The arms race between game developers and cheat makers has been going on for decades. Every new anti-cheat system gets cracked. Every new cheat gets detected. The cycle continues. But AI anti-cheat represents a genuine shift in how the battle is fought, and the results are mixed in interesting ways.

How AI anti-cheat works

Traditional anti-cheat systems work like antivirus software. They look for known cheat programs, scan memory for suspicious modifications, and flag players using detected software. The problem is obvious: cheat developers reverse-engineer the anti-cheat, modify their cheats to avoid detection, and the process repeats.

AI anti-cheat takes a different approach. Instead of looking for specific cheat software, it analyses player behaviour. How do they aim? How fast do they react? Do their mouse movements follow natural human patterns? Are they consistently performing actions that are statistically improbable?

A human player’s aim follows a specific pattern: initial acquisition, tracking adjustments, micro-corrections. An aimbot’s pattern is different — typically snapping to targets with inhuman speed and precision, or tracking with unnatural smoothness. AI systems trained on millions of gameplay samples can identify these patterns with increasing accuracy.

The same principle applies to other types of cheating. Wallhack users make decisions — pre-aiming corners, avoiding ambush positions, tracking enemies through walls — that reveal information they shouldn’t have. AI systems can detect these patterns statistically, even when the cheat software itself is invisible.

What’s actually working

Several major titles have deployed AI anti-cheat systems with measurable results.

VALORANT’s Vanguard has been the most aggressive, running at the kernel level and combining traditional detection with behavioural analysis. Riot claims it has reduced cheating in ranked play significantly, and anecdotally, the VALORANT experience is cleaner than most comparable games.

Counter-Strike 2’s VACnet uses deep learning to analyse aim patterns and flag suspicious players for review. It processes millions of matches and generates automated bans for the most obvious cases while sending borderline cases to human reviewers through the Overwatch system.

Several battle royale games have implemented AI-based movement analysis that detects speed hacks, teleporting, and unusual traversal patterns. These work well because the expected movement parameters are well-defined — a player simply cannot move faster than the game allows without cheating.

The Australian esports scene specifically has benefited from improved anti-cheat. Tournament organisers running local VALORANT and CS2 events report that the AI systems have reduced the number of disputed results and player complaints about suspected cheating.

Firms like AI development company Sydney are also exploring how similar behavioural analysis techniques could be applied to other competitive gaming contexts, including mobile esports where cheating is particularly prevalent.

What’s not working

False positives. AI anti-cheat systems occasionally flag legitimate players whose play style triggers the detection algorithms. A player with exceptionally good aim, unusual sensitivity settings, or a distinctive play style can be misidentified as cheating.

This is a serious problem. Being falsely banned from a game you’ve spent hundreds of hours in is infuriating. The appeals processes for most anti-cheat systems are opaque and slow. Players who are falsely flagged often have little recourse.

Hardware cheats. The newest cheats bypass software-based detection entirely. Hardware-level aimbots that process the game’s video output and inject mouse inputs at the hardware level are extremely difficult for any anti-cheat system — AI or otherwise — to detect. They don’t modify the game’s memory, don’t inject code, and produce inputs that are physically identical to a human using a mouse.

AI behavioural analysis can theoretically detect these, since the aim patterns are still inhuman. But the cheat developers have responded by adding “humanisation” — random delays, micro-imperfections, and natural-looking movement patterns that make the cheats harder to distinguish from skilled human play.

Private cheats. The AI systems are trained on data from detected cheats. Private cheats used by small numbers of players generate too little data for the AI to learn from. As long as a cheat stays below the detection threshold and isn’t widely distributed, it can remain undetected for months or years.

The arms race continues

AI anti-cheat hasn’t solved cheating. It’s raised the floor — the average quality of the online gaming experience has improved because obvious cheats are caught faster. But the ceiling hasn’t changed. The most sophisticated cheats, used by the most motivated cheaters, remain ahead of detection.

What AI anti-cheat has done is change the economics. Developing a cheat that bypasses modern AI detection is significantly more expensive and technically demanding than it used to be. This reduces the supply of cheap, widely available cheats and pushes the problem toward a smaller number of expensive, private cheats.

For the average player, that’s a meaningful improvement. The random encounter with a blatant aimbotter in a ranked match is less common than it was two years ago. The subtle, private-cheat user who’s boosting their rank by 10 percent still exists, but they’re harder to notice and less common.

The dream of a cheat-free online gaming experience remains exactly that — a dream. But AI anti-cheat is the closest the industry has come to making it a partial reality. Progress, even imperfect progress, matters.