AI Guardrails
AI Guardrails are runtime and design-time controls that screen the inputs and outputs of large language model (LLM) applications and AI agents. They detect and block prompt injection, jailbreak attempts, personally identifiable information (PII) leakage, toxic or unsafe content, hallucinations, and policy violations, and they validate structured outputs against schemas. This topic repository catalogs the vendor and open-source landscape — provider-native guardrails (AWS Bedrock, Azure Content Safety, Google Model Armor, OpenAI Moderation), third-party AI security platforms (Lakera, HiddenLayer, Cisco AI Defense, Lasso Security, PromptArmor, Wallarm), and open-source frameworks (Guardrails AI, NVIDIA NeMo Guardrails, DeepEval) — and provides a shared vocabulary, JSON Schema for policy and violation records, JSON-LD context, and example payloads.
AI Guardrails is tracked in the API Evangelist network. This page is the human-readable profile that sits on top of the machine-readable index we maintain at apis.io.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
AI Guardrails is in the network as a tracked entity. We haven't yet indexed a public API surface for it — when one is published, the artifacts, score, and agent-readiness read will appear here automatically. The source repository is where that profile is built.
← All providers · Data indexed from github.com/api-evangelist/guardrails · machine-readable index on apis.io