FireMon Agentic AI is a new solution that changes the way businesses analyze network access, make policy decisions, and prepare security changes. Integrated into FireMon’s network security management solutions, the new agentic AI capabilities position agents as full participants alongside security professionals, putting them to work on complex, multi-step tasks using policy data, context, and the tools professionals already rely on. Customers can use the agents FireMon provides, create their own, or connect external agentic workflows through the FireMon MCP server.
Network security teams face a rising volume of policy decisions that involve diverse firewall vendors, cloud environments, and segmentation technologies. A single business access request can affect multiple devices and policies, with different technically valid methods for implementation. Engineers must gather data, evaluate alternatives, and coordinate proposed changes before anyone can approve them. As business demands faster decisions, this manual coordination, which requires specialized knowledge, slows implementation times.
FireMon Agentic AI transforms this process. The agents handle the analysis and planning phases, collect the relevant evidence, and formulate recommendations within their assigned scope. Professionals steer the work and make decisions that require organizational judgment. This change gives teams greater capacity to manage the complexity of policies and respond to business needs, while the organization maintains control over access and changes.
«Network security has never been this complex, and the answer isn’t to expand the infrastructure, but to leverage the policy context FireMon has developed over 25 years», said Jody Brazil, founder and CEO of FireMon. «Customers can deploy our agents, build their own, and integrate FireMon into their broader, agent-based workflows, while professionals continue to handle decisions that require human judgment».
A New Way to Harness Strategic Information
Agents are at the heart of FireMon’s Agentic AI approach. They operate on instructions, competencies, tools, and assigned permissions, leveraging policy data and operational capabilities that form the core of FireMon’s Network Security Policy Control for the Modern Enterprise. This foundation provides both the context needed to understand a request and the tools to execute the work, from policy log searches and traffic path identification to calculating recommendations and preparing changes in the Policy Plan.
Customers can integrate FireMon Agentic AI into their operations in three ways. They can use the agents provided, customize them, or create new ones in-house, or connect external agentic workflows through the Model Context Protocol (MCP). This gives customers full control over the tasks they automate and how they fit into their processes, delivering value that goes far beyond the initial examples of agents.
Faster Decisions and Less Manual Coordination
The agents included with the platform demonstrate how this underlying technology transforms everyday network security tasks, saving thousands of hours of work while also increasing the accuracy of decisions related to network security policies:
- Analyze policies without writing queries. The FireMon Intelligent Agent searches and analyzes policy data, creates and runs queries in Security Intelligence Query Language (SiQL), and explains the results. Specialists can verify which rules allow access to a service and review policy context without manually configuring searches.
- Turn business access requirements into concrete recommendations. The Network Designer agent analyzes an access requirement and formulates firewall policy recommendations. In the case of an application requiring HTTPS access to a reporting service, it identifies the recommended changes that the engineer will need to evaluate, thereby reducing the analytical work needed before deciding how to grant access.
- Prepare coordinated changes across all devices and policies. The Policy Change Designer agent manages an access request through discovery, recommendation formulation, and change preparation in the Policy Plan. It fills in required fields, such as rule names, and stops when an operator must choose among valid alternatives. Nothing is saved until the user reviews and accepts; the proposal can be discarded or requested changes. Upon acceptance, the proposal is saved in the workflow, but it is not automatically deployed to the firewalls.
These examples illustrate a practical hurdle in automating policy. More than one policy may satisfy an access request, and an object name, while technically valid, may not meet the organization’s conventions. FireMon combines policy context, agent instructions, and specialized tools to prepare work that reflects these requirements and to provide professionals with the most suitable options.
