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How AI Fraud Detection Protects the Integrity of Online Elections
By Mark Radcliffe·April 8, 2026
Online elections face security challenges that paper elections don't. Credential stuffing, coordinated voting patterns, and bot activity can compromise election integrity.
The Threat Landscape
Common threats to online election integrity include credential stuffing, coordinated voting, bot activity, and insider threats.
How CastIron's AI Detection Works
CastIron monitors every voting session in real time using machine learning models trained on millions of legitimate votes. The system flags unusual voting velocity, geographic anomalies, device fingerprint mismatches, and behavioral patterns inconsistent with legitimate voters.
The Track Record
Votem has processed 13 million votes with zero security breaches.
The Threat Landscape
Common threats to online election integrity include credential stuffing, coordinated voting, bot activity, and insider threats.
How CastIron's AI Detection Works
CastIron monitors every voting session in real time using machine learning models trained on millions of legitimate votes. The system flags unusual voting velocity, geographic anomalies, device fingerprint mismatches, and behavioral patterns inconsistent with legitimate voters.
The Track Record
Votem has processed 13 million votes with zero security breaches.