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AI Guardrail

As generative AI becomes widely adopted, large language models (LLMs) are rapidly entering key business scenarios, including customer service, knowledge management, and data analysis. However, potential risks such as erroneous outputs, overstepping of boundaries, and leakage of sensitive information pose challenges to enterprise data security and compliance. Ensuring flexibility while achieving controllable and trustworthy LLM applications has become a core consideration in deploying AI.

AI Guardrail provides comprehensive risk protection across the entire LLM application lifecycle, from input checking and intent recognition to output auditing and sensitive information filtering. It delivers full‑cycle protection through “preventive blocking, in‑process guidance, and post‑output auditing”.

The platform supports enterprises in customizing safety policies and flexibly adapting to various LLMs and application scenarios. AI Guardrail effectively improves the compliance and controllability of AI applications, prevents sensitive data leakage, and mitigates inappropriate responses, laying the foundation for secure and reliable LLM deployment.

Principles of Input and Output Detection

Principles of Input and Output Detection

Highlights

    • Input Detection Mechanism: Includes 15 built-in categories of input detection strategies to precisely identify sensitive intentions such as unauthorised access, inducement, and attacks, blocking high-risk questions.
    • Intent Recognition and Response Strategies: Combines semantic matching with knowledge base validation to ensure replies are truthful, reliable, and contextually appropriate
    • Output Risk Review: Covers 15 types of output risks, including violent content, privacy leakage, code outputs, and sensitive variants, preventing inappropriate information leaks
    • Sensitive Information Filtering Engine: Supports customization of sensitive word rules and processing strategies, automatically replacing, deleting, or anonymizing violating content.
    • Custom Strategy Support: Enables flexible configuration of protection workflows based on custom security policies
    • Compatibility with Major Models: Adaptable to mainstream models such as OpenAI, Qwen, and DeepSeek, with support for various API access methods
    • Containerized Deployment: Supports deployment on Kubernetes (K8s) clusters, enabling concurrent execution of multiple tasks and horizontal scaling.

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