STRIFE: A Socio-Technical Framework for Threat Modeling of Artificial Intelligence Systems

Due to the rapidly growing adoption of artificial intelligence (AI) technology, there has been an increased focus in recent times on the opportunities and perils of AI use. The authors propose STRIFE, a novel socio-technical threat modeling framework which combines technical, ethical, and legal dimensions to proactively identify and address negative and unintended consequences of AI systems. A second contribution of this inquiry is to enable the use of the National Institute of Standards and Technology AI Risk Management Framework to perform threat modeling in conjunction with STRIFE. By addressing AI threats using socio-technical considerations throughout the AI lifecycle, organizations can better engage with their societal stakeholders in managing the risks associated with AI systems. For these reasons, this study is expected to benefit academics, practitioners, and industry as a whole.

Authors

Institutions

Publication Details

Journal
International Journal of Intelligent Information Technologies
Published
2025-03-14
DOI
https://doi.org/10.4018/ijiit.370967
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.37
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

STRIFE: A Socio-Technical Framework for Threat Modeling of Artificial Intelligence Systems

Prasad Bingi, Anuradha Rangarajan, Rangarajan Parthasarathy, Saran Ghatak
1 citations
International Journal of Intelligent Information Technologies
Ethics and Social Impacts of AI
2.37
article

STRIFE: A Socio-Technical Framework for Threat Modeling of Artificial Intelligence Systems

Prasad Bingi, Anuradha Rangarajan, Rangarajan Parthasarathy, Saran Ghatak
article en
1 citations

Abstract

Due to the rapidly growing adoption of artificial intelligence (AI) technology, there has been an increased focus in recent times on the opportunities and perils of AI use. The authors propose STRIFE, a novel socio-technical threat modeling framework which combines technical, ethical, and legal dimensions to proactively identify and address negative and unintended consequences of AI systems. A second contribution of this inquiry is to enable the use of the National Institute of Standards and Technology AI Risk Management Framework to perform threat modeling in conjunction with STRIFE. By addressing AI threats using socio-technical considerations throughout the AI lifecycle, organizations can better engage with their societal stakeholders in managing the risks associated with AI systems. For these reasons, this study is expected to benefit academics, practitioners, and industry as a whole.

International Journal of Intelligent Information TechnologiesVol. 21(1)
University of Wisconsin–Green Bay (US), Illinois Institute of Technology (US), Purdue University Fort Wayne (US)
Openalex Percentile: Top 13%
Ethics and Social Impacts of AI
2.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

STRIFE: A Socio-Technical Framework for Threat Modeling of Artificial Intelligence Systems — Prasad Bingi, Anuradha Rangarajan, et al. · International Journal of Intelligent Information Technologies (2025) | TGRS Research Map | TGRS