AI Guardrail
for Enterprise LLMs
Enterprise AI and LLM guardrails for chatbots and AI agents: prompt-injection detection, data masking and audit logs across GPT, Claude and Gemini.
What is an AI guardrail (LLM guardrail)?
An AI guardrail (also called an LLM guardrail) is a security layer that sits between users and a large language model (LLM) and inspects what goes in and what comes out, so the model only operates within your security policy. It stops sensitive data from leaving for external models, detects prompt injection that tries to override the model's instructions, and checks responses for personal data or inappropriate content before they reach users.
KYRA AI Guardrail is an enterprise AI security guardrail deployed as a proxy in front of your existing LLM applications or API gateway. It inspects the inputs and outputs of internal generative AI chatbots and AI agents through layered input filtering, prompt validation, isolated execution, output inspection, audit logging and risk assessment, and applies the same policy to GPT, Claude, Gemini and other models. Every block and masking action is recorded in the dashboard and audit log.
Why enterprises need an AI guardrail
- Prompt injection is the first entry in the OWASP Top 10 for LLM Applications (2025). Firewalls and WAFs cannot tell a natural-language attack from a normal request.
- Personal data, customer records, source code and API keys typed into prompts can leak to external LLMs unless they are caught at the input stage.
- RAG chatbots and AI agents that read internal documents, web pages or email are exposed to instructions hidden in that content (indirect prompt injection).
- AI regulation, including Korea's AI Basic Act (in force since January 2026) and the EU AI Act, makes records of AI use a compliance requirement.
Architecture
Multi-Layer AI Guardrails for
Safe LLM Adoption
Multi-layer security validates inputs, executes AI models in isolated environments, and verifies outputs to deliver only safe responses. Malicious prompts and dangerous outputs are automatically blocked.
Before vs. After Guardrail
Security Pipeline
Prompt Injection Defense Pipeline
A 6-stage security verification pipeline from input to output ensures safe execution of AI models.
Security Layers
Layered LLM Guardrails,
Protecting Every Step
Input Filter
Sensitive data masking, input validation
Prompt Validation
Injection detection, jailbreak blocking
Isolated Execution
Guardrail environment, resource limits
Output Inspection
Result verification, sensitive data detection
Audit Logging
Full history recording, analysis
Risk Assessment
Comprehensive scoring, reporting
Features
AI Guardrail Features
Isolated Execution
Relays and executes LLM requests safely inside a container-based isolated environment.
Network isolation, resource limits, access control
Prompt Security
Detect and block malicious prompt injection attacks.
Jailbreak attempts, system prompt leak prevention
Output Monitoring
Monitor AI model outputs in real-time and filter dangerous content.
Sensitive data leak detection, inappropriate content blocking
Data Masking
Automatically detects and masks sensitive data such as PII and secrets before it reaches the LLM.
Pattern- and regex-based detection with response restoration
Multi-Model Support
Connects GPT, Claude, Gemini and other LLMs securely through a single guardrail.
Apply security policy across every model at once
Compliance
Provides audit logs and access control for AI regulations and corporate policy.
EU AI Act and domestic AI regulation ready
Use Cases
Generative AI Security
Use Cases
Sensitive-data masking, prompt filtering, response validation, and audit logging — AI Guardrail covers every security requirement of enterprise LLM adoption.
Enterprise LLM Rollout
Adopt LLMs for internal chatbots and workflow automation while blocking sensitive-data leakage at the source.
Sensitive-Data Protection
Automatically masks PII and secrets in prompts and responses to prevent data exposure.
AI Regulatory Compliance
Secures the audit logs and access controls needed for EU AI Act and similar regulations.
Compliance Assurance
Records and audits every conversation securely to demonstrate regulatory compliance.
Integration
Add Guardrails to Your Existing LLM Apps
Drops in front of your existing LLM applications and API gateways to protect traffic in real time.
Gateway Integration
Deploys as a proxy in front of your existing LLM call path and inspects all traffic.
API Support
Integrate with various tools via REST API.
Dashboard
See real-time security posture and audit logs at a glance.
Report Generation
Automatically generate detailed security analysis reports.
See KYRA AI Guardrail in action
FAQ
AI guardrail and LLM guardrail FAQ
Are AI guardrails and LLM guardrails the same thing?
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Yes. Because generative AI services are built on LLMs, the terms are used interchangeably. Both mean a control layer that inspects a model's inputs and outputs to prevent security and policy violations.
Do AI agents need guardrails too?
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Yes. AI agents read documents, email and web pages and call tools, so instructions hidden in that content (indirect prompt injection) can make them act in unintended ways. Inspecting agent inputs and outputs with a guardrail, and limiting the tools and data an agent can reach, are the basic safeguards.
What should I look for in an LLM security solution?
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Check that it inspects both inputs and outputs, applies one policy across multiple LLMs, integrates without major application changes, lets you define sensitive-data rules and blocking actions, and records history in audit logs and a dashboard.
How does KYRA AI Guardrail stop prompt injection?
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At the input stage it detects known injection and jailbreak patterns and attempts to extract the system prompt, and blocks them. At the output stage it checks the response again for sensitive data and inappropriate content. Blocked requests are written to the audit log and shown in the dashboard.
Do I have to change my existing LLM applications?
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Not significantly. KYRA AI Guardrail runs as a proxy in front of your existing LLM call path or API gateway and inspects the traffic. A REST API is available for integrating other tools.
Which LLMs are supported?
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GPT, Claude, Gemini and other LLMs can be connected through a single guardrail, with the same security policy applied to every model.
How is sensitive data handled?
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Personal data, secrets and credentials are detected with pattern and regex rules and masked before the prompt reaches the LLM. Administrators define the detection rules and the masking or blocking action for each severity.
How is it different from open-source guardrail frameworks?
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Open-source guardrail frameworks are usually libraries that developers embed in application code. KYRA AI Guardrail runs outside the application as a proxy, so the security team can manage policy for many services and models in one place and review history in a dashboard and audit log.
How does it help with AI regulation?
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It keeps audit logs of every prompt, response and detection, and provides access control, giving you the records of AI use and the evidence for explanations that regulations such as Korea's AI Basic Act and the EU AI Act call for.
Need Safe
LLM Adoption?
Adopt enterprise AI without sensitive-data leakage with KYRA AI Guardrail.