Toward Responsible AI-Augmented Cyber Defense: Pattern Recognition, Defense-in-Depth, and the Case for Human-AI Collaboration
Researchers formalize AI-augmented defense-in-depth and show full review of AI alerts can lower detection rates.
The paper proposes a falsifiable model connecting defense-in-depth, AI pattern recognition, and human-AI collaboration in security operations. It treats layered defense as a Bernoulli detection cascade, each layer as a Neyman-Pearson detector with a closed-form threshold, and triage as a capacity-constrained cascade. A Monte Carlo simulation finds AI augmentation compounds most where traditional layering saturates. Reviewing every AI-flagged alert cut false alarms roughly 20-fold but lowered system detection because imperfect analyst judgment was applied to every alert.
- Models layered defense as a Bernoulli detection cascade.
- Derives a closed-form Neyman-Pearson detection threshold per layer.
- Full alert review cut false alarms about 20-fold but lowered detection.
- Suggests an interior-optimum analyst capacity ratio for SOCs.
From the original article
1 min read at arxiv.orgCybersecurity literature has extensively documented the operational benefits of artificial intelligence (AI) for threat detection, incident response, and prevention, while raising qualitative concerns about over-automation, algorithmic bias, and analyst-skill erosion.
By Mustafa S. Aljumaily · arxiv.org
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