The rapid pace of AI adoption has entered a new era: agentic AI. A recent Deloitte survey found that 74% of organizations plan to deploy agentic AI within two years. The push toward autonomous AI is fundamentally changing the browser from a passive tool that employees direct to an agent that acts on their behalf.
According to an EY survey of knowledge workers, 84% are eager to embrace agentic AI. With several agentic browsers already widely available, employees are actively experimenting with them. While these new tools enhance productivity, they also introduce new security, compliance, and accountability risks.
Security research has found vulnerabilities in several agentic browsers. The implications are significant because these tools often carry the same access privileges as their human users. When AI operates inside the browser with that level of permission, security strategy must adapt.
How agentic AI transforms the enterprise browser
A traditional browser is a reactive interface that displays content, one page at a time, in response to human input. The agentic browser no longer awaits instructions. It executes complex, multi-step tasks directly in the browser based on user-defined outcomes.
Consider, for example, a common sales workflow. A rep needs to check the status of several feature requests in Jira, update the corresponding Salesforce records, and send a client-facing email summarizing the status. In a traditional browser, this requires the employee to navigate between applications, copy data, and draft the email manually. With an agentic browser, the rep states the goal once, and the agent handles every step, moving between systems and acting without further input.
Enterprise uses for agentic browsers extend well beyond individual productivity gains. However, increased autonomy also heightens exposure and risk if workflows lack proper controls.
The risk of autonomy without guardrails
Nearly half of cybersecurity professionals believe that agentic AI and autonomous systems will become primary cyberattack targets, according to a recent industry poll. Today’s enterprise controls, such as data loss prevention (DLP), identity, access, and governance, cannot keep up with the risks introduced by AI agents.
The risks include:
- Security threats: Threat actors can hijack an agent using prompt injection without installing malware that traditional tools would detect. Security researchers have found several agentic browsers vulnerable to this technique, enabling remote attackers to take control.
- Data leakage: Agents can inadvertently pass sensitive internal data to external communications during a multi-task execution. In the earlier Jira/Salesforce scenario, nothing prevents the agent from including confidential Jira details in the client-facing email unless a human reviews it before it’s sent.
- Accountability gaps: AI agents behave like humans, and visibility tools struggle to differentiate human from agent activity. This creates challenges for governance, auditing, and compliance. Yet only 21% of organizations have a mature agentic AI governance model, the Deloitte survey found.
- Business risk: The agentic browser operates with user-level privileges, and an agent mistake may not trigger a security alert. Accidental deletion or overwrite of critical data could quietly cause operational failures.
- Shadow AI: Employees are adopting agentic browsers faster than enterprise browsers can support these workflows. Without a sanctioned tool, they may also turn to untrusted — and potentially malicious — browser extensions that mimic agentic behavior. This exposure to AI risk beyond IT oversight is already having a tangible impact, with one in five organizations experiencing attacks due to shadow AI tools.
Blocking agentic AI is not the solution. Security teams must extend existing controls and governance to agentic browsing.
Securing agentic browsing from the inside out
A purpose-built, secure enterprise browser addresses the risks of agentic AI by integrating DLP, identity, access controls, and governance, with agentic capability built on top. Think of it as an admin-controlled sandbox, where every agent action is examined and governed before execution.
Palo Alto Networks’ Prisma Browser is an example of this model, providing visibility into human versus agent activity and extending DLP to agentic behavior. Human-in-the-loop controls pause agent actions, such as sending an external email, pending user review. Built-in runtime security mitigates prompt injection by checking prompts and prompt responses before they leave the organization, and by reading web pages before the agent to prevent hijacking. Plus, Prisma AIRS introduces runtime security while adding another layer of topic and toxicity guardrails.
Unlike consumer agentic browsers that lock organizations into a closed AI ecosystem, Prisma Browser supports any large language model (LLM). This flexibility allows enterprises to choose vendors freely as the market evolves.
Security rules have changed
Agentic browsing is not a future scenario. Employees are using these tools today, and this growing trend creates urgency for organizations to respond.
Security leaders who adapt their strategies now can capture the benefits of higher productivity without introducing new risks. Those who wait will fall farther behind. Those who act will define the standard for secure agentic AI.
Discover how Prisma Browser embeds security and governance controls into agentic browsing, so you can adopt AI agents without sacrificing oversight.
