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NeuralFence sits between your applications or agents and their AI destinations. It checks who or what is making a request, evaluates the content against policy, routes approved traffic and inspects the response before it returns. The same gateway records the usage and policy outcome so teams can investigate what happened.
Point applications and agents to the gateway instead of distributing provider credentials across integrations.
Apply data protection and content checks to requests and responses.
Block, mask or log according to policy.
Send approved requests to the selected model or connected service and capture operational and security telemetry.
One team connects a chatbot to a cloud model. Another builds a RAG application. Developers give agents access to tools and enterprise systems. Each connection brings its own credentials, usage patterns and potential path for sensitive information.
Free QuoteNeuralFence brings these interactions into a common control point. Security teams can apply policies to the AI traffic routed through the gateway, while application teams continue working with the models and tools they need. For example, an internal assistant may receive a request containing a customer identifier and need access to an enterprise tool. NeuralFence can apply the configured data policy before the model request, verify access to a registered tool and record the policy outcome. The organization decides whether sensitive content is masked, the interaction is blocked or the event is logged.
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A single keyword list cannot account for the many ways sensitive information or malicious instructions can appear in AI traffic. NeuralFence combines these inspection approaches.
Identify known patterns, keywords and regulated identifiers.
Find sensitive information expressed in less structured text.
Help detect reworded prompt attacks and risky intent that simple pattern matching may miss.
They can invoke tools, connect to enterprise systems and communicate with other agents.
Its agent-to-agent gateway can register, authenticate and route A2A interactions while attributing usage to the originating caller.
Supports connections to providers and runtimes including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Ollama, vLLM and NVIDIA NIM.
Scope access to approved models and apply budgets and rate limits by key, user, team or organization.
Export metrics, traces and logs to supported observability and security systems.
Its self-hosted stack can run with Docker Compose, PostgreSQL, Prometheus, entity recognition and a local model runtime.
Evaluate a sensitive prompt, apply an access policy, route an approved model request and record the outcome.
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Apply guardrails before a request reaches a model and after a response is generated. Policies can block an interaction, mask sensitive content and continue, or log an event for review. Prebuilt identifier detectors and content categories cover examples such as credentials, payment information, national IDs, prompt injection, data exfiltration and harmful content. Teams can also define and version their own policies.
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