What are Guardrails
Guardrails are safety mechanisms that monitor, filter, and control content flowing through your AI model endpoints. They act as intelligent gatekeepers that analyze both user inputs and model outputs to detect and prevent harmful, inappropriate, or policy-violating content.
Why Use Guardrails
AI models can generate unpredictable outputs, and users may submit malicious or inappropriate inputs. Guardrails help you:
- Protect users from harmful content including hate speech, violence, and self-harm
- Ensure compliance with organizational policies and regulatory requirements
- Prevent misuse by blocking prompt injection attacks and jailbreak attempts
- Maintain brand safety by filtering content that could damage reputation
- Detect sensitive data like PII before it’s processed or leaked
How Guardrails Work
Guardrails operate at two critical points in the request/response flow:
User Request → [Input Guard] → Model → [Output Guard] → Response
- Block malicious prompts and injection attacks
- Filter toxic or inappropriate user content
- Detect and redact sensitive information (PII)
Output Guards analyze model responses before returning them:
- Filter harmful or inappropriate generated content
- Detect hallucinations and factual errors
- Ensure responses align with safety policies
Supported Providers
Bud Stack supports multiple guardrail providers, giving you flexibility to choose based on your requirements:
Bud Sentinel (Proprietary)
Our in-house guardrail solution offering comprehensive protection with:
- High-performance gRPC-based communication
- Extensive probe library for various threat categories
- Customizable severity thresholds and rule configurations
- Profile-based management for consistent policies across endpoints
Bud Sentinel is a proprietary service. While the deployment stack includes references to the Bud Sentinel container image, it requires a separate license. Contact sales for access to the gated container registry.
Cloud Providers
Third-party guardrail services from major cloud providers:
| Provider | Models | Best For |
|---|
| OpenAI | text-moderation-latest, omni-moderation-latest | General content moderation with broad category coverage |
| Azure Content Safety | azure-content-safety-text | Enterprise environments with Azure integration |
Core Concepts
Probes
Probes are detection mechanisms that identify specific types of vulnerabilities or threats. Each probe contains:
- Rules: Specific patterns or behaviors the probe detects
- Guard Types: Whether the probe applies to inputs, outputs, or both
- Modality: The content type the probe analyzes (text, image, etc.)
Profiles
Profiles are user-defined configurations that combine multiple probes into a coherent guardrail policy. Profiles allow you to:
- Select which probes to enable
- Set severity thresholds (profile-wide or per-probe)
- Enable/disable specific rules within probes
- Define guard type behavior
Deployments
Deployments activate guardrail profiles on your inference endpoints. You can:
- Deploy to specific endpoints for targeted protection
- Create standalone deployments for batch processing
- Manage multiple deployments per profile
Execution Modes
When multiple providers or probes are configured, guardrails can execute in different modes:
| Mode | Behavior |
|---|
| Parallel | All probes run simultaneously for faster execution |
| Sequential | Probes run in order, allowing early termination |
| Mode | Behavior |
|---|
| Fail Fast | Stop immediately when content is flagged |
| Best Effort | Continue execution and aggregate all results |
When to Use Guardrails
Guardrails are essential for:
- Customer-facing applications where users interact directly with AI
- Sensitive domains like healthcare, finance, or legal
- Multi-tenant platforms requiring consistent safety policies
- Regulated industries with compliance requirements
- High-risk use cases where model outputs have significant impact
Next Steps
