Secrets Management for AI Web Applications
Keep provider, database, cloud, OAuth, webhook, and tenant credentials out of browsers and models with scoped access, rotation, and auditable brokers.
Category
Practical coverage of secure web architecture, applications, protocols, and operations.
Keep provider, database, cloud, OAuth, webhook, and tenant credentials out of browsers and models with scoped access, rotation, and auditable brokers.
Design tenant-aware LLM caches that preserve personalization without leaking responses, retrieval results, tool data, or stale permissions.
A practical resource-governance guide for limiting LLM, agent, tool, queue, and external API spend without mistaking every spike for abuse.
A practical guide to separating authentication sessions, conversations, agent runs, and memory so multi-turn AI applications remain authorized and revocable.
A practical defensive guide to authenticating webhooks and safely triggering AI workflows, tools, jobs, and side effects.
Defensive guidance for securing AI document uploads before parsing, OCR, chunking, embedding, indexing, or LLM retrieval.
A practical guide to enforcing trusted, tenant-bound API authorization for model-proposed tool calls, background jobs, and delegated agents.
A practical architecture for preventing cross-tenant leakage across LLM prompts, RAG, memory, caches, tools, credentials, jobs, logs, and billing.
Defensive guidance for securing AI-agent URL fetching against SSRF with destination policy, DNS validation, egress controls, redirects, and response limits.
A practical CSP guide for AI-generated interfaces, streamed responses, remote assets, workers, frames, and safe browser defense in depth.
Practical browser security guidance for rendering model-generated text, Markdown, links, code, HTML-like content, and structured output.
A practical architecture and checklist for securing web applications that combine LLMs, RAG, uploads, streaming, tools, and multi-tenancy.