Use AI-based browsers at your own risk

Key Takeaways

  • Gartner recommends blocking AI browsers due to risks ranging from data leaks to autonomous actions on malicious sites.
  • Agentic browser defaults often favor convenience, frequently sending browsing data to cloud back ends.
  • Analysts warn that user behavior and automation misuse can worsen exposure, even in compliant environments.

Gartner is advising organizations to block AI browsers, arguing that the current generation of “agentic” tools creates a wide and poorly understood security gap. It’s a strong recommendation—especially coming from researchers Dennis Xu, Evgeny Mirolyubov, and John Watts—and it immediately raises a practical question for CISOs: what exactly are these browsers doing behind the scenes?

According to the analysts, AI browsers and extensions powered by large language models rely heavily on autonomous navigation, workflow automation, and data collection to help users move faster through complex online tasks. These functions depend on the browser’s embedded AI model interpreting, summarizing, and manipulating web content on the fly. That sounds useful, and for many teams it has been. But it also opens the door to a scenario where malicious webpages manipulate the AI agent itself, coaxing it into gathering and exfiltrating sensitive data such as bank information, login credentials, or emails. It’s a small detail, but it tells you a lot about the risk surface: once an AI agent is the one clicking through the page, traditional user-awareness defenses don’t help much.

The analysts point to two defining traits of agentic browsers. First, they interact with web content using the developer’s AI model to provide summarization, translation, search, and data‑extraction capabilities. Second, and more concerning from a security lens, they can perform autonomous actions on websites—including within authenticated sessions. That’s where it gets tricky. If an AI agent has access to a logged‑in environment, the set of things it can accidentally or maliciously do is much larger than what most organizations anticipate.

Another challenge highlighted by Gartner is architectural. Many AI browsers don’t allow users to run the AI features locally. Instead, the browser sends page content, browsing history, open tabs, and other contextual data to the vendor’s cloud‑based AI back end. The analysts note that unless privacy and security settings are deliberately tightened and centrally enforced, this creates a direct path for unintended data exposure. Some organizations might assume they’re safe because they’ve performed a vendor review or checked compliance boxes. Even so, the analysts argue that architectural trust doesn’t fully solve the behavioral issues.

That’s because an employee can unintentionally expose sensitive information by simply having confidential material open in another tab while using the AI assistant to summarize a report or walk through a workflow. The AI sidebar, doing what it’s designed to do, may process context from the entire browser environment. One small mistake becomes a leak vector. It’s the kind of user‑driven exposure that’s hard to mitigate with policy alone.

The risks aren’t limited to data handling. Gartner warns that because these browsers can execute tasks automatically, employees may be tempted to use them to offload mandatory, repetitive work—including cybersecurity training. It’s a scenario that feels almost too on‑the‑nose, but it highlights a tension security teams have been watching closely. Automated tools aren’t just speeding up work; they’re beginning to displace verification steps meant to ensure humans actually understand organizational policies. And if staff begin treating compliance tasks as automated chores, the downstream impact on awareness could get messy.

For organizations that decide not to ban AI browsers outright, the firm suggests user education as a minimum safeguard. Employees should understand that anything visible in the browser might be sent to the AI back end. That sounds simple, but it’s a big shift in mental models for teams accustomed to traditional browsers that keep local content local unless explicitly shared.

Javvad Malik, Lead Security Awareness Advocate at KnowBe4, echoed the Gartner team’s concerns while adding some nuance. He notes that AI features are creating a very real tension between productivity and security, and that the default configurations in these emerging tools usually skew toward making the experience smooth rather than locked down. It mirrors patterns seen in earlier technology waves, where convenience and rapid adoption outran security hardening.

Still, Malik argues that broad bans rarely hold up over time. He recommends risk assessments focused on the specific AI services powering each browser to determine where controlled adoption might be possible. It’s a more measured stance, though he agrees that organizations need playbooks for assessing and protecting AI agents as they become embedded across technology stacks. It’s worth noting that analysts across the industry—from Gartner to groups such as ENISA—have been increasingly calling for closer examination of AI‑driven automation in enterprise tools. Not because the technology is inherently unsafe, but because the guardrails are still evolving.

A micro‑tangent here: the debate feels similar to early BYOD policies, where businesses initially defaulted to bans before shifting toward structured enablement. The same push‑and‑pull between user expectations and risk appetite is surfacing again, just with a different layer of automation making the decisions.

The Gartner advisory, published via The Register, lands on a conservative, defensive posture. Organizations should block AI browsers unless they’re prepared to scrutinize the underlying AI services and enforce hardened configurations. And even then, user habits can undermine the best technical controls. For security leaders already struggling with cloud exposure, shadow IT, and increasingly autonomous enterprise tools, the message is clear: agentic browsers don’t fit neatly into existing risk models.

Whether enterprises follow the recommendation is another matter. But the concerns are concrete, the attack surface is real, and the defaults aren’t in security’s favor.