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Reviewed and published by trentmaziarz, March 20, 2026. Discovered and drafted by our automated research pipeline.

Embark deploys machine learning models that analyze player behavior patterns (particularly mouse input, aiming, and recoil) to detect cheaters such as aimbot users, complementing traditional anti-cheat software like Easy Anti-Cheat and Denuvo.

Details

Embark has used ML for cheat detection since The Finals' launch in December 2023, as confirmed in official patch notes. The system analyzes hundreds of mouse/controller inputs per burst, comparing organic human recoil patterns (noisy, variable) against artificial cheat patterns (consistent, noise-free). In early 2026, Embark stated models deployed over the previous six months were "catching cheats that previously went undetected" and that in all manually reviewed cases, "the results have been correct." A dedicated cross-functional Anti-Cheat team includes game security developers, backend/data engineers, and ML engineers. A now-filled job listing for "Experienced ML Engineer - Anti-Cheat" described responsibilities including building ML cheat detection systems, deploying models, and analyzing results. At GDC 2026, Embark's Tom van Dijck participated in an anti-cheat panel alongside Amazon Games and Denuvo representatives.

Products affected

The FinalsARC Raiders

Sources & Evidence

Cite this record

Trace Foundation. (2026). Embark Studios: Embark deploys machine learning models that analyze player behavior patterns (particularly mouse input, aiming, and recoil) to detect cheaters such as aimbot users, complementing traditional anti-cheat software like Easy Anti-Cheat and Denuvo (data as of 2026-03-20) [Data set record]. AI Trace. https://www.aitrace.org/r/practice/c78065ba-5d37-4f52-966b-75e7b974c41d. Accessed October 10, 2026.

Stable link
https://www.aitrace.org/r/practice/c78065ba-5d37-4f52-966b-75e7b974c41d
Data as of
March 20, 2026
Last verified
March 24, 2026

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