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Algorithm Perception When Using Threat Intelligence in Vulnerability Risk Assessment.

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Summary
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Participants distrusted AI-driven cyber threat intelligence recommendations. Security expertise increased trust, but perceived bias remained similar for human versus artificial intelligence (AI) sources.

Keywords:
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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Government and commercial sectors are increasingly adopting AI for cyber threat intelligence analysis.
  • Understanding potential bias from human versus AI sources is crucial before production deployment of automated solutions.

Purpose of the Study:

  • To measure bias introduced by the source of cyber threat intelligence (human vs. AI).
  • To assess the impact of participant expertise (security and machine learning) on bias.

Main Methods:

  • A controlled experiment was conducted with 57 master's students.
  • Participants analyzed cyber threat intelligence reports with manipulated sources (human expert or AI algorithm).
  • Perceived bias and agreement with recommendations were measured.

Main Results:

  • Participants tended to disagree with AI-generated recommendations.
  • Higher security expertise correlated with greater agreement with recommendations.
  • Perceived bias was statistically equivalent whether recommendations came from human or AI sources.
  • Disagreement with a recommendation, regardless of source, was the primary factor influencing perceived bias.

Conclusions:

  • The introduction of AI in cyber threat intelligence may impact Tier 1 SOC analysts.
  • Further research with experienced security professionals is needed to generalize findings to professional practice.