Generative AI has quickly become part of everyday work—from chatbots to writing or coding tools. However, this also increases the need to protect users, data, and the models themselves. Palo Alto Networks presents a dual approach: securing access to GenAI services and protecting the operation of models within organizations.
Why AI Security Is Urgent
Organizations today use thousands of generative AI applications in SaaS mode, while also running their own models for document processing, recruiting, or customer support. Attackers see an opportunity in this: they either abuse popular services and feed users malicious content, or try to penetrate through model interfaces directly into the company. It is no longer just about protecting individual files, but about the security of people, processes, and sensitive data flows.
The key is having oversight: who uses which tools, what data passes through them, and what threats are emerging. Without this visibility, it is difficult to make quick, informed decisions on whether to allow, restrict, or block a given application. This is where specialized tools come in, bringing together information about applications, users, and risks into a single control panel.
Access Control for GenAI: AI Security
The first line of defense focuses on the safe use of GenAI services. It leverages existing network infrastructure—from modern firewalls to SASE—and controls who accesses AI applications and how. Administrators gain visibility into the tools in use, the number of users, data transferred, identified threats, and whether the service is suitable for the enterprise environment.
The interface collects practical signals: does the application work with text, files, or images, does it use data for further training, and how does it fare on identity, privacy, and compliance? Based on this information, you can decide quickly right in the system—allow or block the application, or allow it only for selected teams. The goal is to maintain productivity while protecting sensitive information.
Securing Model Operations: AI Runtime Security
The second perspective focuses on the security of in-house applications and models. An API-driven environment provides an inventory of applications, agents, datasets, training bases, and models, while automatically searching for vulnerabilities and misconfigurations. It can, for example, detect a model or library with a known flaw or an agent with excessive permissions and offer direct remediation—restricting privileges or applying an update.
It also includes resilience testing: scanning agents, attempts at prompt injection, checking for possible data exfiltration, and other common weaknesses. The findings can be immediately turned into a policy that is deployed across the environment. The result is continuous visibility and control over the models the company uses, without the need to change development practices or build parallel systems.
