AI Guardrails / The library
Find the control you need to build.
practical guides
with examples and source documents
Foundations
Understand the controls
What guardrails check, where they run and which risks remain.
What are AI guardrails?
Learn where AI guardrails check content, data and actions, and how to choose controls for a real application.
Prompt injection vs jailbreak
Distinguish instruction attacks, safety bypasses and tool effects so the test measures the failure you care about.
LLM security: map the whole application
Build a threat model around data, retrieval, tools, credentials and output consumers.
Threats
Trace the failure
Untrusted documents, persistent memory and instructions that cross a trust boundary.
Prompt injection prevention for apps and agents
Reduce prompt injection risk by separating untrusted content from authority and testing the data and actions an attacker could reach.
Indirect prompt injection
Trace an injected document or tool result through an agent, and test the boundary that prevents an unauthorized effect.
Memory poisoning in AI agents
Track untrusted information from memory write to later retrieval, correction and action.
Implementation
Build the boundary
Retrieval, output, data access and the tools an agent can use.
LLM guardrails for applications that generate answers
Design LLM guardrails around retrieval, generation, output validation and failure handling without assuming one filter covers every path.
AI agent guardrails for work with real consequences
Control an agent's tools, targets and approvals so a mistaken plan does not automatically become an authorized action.
Tool calling guardrails that hold at execution time
Validate tool arguments, check trusted permissions and bind approvals before executing an AI agent's proposed operation.
PII redaction in AI applications
Place personal-data redaction before disclosure, test the entities your application handles and inspect tool and logging paths separately.
Structured output validation for AI applications
Validate generated JSON before using it, then check the business rules and permissions a schema cannot decide.
Fail-closed guardrails without accidental bypasses
Define how protected actions stop when guardrail decisions are missing, stale or unusable, and plan recovery without granting wider permission.
RAG security from ingestion to answers
Preserve document permissions through chunks, retrieval, caches and deletion, with a worked two-tenant example.
AI agent sandboxes
Review filesystem, network, identity and secret boundaries before giving a coding agent an execution environment.
AI guardrails examples
Work through schema, redaction, permissions, approvals and outage checks with explicit expected outcomes.
Testing & tools
Check what holds
Worked cases, red teaming and evidence for choosing a control.
How to test AI guardrails
Build a repeatable guardrail test suite that measures prohibited outcomes, valid-task failures and behavior during outages.
Guardrails vs fine-tuning: which problem are you solving?
Choose between runtime checks and model training by separating task-quality problems from permissions and application rules.
How to choose AI guardrails tools
Compare guardrail tool categories by what they inspect, where they enforce rules and how they fail in your application.
An AI guardrails checklist for release review
Use an evidence-based checklist to review data access, outputs, tool permissions, failure handling and ownership before releasing an AI workflow.
Prompt injection detection
Evaluate detector coverage, score thresholds, false blocks and outages against the inputs your application actually processes.
LLM red teaming for agents
Plan an authorized adversarial exercise, inspect real effects and turn findings into regression tests.
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