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
AnShinGPT
Practical guides for securing LLM and AI agent apps
Practical GenAI security guides from the AnShinGPT team, the scanner API for prompts and LLM responses: prompt injection, RAG, agents, MCP, output handling and data protection.
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
OWASP Top 10 for LLM Apps: Turning It Into Controls
Map each OWASP LLM Top 10 risk to an owner, a preventive control, a monitoring signal and a test, so the list becomes evidence instead of a slide.
4 min read
Fundamentals See all 1 →
OWASP Top 10 for LLM Apps: Turning It Into Controls
Map each OWASP LLM Top 10 risk to an owner, a preventive control, a monitoring signal and a test, so the list becomes evidence instead of a slide.
4 min read
Evaluation & Ops See all 3 →
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
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