AI applications introduce new safety, security, and compliance risks when deployed at scale. Common issues include prompt injection, data leakage, toxic content generation, and hallucinations. Without proper controls, these risks can lead to regulatory violations, loss of trust, and operational failures.
AI gateways with built-in guardrails address these challenges by acting as a centralized control layer between applications and foundation models. Instead of embedding safety logic into every application, teams can enforce policies, validate content, and monitor behavior at the gateway level.
Why Guardrails Are Critical in Production AI
As AI systems move into real-world use, organizations must manage multiple risk categories:
- Security risks - Prompt injection and jailbreak attempts can override system instructions or expose sensitive prompts
- Data privacy risks - Models may generate or reveal personally identifiable information (PII) such as financial or health data
- Content safety risks - Harmful or misleading content can be produced without strong filtering mechanisms
- Compliance risks - Regulated industries require audit trails, content moderation, and strict data handling controls
Implementing guardrails at the gateway layer ensures consistent enforcement across all AI interactions while allowing teams to iterate on applications without reimplementing safety logic each time.
1. Bifrost by Maxim AI
Bifrost is an open source LLM gateway built in Go, designed for high-performance and enterprise-grade deployments. The core gateway is free and open source, while advanced guardrails capabilities are available in the enterprise edition.
Guardrails Capabilities
Bifrost integrates with leading guardrail providers to deliver comprehensive protection:
- AWS Bedrock Guardrails - Content filtering across harmful categories, PII detection and redaction for 50+ entity types, and contextual grounding checks
- Azure Content Safety - Severity-based content moderation, Prompt Shield for jailbreak detection, and groundedness checks
- Patronus AI - Hallucination detection, toxicity screening, and prompt injection defense with customizable policies
How It Works
Guardrails can be applied at both input and output stages using flexible configuration. Teams can attach guardrails to specific API calls or enforce defaults globally. Validation can run synchronously or asynchronously based on latency needs.
Supported actions include:
- Block unsafe requests or responses
- Redact sensitive data
- Log violations for audit and analysis
Enterprise Readiness
The Bifrost enterprise version adds clustering, high availability, secure secret management, adaptive load balancing, and detailed audit logs. Organizations can start with a free 14-day enterprise trial.
2. Portkey
Portkey offers an AI gateway with more than 60 built-in guardrails. It is available as a managed cloud service as well as an open source gateway.
Key Features
- Input validation - Detects prompt injection, malicious intent, and off-topic queries
- Output validation - Filters harmful content and prevents PII leakage
- Ecosystem integrations - Works with providers like Palo Alto Networks AIRS and supports custom webhooks
Portkey also enables routing decisions based on guardrail outcomes, such as retrying with different models or blocking requests entirely. Teams get real-time visibility into violation rates, latency impact, and guardrail effectiveness.
3. LiteLLM
LiteLLM is an open source LLM gateway focused on flexibility and customization. It supports guardrails through both built-in mechanisms and third-party integrations.
Guardrails Execution Stages
LiteLLM allows validation at multiple points:
- Pre-call - Validate prompts before model invocation
- During-call - Run checks in parallel with inference
- Post-call - Inspect responses before returning them to applications
Built-in and External Guardrails
Built-in features include keyword blocking and regex-based detection for sensitive data such as emails, API keys, and identification numbers. LiteLLM also integrates with providers like AWS Bedrock Guardrails, Guardrails AI, Aporia, and Lakera.
Granular Policy Control
Different guardrail policies can be applied per API key or project, making LiteLLM suitable for multi-tenant environments with varying safety requirements.
4. AWS Bedrock Guardrails
AWS Bedrock Guardrails is a standalone content validation service that works with any foundation model, whether hosted on AWS or externally.
Core Capabilities
- Content filters - Configurable detection of hate speech, violence, sexual content, and misconduct
- Denied topics - Natural language definitions of prohibited subjects
- Word filters - Custom blocked terms and patterns
- PII redaction - Identification and masking of sensitive personal data
- Contextual grounding - Ensures responses align with provided source documents
- Automated reasoning checks - Verifies outputs against formal policies for compliance use cases
The ApplyGuardrail API allows teams to validate content independently of model inference, enabling multi-stage safety workflows.
5. Azure Content Safety
Azure AI Content Safety provides text and image moderation through Microsoft’s cognitive services platform.
Detection Features
- Severity-based classification for hate, sexual content, violence, and self-harm
- Prompt Shield for detecting jailbreaks and indirect attacks
- Groundedness detection to reduce hallucinations
- Detection of protected or copyrighted material
Organizations can also define custom categories using natural language descriptions to enforce internal content policies.
How to Choose the Right AI Gateway
The right gateway depends on your technical and operational needs:
- Latency and performance requirements
- Compatibility with your model providers and guardrail services
- Deployment preferences - Managed, self-hosted, or hybrid
- Ease of integration with existing auth, logging, and monitoring systems
- Cost considerations, including caching and routing efficiencies
For teams that need high-performance guardrails with strong enterprise features, Bifrost offers a flexible open source foundation with an upgrade path for production scale.
Implementing Guardrails at Scale
Technology alone is not enough. Effective guardrails require:
- Clearly defined safety and compliance policies
- Baselines for acceptable content across use cases
- Risk-based actions for different violation types
- Ongoing monitoring to reduce false positives
- Feedback loops to improve prompts and models over time
Maxim AI’s platform helps teams observe and evaluate guardrail performance alongside other AI quality metrics across the full lifecycle.
Ready to secure your AI applications? Start with Bifrost for centralized, enterprise-grade guardrails, or schedule a demo to see how Maxim AI can help you build and monitor reliable AI systems.
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