PromptHalo provides runtime security for AI applications, detecting and blocking unsafe prompts before they reach the model. It integrates with application APIs, inference layers, and data sources to perform red‑team style threat analysis and enforce controls across agents, tools, and foundation models. The platform offers an interactive visualization of the security pipeline, helping developers ensure safe AI behavior in production environments.
Funding
Funding not disclosed
Founders
Product
Problem
AI applications can behave safely in controlled testing environments, but in production they encounter dynamic inputs, tool integrations, and multi-step decision flows that expose them to unauthorized actions, data leakage, and policy violations. Existing security measures often lack real-time analysis of prompts and model outputs, leaving organizations vulnerable to emerging AI threats.
Solution
PromptHalo provides a runtime security platform that monitors AI application traffic at the API layer, analyzing incoming prompts for known vulnerabilities and enforcing policy decisions before the request reaches the model. After inference, the platform inspects model responses for unsafe content or prohibited actions, allowing or blocking delivery based on configurable rules. The solution integrates with REST, gRPC, and orchestration interfaces, supporting any foundation model provider and covering agents, tools, and data sources. By combining red‑team style threat detection with automated enforcement, PromptHalo enables continuous protection of AI systems throughout their production lifecycle.
Target Audience
Primary customers are enterprises and developers deploying AI-powered chatbots, text generation interfaces, or autonomous agents that require continuous security oversight in production environments.
Features
- Real-time threat analysis of prompts using a blue‑team detection engine that flags risky inputs before model invocation
- Runtime enforcement at the inference layer to allow or block requests based on policy outcomes
- Post‑response inspection for unsafe content, unauthorized actions, or policy violations prior to delivery
- Compatibility with REST, gRPC, and orchestration APIs, enabling seamless integration into existing AI stacks
- Support for any foundation model provider, as well as associated agents, tools, and data stores (e.g., RAG, external APIs)
- Visual pipeline interface that maps request flow from application API through detection, enforcement, model inference, and response inspection