An AI firewall controls what reaches AI tools and agents. Strac is the data firewall for AI: it redacts and blocks PII, PHI, and secrets at the browser, endpoint, and MCP before they reach ChatGPT, Claude, Gemini, or any AI agent.
Short answer: An AI firewall is a security control between AI and your data that decides what is allowed. Strac is the data firewall for AI: it sits at the AI boundary, in the browser extension, the endpoint agent, and across MCP DLP, and redacts or blocks sensitive data like PII, PHI, card numbers, and secrets before it ever reaches ChatGPT, Claude, Gemini, Copilot, or any AI agent.
An AI firewall is the control layer that sits between AI tools or agents and the people and data they touch, and decides what is allowed through. As teams adopt ChatGPT, Claude, Copilot, and autonomous agents, an AI firewall is how security teams keep sensitive data and risky actions under control without blocking AI outright.
The term covers two different controls, and most organizations need both:
| Type | What it protects against | Examples |
| Network / prompt firewall for AI | Prompt injection, jailbreaks, abusive traffic, malicious output | Network-security vendors |
| Data firewall for AI | Sensitive data (PII, PHI, PCI, secrets) flowing into or out of AI | Strac |
A network firewall for AI watches the traffic for attacks. A data firewall watches the data, and stops regulated or confidential data from reaching the model in the first place. They solve different problems, so running both is common.
Strac enforces the data firewall at every place AI touches your data, under one policy:
A firewall is also about access, not just content. Strac gives each AI agent only the data its task needs: it governs what an agent can reach over MCP, redacts sensitive values in every tool response, and lets you cut an agent’s access in one step. That is agent access control and data loss prevention in one layer. See MCP gateway and agentic AI security.
Strac ships 100+ detectors and finds sensitive data even inside images, screenshots, and documents using OCR and ML:
An AI firewall only works on the AI you know about. Strac surfaces shadow AI, the unsanctioned AI tools employees adopted, so the data firewall covers the whole AI surface, not just approved tools.
AI adoption moved faster than the controls around it. Employees paste customer records into ChatGPT, developers drop source code into Claude, and autonomous agents pull data from your systems over MCP, all in channels traditional DLP never watched. The risk is specific:
An AI firewall closes these gaps by controlling the data at the AI boundary, so your team keeps using AI without sending regulated data to someone else’s model.
Where a data firewall for AI earns its place:
| Use case | What the firewall does |
| Support teams using AI | Redacts customer PII and card data before it reaches an AI assistant drafting replies |
| Developers with AI coding tools | Blocks API keys and proprietary source code from going into Claude Code, Copilot, or Cursor |
| Healthcare and fintech | Keeps PHI and PCI out of ChatGPT and Gemini, with an audit trail for HIPAA and PCI DSS |
| AI agents over MCP | Redacts sensitive values in tool responses before an agent or model sees them |
| Enterprise AI rollout | One policy plane to deploy ChatGPT, Claude, and Copilot at scale without data exfiltration |
Not all AI firewalls do the same job. Use this checklist:
Most teams are enforcing across multiple AI tools within the first hour, with nothing to re-route at the network layer.
These overlap. AI guardrails are the broad set of controls (content, accuracy, access, data) that keep AI safe; a data firewall is the data guardrail, enforced at the boundary; AI DLP is the detection-and-redaction engine underneath. Strac is all three for the data layer: the data firewall for AI, the data guardrail, and the AI DLP that powers them.
Ready to put a data firewall in front of every AI tool and agent? Book a Strac demo.
An AI firewall is DLP applied at the AI boundary. Traditional DLP watched email, network, and files; an AI data firewall extends the same detect-and-redact logic to AI chat, pastes, uploads, screenshots, and agent tool calls, where data now actually leaks.
Usually yes. A network firewall for AI blocks attacks and abusive traffic but does not stop your own employees or agents from sending sensitive data to a model. A data firewall for AI does, so many teams run both.
With Strac, most teams are enforcing across multiple AI tools within the first hour. There is no network re-architecture: you connect the browser extension, the endpoint agent, and MCP for agents, then set policy.
An AI firewall is a security control that sits between AI tools or agents and the people and data interacting with them, and decides what is allowed. There are two kinds: a network or prompt firewall that filters prompts and model output for attacks and abuse, and a data firewall that stops sensitive data from flowing into or out of AI. Strac is the data firewall.
A data firewall for AI inspects the data moving to and from AI tools and agents, and redacts, blocks, warns, or allows it by policy, so sensitive data like PII, PHI, card numbers, and secrets never reaches the model. Strac enforces this at the browser, the endpoint, and across MCP, so it covers every AI tool, not one approved model.
They describe the same idea at different scopes. An LLM firewall is scoped to a large language model; an AI firewall is the broader term covering any AI tool, including agents and embedded AI features. A data firewall like Strac applies to both.
A network or prompt firewall filters prompts and output for attacks, but does not stop your sensitive data from being sent to the model. Stopping data leaks requires a data firewall that inspects what is being sent. That is what Strac does: it redacts or blocks PII, PHI, and secrets before they leave your environment.
Network firewalls for AI (and prompt firewalls) focus on blocking attacks, prompt injection, and abusive traffic. Strac focuses on the data: it discovers and redacts sensitive data at the AI boundary across the browser, endpoint, and MCP, and governs what AI agents can access. Many teams run both, a network firewall for attacks and Strac for data.
Yes. Strac sits in the MCP path between an AI agent and your tools as a reverse proxy, and redacts sensitive values in tool responses before the model sees them, agentlessly. See MCP DLP and MCP gateway.
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