NeuralSeek articlesPractical reading on building, governing and running AI agents.https://neuralseek.com/resources/2026-10-04T00:00:00ZNeuralSeekhttps://neuralseek.com/https://neuralseek.com/favicon-v2_150x150.pngPrompt injection: direct, indirect, and how to contain bothhttps://neuralseek.com/resources/prompt-injection/2026-10-04T00:00:00Z2026-10-04T00:00:00ZWhy the attack cannot be patched at the model layer, what an indirect payload looks like, and where the controls have to sit.Finding the PII that pattern matching misseshttps://neuralseek.com/resources/pii-detection-in-llm-pipelines/2026-10-04T00:00:00Z2026-10-04T00:00:00ZRegular expressions catch the personal data that looks like personal data. A second, contextual pass finds the kind hidden in ordinary language.Running language models in a fully air-gapped environmenthttps://neuralseek.com/resources/air-gapped-ai-deployment/2026-10-04T00:00:00Z2026-10-04T00:00:00ZA practical walkthrough of no-egress deployment: mirrored registries, in-cluster model serving, a private retrieval stack, and the hardening that makes it defensible.The questions a healthcare privacy review asks about your AI layerhttps://neuralseek.com/resources/hipaa-and-the-llm-layer/2026-10-04T00:00:00Z2026-10-04T00:00:00ZThe engineering questions that decide whether a clinical AI project clears privacy review, and how each one becomes a configuration you can point at.Building an AI audit trail an examiner will accepthttps://neuralseek.com/resources/ai-audit-trails-in-financial-services/2026-10-04T00:00:00Z2026-10-04T00:00:00ZThe hardest audit question is what the system was configured to do on a given day. The properties that make that answerable.Controlling what your AI is allowed to readhttps://neuralseek.com/resources/retrieval-grounding/2026-10-04T00:00:00Z2026-10-04T00:00:00ZRelevance bands, freshness decay, document limits and snippet sizing: the upstream decisions that set answer accuracy before the model writes anything.