categories the policy reads. The category set includes PII (email, phone, SSN, NPI), PHI, PCI, secrets (API keys, tokens, certificates), source code, and customer identifiers. Document-level classifiers do not run cleanly against prompt context windows. The inspection-point classifier runs at request time, surfaces labels the policy uses, and stamps the labels on the per-decision audit record.

AI Security Solutionsdata-loss-preventionai-securitydlppolicy-enforcementinline-enforcement
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Agentic AI in the Enterprise: Where the Action Surface Sits and How It Gets Controlled

Agentic AI in the enterprise introduces a new action surface: the LLM-driven agent that calls tools, queries databases, sends emails, files tickets, runs code, and triggers workflows on behalf of an authenticated user. The control problem is not whether the model behaves. The control problem is who authorized this specific action, against what data, under which policy, and with what audit record. I walk through what the enterprise action surface looks like in 2026, where the control points sit, and how the NIST three-pillar framework maps to the enterprise deployment.

Problem-Awareagentic-aiai-securityidentity-and-authorizationinline-enforcementarchitecture
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EU AI Act Article 15: What the Accuracy, Resilience, and Cybersecurity Obligation Requires

Article 15 of the EU AI Act sets the accuracy, resilience, and cybersecurity floor for high-risk AI systems before the August 2, 2026 deadline. The obligation runs end to end across the deployment, from the declared accuracy metric in the technical documentation to the runtime behavior under adversarial pressure. This article walks through the regulation text, the structural gaps in most deployments, and the architectural pattern that satisfies all three properties together.

Compliance & Regulationeu-ai-actai-complianceai-securityregulationinline-enforcement
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EU AI Act Prohibited Practices: What Article 5 Bans and How Enforcement Catches It

Article 5 of the EU AI Act lists the practices the regulation prohibits outright. Subliminal manipulation, exploitation of vulnerability, social scoring by public authorities, predictive policing based on profiling, untargeted facial scraping, emotion inference in workplaces and schools, biometric categorisation by protected characteristic, and most real-time biometric identification in public spaces. The prohibitions took effect February 2, 2025. The €35 million / 7% penalty tier applies. This article walks through the eight prohibitions and the architecture that catches them at the AI request boundary.

Compliance & Regulationeu-ai-actai-complianceregulationai-governanceinline-enforcement
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EU AI Act Codes of Practice: What the GPAI Provisions Expect and Where Deployers Sit

The Codes of Practice in the EU AI Act are the operational mechanism that translates the GPAI obligations under Articles 53 and 55 into concrete commitments providers can sign. The Code on General-Purpose AI Models, published by the AI Office, sets out the transparency, copyright, and safety expectations the providers have agreed to. Deployers that build on top of GPAI inherit downstream obligations and a set of expectations the deployer cannot delegate to the provider.

Compliance & Regulationeu-ai-actai-complianceai-governanceregulationllm
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AI Risk Register Template: What Each Row Has to Capture and Where the Evidence Comes From

An AI risk register is the operational artefact that records the risks the deployer has identified for each AI system, the controls applied, the residual risk, and the evidence that the controls are working. EU AI Act Article 9, NIST AI RMF, ISO 42001, and Fannie Mae LL-2026-04 each expect a register the deployer can produce on demand. This article walks through the columns that hold up across regimes and the runtime evidence each column depends on.

Compliance & Regulationai-governanceai-complianceeu-ai-actiso-42001audit
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AI Gateway High Availability: The Failure Modes That Matter and the Topology That Survives Them

An AI gateway sits inline between the user and the LLM. When the gateway fails, the AI traffic either stops (fail closed) or bypasses the gateway (fail open). Both choices have costs. This article walks through the failure modes that matter in production, the topology patterns that survive them, and the architectural trade-offs around fail-closed vs fail-open under regulatory pressure.

Platform & Architectureai-gatewayarchitectureai-securityinline-enforcementcloud-security
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EU AI Act Article 11: What Technical Documentation Must Show for Your AI System

Article 11 of the EU AI Act requires providers of high-risk AI systems to prepare and keep up-to-date technical documentation before placing the system on the market. The documentation has to demonstrate conformity with the high-risk requirements and be detailed enough that a national authority can assess it. Most engineering teams treat technical documentation as a deliverable rather than a continuously maintained artifact, and that habit fails Article 11 the first time a market surveillance authority asks for the file.

Compliance & Regulationeu-ai-actai-governancecomplianceauditdocumentationregulation
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AI Gateway Architecture for Streaming LLM Responses: Policy, Audit, Backpressure

Streaming LLM responses arrive as server-sent events or chunked HTTP, token by token, over a connection that may stay open for seconds or minutes. An AI gateway built for request-response patterns cannot enforce policy, redact sensitive content, or produce per-decision audit records on streaming traffic without re-architecting the proxy. This piece walks through the architectural changes streaming requires, the enforcement model that holds at chunk granularity, and the audit record shape that survives the inspection.

Platform & Architectureai-gatewaystreamingai-securitypolicy-enforcementauditarchitecture
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AI Gateway Policies for Tool Use: Authorizing Function Calls at the Request Boundary

Tool use turns an LLM call into a sequence of function invocations against the application backend, the file system, third-party APIs, and other tools the model is allowed to call. Each function call has its own authorization scope and its own audit shape. An AI gateway that enforces policy on the model request alone leaves the tool invocations unauthorized. This piece walks through the architecture for authorizing tool use at the request boundary, the per-tool policy shape, and the audit record that captures the full tool-use trace.

Platform & Architectureai-gatewaytool-usefunction-callingai-securitypolicy-enforcementaudit
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AI Gateway Redaction for RAG Contexts: Stopping Cross-Tenant Data Leakage

A retrieval-augmented generation pipeline fetches documents from a vector store, concatenates them into the prompt context, and sends the assembled prompt to the LLM. The fetched chunks can carry data the requesting user is not authorized to see. The model has no way to distinguish authorized content from leaked content. An AI gateway that redacts at the context-assembly boundary, with identity-bound policy on each retrieved chunk, is the architectural pattern that stops cross-tenant data leakage in RAG.

Platform & Architectureai-gatewayragredactionai-securitypolicy-enforcementdata-leakage
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AI System Prompt Leakage: What Leaks, How It Leaks, and Where to Stop It

System prompts carry the AI applications instructions, role assignments, tool definitions, retrieved context, and sometimes credentials or routing keys. A leaked system prompt exposes the application logic to an attacker, including the role boundaries, the tool catalog, and any sensitive context the prompt happened to include. The leakage modes are well-understood. The mitigations live at the AI request boundary, not inside the model. This piece walks through the leak surfaces, the demonstrated attack techniques, and the architectural pattern that prevents leakage.

Problem-Awaresystem-promptprompt-injectionai-securitydata-leakageai-governanceowasp-llm
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