Securing Agentic AI with the NIST Cybersecurity Framework 2.0

Agentic AI —LLM-powered autonomous agents— is reshaping cybersecurity paradigms, introducing a novel attack surface that exposes gaps in current security approaches. This Systematic Literature Review (SLR) of 30 peer-reviewed papers examines the emerging threats and mitigation strategies for these agentic systems. The review synthesizes evidence through a four-dimensional taxonomy derived from the OWASP Agentic AI Threats framework. Building on these findings, the paper proposes a new adaptation of the NIST Cybersecurity Framework (CSF) 2.0 to guide organizations in identifying, protecting, responding to, and recovering from risks associated with agentic AI. The presented framework provides a clear and practical method for securing agentic AI-driven systems in both enterprise and research contexts.

Authors

Institutions

Publication Details

Published
2025-11-27
DOI
https://doi.org/10.1109/eleco69582.2025.11329370
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Securing Agentic AI with the NIST Cybersecurity Framework 2.0

Faisal Iradat, Martín Wynn, Bilgin Metin, Hikmet Sami Karaca
Smart Grid Security and Resilience
article

Securing Agentic AI with the NIST Cybersecurity Framework 2.0

Faisal Iradat, Martín Wynn, Bilgin Metin, Hikmet Sami Karaca
article en

Abstract

Agentic AI —LLM-powered autonomous agents— is reshaping cybersecurity paradigms, introducing a novel attack surface that exposes gaps in current security approaches. This Systematic Literature Review (SLR) of 30 peer-reviewed papers examines the emerging threats and mitigation strategies for these agentic systems. The review synthesizes evidence through a four-dimensional taxonomy derived from the OWASP Agentic AI Threats framework. Building on these findings, the paper proposes a new adaptation of the NIST Cybersecurity Framework (CSF) 2.0 to guide organizations in identifying, protecting, responding to, and recovering from risks associated with agentic AI. The presented framework provides a clear and practical method for securing agentic AI-driven systems in both enterprise and research contexts.

University of Gloucestershire (GB), Institute of Business Administration Karachi (PK), Boğaziçi University (TR)
Openalex Percentile: Top 33%
Smart Grid Security and Resilience
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.