LLM guardrails and policy enforcement for secure AI
Protect against prompt injection and unsafe outputs. Enforce policies at input/output and log every decision for compliance and security.
Multi-layer LLM protection
Input validation, output filtering, and policy enforcement—with full audit trails.
Input Validation
Detect and block prompt injection, jailbreak attempts, and malicious inputs before they reach your LLM.
Output Filtering
Prevent unsafe, biased, or non-compliant outputs from reaching end users with policy-based filtering.
Policy Enforcement
Enforce organizational policies at runtime—block prohibited content, limit scope, and enforce guardrails.
PHI & PII Protection
Detect and redact protected health information and personally identifiable information in prompts and responses.
Audit Evidence
Log every guardrail decision, policy violation, and intervention with immutable audit trails.
Real-time Monitoring
Monitor guardrail effectiveness, violation rates, and false positives with live dashboards.
What LLM guardrails defend against
Common threats to LLM security and how Aureum Health mitigates them.
Prompt Injection
Malicious inputs that attempt to override system instructions or extract sensitive information.
Input validation and sanitization before LLM processing with pattern detection and blocking.
Jailbreak Attempts
Users trying to bypass safety controls or get the model to violate policies.
Multi-layer guardrails that detect manipulation attempts and enforce constraints.
Data Leakage
LLM outputs that inadvertently expose PHI, PII, or proprietary information.
Output filtering with regex, NER, and policy-based redaction before delivery.
Unsafe Outputs
Harmful, biased, or non-compliant responses that violate organizational policies.
Content moderation and policy checks on every response with automatic blocking.
Guardrails that generate compliance evidence
Every decision logged, every policy enforced, every intervention auditable.
Frequently Asked Questions
What are LLM guardrails?
LLM guardrails are runtime controls that validate inputs, filter outputs, and enforce policies before and after LLM processing. They protect against prompt injection, unsafe outputs, and policy violations.
How do guardrails prevent prompt injection?
Guardrails detect prompt injection by analyzing input patterns, identifying instruction override attempts, and validating against known attack signatures before the prompt reaches the LLM.
Do guardrails impact LLM performance?
Guardrails add minimal latency (typically <100ms) but dramatically reduce risk. Aureum Health optimizes guardrail execution to balance security and performance.
How are guardrail decisions logged?
Every guardrail decision is logged to an immutable audit trail with the input, output, policy applied, and decision made—creating compliance evidence automatically.