AI-Driven Security Requires Complete, Unfiltered Data for Effective Threat Detection

The Data Gap in Current Security Systems

For 25 years, security data primarily consisted of logs and events. However, security products pre-filter and normalize telemetry before it reaches Security Information and Event Management (SIEM) systems. This process means SIEMs receive only 10-20% of the data originally generated by the environment, losing crucial context and timing relationships for events.

Challenges for AI in Cybersecurity

The AI era has increased the complexity of cyberattacks, which now span multiple domains. Detecting and correlating these attacks requires complete, multi-product data. Existing systems, designed without this need in mind, struggle to provide the necessary high-fidelity input for advanced AI models to function effectively.

Reconstructing Complex Attack Chains

An example of a multi-domain attack involves a departing employee accessing, downloading, uploading to personal cloud storage, and emailing a competitive document. Traditional SIEMs would only flag isolated fragments, such as a Data Loss Prevention (DLP) alert or a Cloud Access Security Broker (CASB) flag. Without the file's lineage, including content, access history, and user behavior baselines, reconstructing the intent and full attack sequence is not possible.

The Need for Comprehensive Data for Anomaly Detection

With AI lowering the barrier for cyberattacks, it is necessary to assume attackers may already be inside systems. Identifying subtle deviations from normal behavior is critical, but this requires large, complete data samples. A single anomalous login is ambiguous, but correlating multiple logins over time with device telemetry and access patterns provides the necessary context to distinguish legitimate activity from a security threat.