Deeploy’s guardrails give you structured control over sensitive data in your LLM applications, ensuring compliance and security.
When organization use LLM-powered chatbots employees often input sensitive data, like customer details, health records, or confidential company information, into these systems. Vendor models can expose this data, creating compliance and security risks. Written policies aren’t enough. LLM guardrails solve this by automatically detecting, filtering, and neutralizing sensitive or unwanted content before it reaches external providers, ensuring your deployments handle data securely and consistently.
Guardrails act like smart filters, checking every input and output for sensitive information. When they find risky content, they automatically remove or replace it before anything is processed, keeping data safe without interrupting the user experience. This ensures rules are applied consistently, so you don’t have to rely on users to follow policies.
Deeploy provides guardrails that can be deployed along with your LLM models. Unlike policy documents that depend on user compliance, Deeploy guardrails enforce data protection rules automatically at the technical layer, creating a reliable security boundary between your users and external model providers.
About LLM Guardrails
LLM guardrails are automated filtering mechanisms that detect and remove sensitive content from chatbot inputs and outputs. Organizations need them because employees often inadvertently share personal information, confidential data, or regulated content with external LLM providers, creating compliance violations and security vulnerabilities that written policies alone cannot prevent.
Prompt engineering relies on instructions embedded in system prompts that models may ignore or users may bypass. Deeploy guardrails operate at the infrastructure layer, intercepting and filtering content before it reaches the model or returns to users, providing reliable technical enforcement independent of model behavior or user intentions.
Guardrails excel at detecting structured patterns like email addresses, prompt injection attempts, and known attack signatures. Deeploy supports both default regex patterns for common threats and custom patterns tailored to your organization’s specific needs, enabling detection of proprietary data formats and other specific information categories like phone numbers, credit card details, or internal identifiers.
The EU AI Act requires technical controls for data governance, transparency, and risk management in AI systems. Deeploy guardrails provide automated technical controls that filter sensitive data, prevent prompt injection attacks, and maintain detailed audit trails, directly supporting compliance requirements for high-risk AI applications.
Yes. Deeploy includes default guardrails specifically designed to detect prompt injection attempts and manipulative inputs. These guardrails identify patterns commonly used to bypass AI safety instructions or extract unauthorized information, neutralizing attacks before they compromise your system.
Deeploy logs the original request and response before guardrail processing, ensuring complete audit trails for compliance and security investigations. Users receive the filtered version, but system administrators can review prediction logs to see exactly what content was detected and removed by guardrails.
