**Title:** "AI vs Human Hackers: The Battle for Cybersecurity Supremacy in a World of Increasing Threats and Evolving Technologies

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AI-Augmented Teams Outperform Human-Only Teams in Cybersecurity Competition

A recent cybersecurity competition has yielded the largest controlled dataset to date comparing the performance of AI-augmented teams to human-only teams in professional-grade offensive security tasks.

Key Findings

The results show that AI-augmented teams completed challenges at a significantly higher rate, with approximately 73% finishing at least one challenge, compared to 46% for human-only participants.

“The advantage of AI-augmented teams was most pronounced among lower-ranked teams, with a solve rate advantage of 3.2 times across all participants.”

However, this advantage narrowed as the skill level increased, dropping to 1.69 times among the top 5% of teams. Notably, the best human team outscored the top AI-augmented team in terms of total challenges solved at the elite tier.

Performance Edge Across Difficulty Tiers

The performance edge of AI-augmented teams varied across difficulty tiers, with the greatest advantage observed at medium complexity levels, where mid-career analysts typically operate.

However, AI teams struggled with the hardest challenges, failing to complete three challenges entirely. In contrast, AI teams excelled at the easiest challenges, with solve rates more than double those of human teams, highlighting the potential for automation risk in entry-level roles.

Completion Times and Operational Differentiators

An analysis of completion times revealed that AI-augmented teams were marginally slower on average. However, at the elite tier, the top AI-augmented teams completed challenges significantly faster than their human-only counterparts, with speed emerging as the clearest operational differentiator at this level.

Performance Gap Across Security Domains

The performance gap between AI-augmented and human-only teams varied widely across different security domains. Structured and systematic domains, such as Secure Coding and Blockchain, showed the largest AI advantages, while creative domains, such as Coding and Reversing, displayed the smallest gaps, particularly among elite performers.

Implications for Workforce Planning

The findings of this study have significant implications for workforce planning across different career tiers. At the entry level, AI solve rates on routine tasks suggest that standard analyst work can be automated with current tooling, potentially creating a gap in the pipeline for future senior practitioners.

At the mid-career tier, the AI advantage on medium-difficulty tasks is the strongest, making it a high-return target for AI tooling deployment. At the elite tier, AI functions as a speed multiplier, allowing senior practitioners to retain their capability edge on the hardest problems.

Recommendations for Organizations

Organizations should take note of these findings when modeling AI output multipliers into red-team threat scenarios, as this will enable them to set more realistic assumptions about adversary speed and capability.

Moreover, incident response windows and service level agreements should be revised to account for the potential threat from AI-augmented adversaries. The deployment of AI tooling by domain, starting with structured exploitation categories, is likely to deliver faster returns than a uniform rollout.

However, retaining and developing senior operators remains a priority, as novel reasoning on hard problems is where human expertise still leads, and this capability requires sustained investment in real-world challenge training to maintain.