AnShinGPT
Fail Open or Fail Closed for LLM Security Controls
What should your AI app do when its security scanner times out or errors? A guide to choosing fail-open, fail-closed or review per workflow, with tested fallbacks.
4 min read
Category
Evaluation & Ops
Buying and running LLM security in production: build vs buy, evaluating security APIs, choosing fail-open or fail-closed behavior, balancing latency and false positives, and logging without over-retaining data.
3 articles
LLM Security in Production: Latency vs False Positives
Balance latency, false positives and missed attacks in LLM security. Set per-category thresholds, use warn and review states and measure tail latency.
4 min read
AI Security Logging and Observability
What to log for AI security events and what to leave out: request IDs, categories, policy versions and tool calls, with raw prompts kept separate.
4 min read