The adoption of artificial intelligence is entering a new phase: agentic AI. A recent survey by Deloitte shows that 74% of organizations plan to deploy agentic AI within two years. This trend is fundamentally reshaping the role of the browser—it is no longer just a passive tool operated by employees, but an intelligent agent that can autonomously execute tasks on behalf of users.

A survey by EY of knowledge workers found that 84% of respondents have positive expectations for agentic AI. Currently, multiple agentic browsers are widely available, and employees are actively trying them out. While these new tools can boost productivity, they also introduce new security, compliance, and accountability risks.

How agentic AI is changing the enterprise browser

A traditional browser is a responsive interface that displays content page by page based on manual input. An agentic browser, in contrast, does not wait for instructions but directly executes complex, multi-step tasks within the browser based on goals set by the user.

Take a common sales process as an example: a sales representative needs to check the status of several feature requests in Jira, update corresponding Salesforce records, and send a summary email to the customer. In a traditional browser, the employee would need to switch between multiple applications, manually copy data, and draft the email. With an agentic browser, the representative only needs to state the goal once, and the agent automatically handles all steps, operating across systems without further input.

The enterprise use of agentic browsers goes far beyond enhancing individual productivity. However, without proper controls in workflows, greater autonomy also amplifies the attack surface and risks.

Risks of unguarded autonomy

According to a recent industry survey, nearly half of cybersecurity professionals believe that agentic AI and autonomous systems will become primary cyberattack targets. Current enterprise controls, such as data loss prevention (DLP), identity, access, and governance mechanisms, are struggling to address the new risks posed by AI agents.

Specific risks include:

  • Security threats:Attackers can hijack agents through prompt injection without needing to install malware detectable by traditional tools. Security researchers have found this vulnerability in multiple agentic browsers, allowing remote attackers to take control.
  • Data leakage:During multi-task execution, agents may inadvertently pass sensitive internal data to external communications. In the Jira/Salesforce scenario above, if no human reviews before sending, the agent could include confidential details from Jira in the customer email.
  • Accountability gaps:AI agents behave similarly to humans, and visibility tools struggle to distinguish between human and agent activity, posing challenges for governance, audit, and compliance. Yet, the Deloitte survey shows that only 21% of organizations have mature agentic AI governance models.
  • Business risks:Agentic browsers operate with user-level permissions, and erroneous actions by agents may not trigger security alerts. Accidental deletion or overwriting of critical data could silently lead to operational failures.
  • Shadow AI:Employees are adopting agentic browsers faster than enterprise browsers can support these workflows. In the absence of official tools, they may turn to untrusted—or even malicious—browser extensions that mimic agent behavior. This AI risk beyond IT oversight has real-world impact, with one in five organizations having suffered attacks due to shadow AI tools.

Banning agentic AI is not the solution. Security teams must extend existing controls and governance measures to agentic browsing scenarios.

Securing agentic browsing from the inside out

A purpose-built secure enterprise browser, which integrates DLP, identity, access control, and governance, and builds agentic capabilities on top of it, can address the risks of agentic AI. Think of it as an administrator-controlled sandbox where every action of the agent is checked and governed before execution.

Palo Alto Networks' Prisma Browser exemplifies this model: it provides visibility into human and agent activity and extends DLP to agent behavior. Human-in-the-loop controls can pause agent actions (such as sending external emails) pending user review. Built-in runtime security mitigates prompt injection—checking prompts and responses before they leave the organization and reading web pages before the agent accesses them to prevent hijacking. Additionally, Prisma AIRS adds an extra layer of topic and toxicity guardrails while enhancing runtime security.

Unlike consumer-grade agentic browsers that lock organizations into closed AI ecosystems, Prisma Browser supports any large language model (LLM). This flexibility enables enterprises to freely choose vendors as the market evolves.

The security rules have changed

Agentic browsing is not a future scenario. Employees are already using these tools daily, and this growing trend requires organizations to act promptly.

Leaders who adjust their security strategies now can reap higher productivity while introducing new risks. Those who wait will fall further behind, while those who act will define the standard for secure agentic AI.

Learnhow Prisma Browser embeds security and governance controls into agentic browsing, enabling you to adopt AI agents without losing oversight.