According to a recent LinkedIn post from Cyberhaven, research from Cyberhaven Labs suggests that nearly 40% of AI interactions involve sensitive data. The post indicates that many existing security programs were designed before AI agents could autonomously read email, ship code, and query databases, creating potential coverage gaps.
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The company’s LinkedIn post highlights a Security Buyer’s Guide that outlines six criteria distinguishing modern AI security from legacy point solutions. These criteria reportedly include capabilities such as continuous agent inventory, full execution lifecycle observability, and controls that follow data rather than specific tools.
The post also references an AI Security Readiness Checklist aimed at helping security leaders quickly assess their current programs before engaging with vendors. For investors, this emphasis on AI-centric security tooling may signal Cyberhaven’s intent to position itself as a specialized provider in a growing niche of data protection for AI-driven workflows.
If the guide and checklist gain traction with security decision-makers, Cyberhaven could benefit from increased demand for platforms designed around AI usage patterns rather than traditional perimeter or endpoint models. This positioning may enhance the company’s competitive standing in enterprise security, particularly among organizations rapidly adopting AI agents and seeking to address associated data-risk exposures.