ZenGuard provides a real-time trust layer for AI agents. It is for developers and security teams who put language models into products and need to stop prompt attacks, data leaks and misuse while the agent is running, not after the fact.
The problem is that a model will act on whatever it is given and say whatever it produces, so an agent built on one can be manipulated by a crafted prompt or leak data in its answer. ZenGuard adds a check on both sides of that exchange.
At A Glance
| Vendor | ZenGuard (ZenGuard AI) |
|---|---|
| Category | AI guardrails and runtime protection for AI agents |
| Deployment | Cloud API, self-hosted or hybrid via Docker Compose, on-premise or VPC |
| Agentic AI | Runtime trust layer for AI agents |
| Licence | Python client under MIT |
| Market classification | AI Safety & Guardrails; also AI Model Security |
What ZenGuard Does
ZenGuard sits between an AI agent and the model it calls and checks traffic in both directions. A risky prompt is blocked before it reaches the model, and a risky response is stopped before it reaches the user. Because the detectors are modular, each one can be switched on, switched off or tuned for the use case at hand.
The service is model-agnostic and is built to be added to an existing agent with little code change, through a single API endpoint. Policy changes for any detector can be made from a UI or through the API, so security teams can adjust rules without a new release of the agent.
Products
ZenGuard Trust Layer
The Trust Layer is the core product: an API that exposes a set of configurable detectors which developers call from their own code.
| Prompt attacks | Finds and blocks prompt injections, jailbreaks and other manipulation in user inputs or reference material |
|---|---|
| PII | Lowers the risk of sensitive personal or confidential data appearing in inputs or outputs |
| Intended use | Allowed and banned topic detection keeps an agent to the job it was built for |
| Secrets | Spots keys, tokens and other secrets that should not be shared |
| Keywords | Flags custom keywords in prompts |
Python developers can also call the detectors through the MIT licensed zenguard client on PyPI, where the PII detection is tuned for customer experience use.
How It Works
Applications call the service directly over the API or through the Python client. Both prompts and responses pass through the chosen detectors before they continue.
| Component | What it does |
|---|---|
| Cloud API | Hosted endpoints that any AI agent can reach. |
| Self-hosted or hybrid | Deployment with Docker Compose, including on-premise or inside a customer VPC. |
| Detector API | Each detector has its own endpoint, for example https://api.zenguard.ai/v1/detect/prompt_injection, called with an x-api-key header. The response returns is_detected and a score giving the likelihood that the prompt is an injection attack. |
Integrations
| Area | Integrations |
|---|---|
| Models | OpenAI, Anthropic, Gemini, Llama, Mistral, DeepSeek |
| Frameworks | LangChain, LlamaIndex |
Company Background
| Founded | 2023 |
|---|---|
| Headquarters | San Francisco, United States |
| Funding | A reported $150K, with a convertible note as the latest round, from investors including Entrepreneurs Roundtable Accelerator, Sand Hill North, MOST Ventures Fund and TechOperators |
Note: This overview is compiled from ZenGuard’s publicly available information as of 3 November 2026. It is provided for information only. NHI Mgmt Group does not endorse any vendor, and nothing on this page represents the opinions of NHI Mgmt Group.