To Know What You Do Not Know

Challenges for Explainable AI for Security and Threat Intelligence

Book chapter (2024)

Authors

Sarah van Gerwen Vrije Universiteit Amsterdam
J.E. Constantino Torres Organisation & Governance - TPM
Ritten Roothaert Vrije Universiteit Amsterdam
Brecht Weerheijm Universiteit Leiden
Ben Wagner Organisation & Governance - TPM
Gregor Pavlin Thales Research and Technology
Bram Klievink Universiteit Leiden
Stefan Schlobach Vrije Universiteit Amsterdam
Katja Tuma Vrije Universiteit Amsterdam
Fabio Massacci Vrije Universiteit Amsterdam, Università di Trento
Research Group
Organisation & Governance (TPM) (TU Delft)
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Published Date

2024

Language

English

Reuse Rights

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Faculty

Technology, Policy and Management

Department

Multi Actor Systems

Research Group

Organisation & Governance

Abstract

Human analysts working for threat intelligence leverage tools powered by artificial intelligence to routinely assemble actionable intelligence. Yet, threat intelligence sources and methods often have significant uncertainties and biases. In addition, data sharing might be limited for operational, strategic, or legal reasons. Experts are aware of these limitations but lack formal means to represent and quantify these uncertainties in their daily work. In this chapter, we enunciate the technical, legal, and societal challenges for building explainable AI for threat intelligence. We also discuss ideas for overcoming these challenges.

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