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AI security tools enhance testing but human expertise remains key

By PulseAugur Editorial · [1 sources] ·

Doyensec has developed an AI-assisted security testing workflow that enhances codebase understanding and vulnerability discovery. When tested against a previous target, this workflow not only identified previously reported critical and high findings but also uncovered two new vulnerabilities: a cross-tenant invitation token flaw and a read-only SSRF in the OAuth implementation. The company concluded that while AI tools significantly boost testing efficiency, human expertise remains crucial for optimal results. AI

IMPACT AI-powered security tools can significantly improve vulnerability detection, but human expertise is still essential for comprehensive security assessments.

RANK_REASON The item describes the results of a security research effort using AI tools and a whitepaper comparison. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI security tools enhance testing but human expertise remains key

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0 / 100
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The item describes the results of a security research effort using AI tools and a whitepaper comparison. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
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93 days old
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    After publishing our whitepaper ( https:// blog.doyensec.com/2026/05/27/a ikido-xbow.html ) comparing Aikido and XBOW, we evaluated our own AI-assisted testing

    After publishing our whitepaper ( https:// blog.doyensec.com/2026/05/27/a ikido-xbow.html ) comparing Aikido and XBOW, we evaluated our own AI-assisted testing workflow against one of the same targets to see whether it would identify the same High and Critical findings. Built by …

    LINKS blog.doyensec.com/…/aikido-xbow.html github.com/…/1561 github.com/…/1567 github.com/…/pulls

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