Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909695587909632 |
|---|---|
| author | Rastogi, Nidhi Pant, Shirid Dhanuka, Devang Saxena, Amulya Mairal, Pranjal |
| author_facet | Rastogi, Nidhi Pant, Shirid Dhanuka, Devang Saxena, Amulya Mairal, Pranjal |
| contents | Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection in Security Operations Centers (SOCs). However, identifying the appropriate level and format of explanation, particularly in environments that demand rapid decision-making under high-stakes conditions, remains a complex and underexplored challenge. To address this gap, we conducted a three-month mixed-methods study combining an online survey (N1=248) with in-depth interviews (N2=24) to examine (1) how SOC analysts conceptualize AI-generated explanations and (2) which types of explanations are perceived as actionable and trustworthy across different analyst roles. Our findings reveal that participants were consistently willing to accept XAI outputs, even in cases of lower predictive accuracy, when explanations were perceived as relevant and evidence-backed. Analysts repeatedly emphasized the importance of understanding the rationale behind AI decisions, expressing a strong preference for contextual depth over a mere presentation of outcomes on dashboards. Building on these insights, this study re-evaluates current explanation methods within security contexts and demonstrates that role-aware, context-rich XAI designs aligned with SOC workflows can substantially improve practical utility. Such tailored explainability enhances analyst comprehension, increases triage efficiency, and supports more confident responses to evolving threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs Rastogi, Nidhi Pant, Shirid Dhanuka, Devang Saxena, Amulya Mairal, Pranjal Cryptography and Security Artificial Intelligence Information Retrieval I.2; E.3; J.4 Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection in Security Operations Centers (SOCs). However, identifying the appropriate level and format of explanation, particularly in environments that demand rapid decision-making under high-stakes conditions, remains a complex and underexplored challenge. To address this gap, we conducted a three-month mixed-methods study combining an online survey (N1=248) with in-depth interviews (N2=24) to examine (1) how SOC analysts conceptualize AI-generated explanations and (2) which types of explanations are perceived as actionable and trustworthy across different analyst roles. Our findings reveal that participants were consistently willing to accept XAI outputs, even in cases of lower predictive accuracy, when explanations were perceived as relevant and evidence-backed. Analysts repeatedly emphasized the importance of understanding the rationale behind AI decisions, expressing a strong preference for contextual depth over a mere presentation of outcomes on dashboards. Building on these insights, this study re-evaluates current explanation methods within security contexts and demonstrates that role-aware, context-rich XAI designs aligned with SOC workflows can substantially improve practical utility. Such tailored explainability enhances analyst comprehension, increases triage efficiency, and supports more confident responses to evolving threats. |
| title | Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs |
| topic | Cryptography and Security Artificial Intelligence Information Retrieval I.2; E.3; J.4 |
| url | https://arxiv.org/abs/2503.02065 |