Darnell Okafor

Modern insider threat requires investigation capability, not just anomaly detection.

Five threat archetypes demand different detection strategies from insider threat platforms.

Static rules miss context that machine learning models learn from behavior.

Four key metrics beyond behavioral baselines separate truly accurate insider threat detection.

AI learns what normal looks like for each user, catching insider threats rules miss.

Governance structures, not monitoring tools, make insider threat programs legally defensible.

Unaddressed alerts cost millions in wasted labor and delayed breach detection.

Legacy DLP's static rules demand permanent expert tuning.

Identity data fragmentation and latency make insider risk scoring unreliable.

Realistic timelines demand 18-24 months, not the three-to-six-month fantasy vendors pitch.

Behavioral detection catches insider threats that rule-based systems miss.

CASB governs access; DLP governs content—and you need both to avoid silent breaches.