Fiddler Guardrails
Safeguard Enterprise Agents Within Your Own Secure Boundaries
Fiddler Guardrails is the industry’s fastest enterprise safety solution for enterprise AI guardrails that proactively moderates inputs and outputs in agentic systems.
With under 100ms response time, Fiddler Guardrails leverages the purpose-built, fine-tuned, and task-specific Fiddler Trust Models at runtime for high quality LLM scoring. Our solution immediately intercepts prompts and responses that fall outside accepted thresholds for hallucinations, safety violations, and jailbreak attempts, and enforces rules and policies, maintaining complete control of agent interactions.
Fiddler Guardrails Delivers Industry-Leading Security
Trust Models Built for the Fastest, Most Efficient, and Secure Guardrails
Fiddler Guardrails is powered by the Fiddler Trust Service — purpose-built Trust Models that score agent and LLM inputs and outputs in real-time, running entirely in your cloud and VPC environments. These "batteries-included" models remove the unpredictable, hidden costs of external API calls for agent and LLM evaluation. Because everything runs in your infrastructure, you get enterprise-grade protection, policy enforcement, and complete data privacy with full control over your environment.
These trust models, including Safety, PII, and Faithfulness models, help power Fiddler's AI Observability and Security solutions: Guardrails, LLM Observability, and Agentic Observability.
How Fiddler Guardrails Works
1.Implement Rapidly
Protecting agentic deployments using Fiddler Guardrails is easy.
Simply write three to five lines of code to initialize your HTTP client.
2.Connect to Fiddler Trust Models
Call the Fiddler Guardrails API, to our Trust models for moderating inputs and outputs.
3.Choose a Framework
Access our API using pre-built code examples in NodeJS, Python, or cURL — or any HTTP library of your choice.
Take advantage of our out-of-the-box integrations with NVIDIA NeMo Guardrails.
Fiddler Guardrails: Benchmark-Proven Industry Leader
We conducted a comprehensive benchmarks report comparing popular guardrails solutions across three critical dimensions — speed, cost, and accuracy — and three security metrics — jailbreak, toxicity, and faithfulness.The report findings show that Fiddler Guardrails is the fastest, most cost-effective, and most secure solution for agentic security across all metrics, making it the optimal choice for enterprise applications requiring:
- Task-specific, contextual use cases
- Latency-sensitive applications
- Cost-optimized deployments
- Fine-grained control and monitoring
The numbers prove our Guardrails' advantage:
Featured Resources
Frequently Asked Questions About Guardrails
What is a guardrail in AI?
A guardrail in AI refers to safety mechanisms designed to monitor, control, and restrict AI system behaviors to prevent harmful, biased, or unintended outputs. AI guardrails ensure that AI applications, especially large language models (LLMs), operate within defined ethical, legal, and security boundaries, maintaining trust and reliability in enterprise deployments.
What are the 3 general types of guardrails?
The four general types of AI guardrails typically include:
- Content Safety Guardrails: Prevent toxic, harmful, or inappropriate content generation.
- Security Guardrails: Protect AI systems from adversarial attacks such as prompt injections or jailbreaks.
- Accuracy Guardrails: Ensure outputs are accurate, reliable, and contextually relevant, preventing hallucinations or misinformation.
These guardrails form a comprehensive framework for responsible AI and LLM monitoring.
What is a guardrail in programming?
In programming, a guardrail is a control or safeguard built into software to prevent errors, security vulnerabilities, or misuse. In the context of AI and LLMs, programming guardrails often take the form of runtime checks, validation layers, and filters that regulate model inputs and outputs to uphold system integrity and compliance.
What are guardrail metrics for LLM?
Guardrail metrics for LLMs are measurable indicators used to assess large language model outputs' safety, reliability, and security. Common metrics include detection rates for:
- Jailbreak attempts that try to bypass restrictions.
- Toxicity and harmful content levels.
- Faithfulness and groundedness to source data.