Closing the Employment Fraud Gap in the AI Era.

State actors and financial fraudsters are exploiting employment as a cyberattack surface. The North Korean IT-worker program alone has placed operatives inside more than one hundred US enterprises, including security vendors and Fortune 500 firms, generating hundreds of millions of dollars per year in fraudulent salaries and, after detection, escalating to data extortion against the very employers being defrauded. The capabilities required to detect this threat exist today as point tools (resume validation, background checks, document authenticity, deepfake detection, behavioral biometrics) but they do not communicate with one another, and no system reasons over them coherently across the full employment lifecycle. Census Networks is an AI-first platform that fills that gap.

Multiple specialized models, organized into four pillars, produce independent judgments that a fault-tolerant consensus engine fuses into a single, explainable risk score that travels with a candidate from application through continuous post-hire monitoring. This paper sets out the threat model, the platform's design principles, the scoring methodology and compliance posture, a concrete proposal for cross-industry employment-risk signal sharing, and the deployment and integration patterns that make the platform real.