Why Predictive Cybersecurity Depends on Connected Data

Predictive cybersecurity can fail even with capable AI models when security telemetry is fragmented, inconsistent, outdated, or disconnected from organizational context. The article highlights SIEM, SOAR, DLP, identity, endpoint, and network data as inputs that need to be correlated rather than analyzed in isolation. It recommends unified data foundations, dataset auditing, synthetic data for coverage gaps, retraining, and entity-level context. Technology leaders should treat data integration and data quality as core security architecture decisions before investing further in predictive models.