AI Security: Visibility Gap Undermines Autonomy

AI agents hold significant promise for augmenting cybersecurity teams by triaging alerts and investigating incidents. However, many security leaders find these agents ineffective because they operate with fragmented data, akin to relying on incomplete puzzle pieces. This lack of comprehensive network visibility means AI struggles to understand normal behavior, leading to increased false positives and stalled investigations.

Effective autonomy in cybersecurity hinges on a robust data foundation. Practitioners are rediscovering the network as a primary source of truth, as it captures unavoidable attacker activity. By unifying network data with insights into user identities and cloud-native workloads, AI agents can gain the necessary context to make accurate, deterministic decisions. This allows AI to handle routine tasks, freeing human analysts to focus on complex threats, but only if the underlying data is trustworthy and accessible.