arbiter
JournalsPricingAccount

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

Rangarajan Parthasarathy et al.

International Journal of Intelligent Information Technologies2025https://doi.org/10.4018/ijiit.370967article
ABDC C
Weight
0.37

What the paper says

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.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.4018/ijiit.370967

Or copy a formatted citation

@article{rangarajan2025,
  title        = {{STRIFE: A Socio-Technical Framework for Threat Modeling of Artificial Intelligence Systems}},
  author       = {Rangarajan Parthasarathy et al.},
  journal      = {International Journal of Intelligent Information Technologies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijiit.370967},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

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

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.