AI cybersecurity tools are currently struggling because they lack the complete data needed to make accurate decisions.

Key points

• The "Context Gap": While AI agents are designed to automate threat detection and response, they often fail because they rely on fragmented, incomplete data from different security tools.
• Risk of Miscalculation: Without a full picture of network activity, AI can trigger false alarms, disrupt legitimate business operations, or miss sophisticated cyberattacks.
• The Network as a Source of Truth: Experts argue that organizations must prioritize high-quality, unified data—specifically from network traffic, user identities, and cloud workloads—to give AI a reliable foundation.
• AI as an Assistant, Not a Replacement: Effective AI should handle repetitive tasks and data correlation, leaving complex decision-making to human analysts.
• Automation Amplifies Foundations: AI is not a shortcut; it simply amplifies the quality of the data it is given. If your data is poor, your automation will be ineffective.

Companies are rushing to adopt AI to solve cybersecurity staffing shortages, but relying on these tools without first fixing data visibility issues can create dangerous blind spots. To be effective, AI must be fed a clear, unified view of the entire digital environment rather than isolated fragments of information.