We Built a Chatbot Fluent in Network Security.

AI is everywhere today, but not every application of it has a really good use case. Did you know there’s an AI that writes cocktail recipes based on your mood and Spotify playlist? Entertaining? Absolutely. Do we need AI to do this? We’ll let you be the judge of that. 

Like giving you a custom recipe for dry martinis (shaken, not stirred), many AI applications seem like they’re layered onto tools without really solving a core problem. 

That kind of surface-level integration might be fine in novelty apps. But the stakes are high in network security. And the environments are complex, even a bit messy. Managing a network means navigating a maze of policies, platforms, and people. It requires deep insight and a way for teams to actually collaborate in real time.

That’s where AI shows real potential. It can see across systems, and doesn’t lose context between handoffs. It can sift through massive amounts of data in seconds. And when designed well, AI can give each team exactly the view they need.

And even more, it can offer the context, guidance, or automation to actually act on the information. Those strengths matter in network security, where teams are moving fast, managing risk, and working across fragmented environments.

Network access is an impactful AI chatbot use case

For example, when a user can’t get to a file or system, what seems like a simple issue can quickly turn into a multi-team investigation. IT gets the ticket first. They confirm the problem but can’t always see what’s blocking it. So the issue moves to Network Security, where someone digs into firewall rules, cloud security groups, and policy logic.

Both teams are working on the same issue. But they’re using different tools, speaking different operational languages, and looking at different slices of the environment. No one sees the full path. Your teams might try to solve the problem with dashboards, escalation paths, more documentation, but context still gets lost. 

What should take minutes drags into hours. Sometimes days, depending on the issue.
That is why we built TufinMate. It is a chatbot interface that allows people to ask network security questions in natural language and get clear, actionable answers.

Users can ask about traffic flows and access issues. TufinMate responds using live network data and real-time topology awareness. It connects directly to your environment to understand how traffic moves, what rules apply, and where things break down.

It gives IT, SOC, and Network Security teams a way to ask questions from the tools they already use. These include Microsoft Teams, Microsoft Security Copilot, and the Tufin platform itself. The responses are designed to be immediately useful.

For example, an IT team member could troubleshoot an access issue using Microsoft Teams. They could ask, “Why can’t this user reach that system?” and get back a direct answer, grounded in live policy and traffic path data. It goes even further than that. IT can actually resolve issues by executing a network change directly from Microsoft Teams.

SOC analysts use TufinMate in Microsoft Security Copilot to check if access to a vulnerable system is possible, identify overly permissive or unused rules, or validate compliance. That context helps them respond faster and with greater confidence.

Network Security teams use TufinMate as a guided interface into the Tufin knowledge center. They can search for answers, process steps, or policy guidance without leaving their workflow, freeing them up to focus on decision-making and design. It also doubles as a training tool for junior analysts, helping them get up to speed faster than they would on their own. All of this is done using a natural language interface, similar to LLMs people are using every day.  

Cutting down CVEs: A Sneak Peak into TufinAI

TufinMate is just the first step in a larger evolution. It’s where we began applying AI in a way that makes daily network security work faster, clearer, and more collaborative. But we’re not stopping at better answers.

We’re building toward something much more powerful: a system that doesn’t just respond to questions, but actively identifies issues, analyzes risk, and helps implement the right fix. We call this direction agentic network security: AI that doesn’t just explain what’s happening, but helps teams decide what to do next (and in some cases actually does it for them). You’ll get to know TufinAI more in the coming months as we roll out new features.

Take vulnerability management, for example. It’s not unusual for enterprise security teams to receive tens of thousands of CVEs from tools like Qualys or Rapid7. In the future, TufinAI will evaluate those vulnerabilities in real time, compare them to actual access paths in your environment, and identify which ones are truly exposed. It will prioritize what’s reachable, what’s high risk, and what needs action now.

The same system will help validate segmentation, assess the blast radius of a potential breach, or recommend compliant access changes, all backed by real policy and topology awareness. You’ll still stay in control. But the platform will take on more of the work. Not through generic automation, but through decisions grounded in how your network actually functions.

That’s the vision behind TufinAI. And TufinMate is just the start.