A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications

TL;DR

This paper systematically reviews automated red teaming methods in cybersecurity, highlighting their methodologies, benefits, challenges, and future research directions to enhance AI application security.

Contribution

It consolidates existing research on automated red teaming, identifying current trends, limitations, and gaps to guide future advancements in cybersecurity testing.

Findings

Automation improves red teaming efficiency and scalability.

Current methods face challenges in adaptability and comprehensiveness.

Future research should address identified gaps for better security assurance.

Abstract

Cybersecurity threats are becoming increasingly sophisticated, making traditional defense mechanisms and manual red teaming approaches insufficient for modern organizations. While red teaming has long been recognized as an effective method to identify vulnerabilities by simulating real-world attacks, its manual execution is resource-intensive, time-consuming, and lacks scalability for frequent assessments. These limitations have driven the evolution toward auto-mated red teaming, which leverages artificial intelligence and automation to deliver efficient and adaptive security evaluations. This systematic review consolidates existing research on automated red teaming, examining its methodologies, tools, benefits, and limitations. The paper also highlights current trends, challenges, and research gaps, offering insights into future directions for improving automated red teaming as a critical component of proactive cybersecurity strategies. By synthesizing findings from diverse studies, this review aims to provide a comprehensive understanding of how automation enhances red teaming and strengthens organizational resilience against evolving cyber threats.