GoKawiilBy Thyaga Vasudevan, EVP of Product at Skyhigh Security
For decades, enterprise security was largely focused on endpoints and networks. By protecting corporate end-user devices, establishing secure connectivity, and controlling access to on-premises resources, teams could effectively keep their centralized servers secure from intrusion.
Eventually, these priorities evolved as enterprises embraced software-as-a-service (SaaS) models and cloud services, shifting to include new cloud security, data protection, and identity-based controls.
Then employees began working from anywhere, on any device, and the network expanded even further. And if that wasn’t enough, the artificial intelligence (AI) boom came along and made the threat landscape even more complicated.
When users can be in person, remote, or hybrid, and access data from a corporate computer or a personal laptop—then turn around and share that data with third-party AI models—endpoint and network measures alone are no longer sufficient.
Even as enterprise networks become increasingly distributed and complex, there is one common thread that connects users, applications, data, and AI models: the browser. It is the primary interface for modern work, acting as a gateway to business-critical tools, platforms, and more.
Because of this, securing the browser has taken on even more significance, emerging as a critical component of modern security strategies designed to protect data wherever it is accessed—even by AI models.
How AI Revealed an Existing Blind Spot
The excitement around AI has understandably focused enterprise attention on the new risks associated with these models. Security teams worry about employees copying and pasting sensitive information, uploading private files, or inadvertently exposing intellectual property while using AI services and workflows.
Because of this, they focus on protecting sanctioned enterprise AI tools while discouraging the use of unsanctioned shadow AI models, trying to promote adoption while avoiding new threats.
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