The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index, and 1 are not checked yet.
Toward Robust Security Orchestration and Automated Response in Security Operations Centers with a Hyper-Automation Approach Using Agentic Artificial Intelligence
Open accessAbstract
The evolving landscape of cybersecurity threats demands the modernization of Security Operations Centers (SOCs) to enhance threat detection, response, and mitigation. Security Orchestration, Automation, and Response (SOAR) platforms play a crucial role in addressing operational inefficiencies; however, traditional no-code SOAR solutions face significant limitations, including restricted flexibility, scalability challenges, inadequate support for advanced logic, and difficulties in managing large playbooks. These constraints hinder effective automation, reduce adaptability, and underutilize analysts’ technical expertise, underscoring the need for more sophisticated solutions. To address these challenges, we propose a hyper-automation SOAR platform powered by agentic-LLM, leveraging Large Language Models (LLMs) to optimize automation workflows. This approach shifts from rigid no-code playbooks to AI-generated code, providing a more flexible and scalable alternative while reducing operational complexity. Additionally, we introduce the IVAM framework, comprising three critical stages: (1) Investigation, structuring incident response into actionable steps based on tailored recommendations, (2) Validation, ensuring the accuracy and effectiveness of executed actions, (3) Active Monitoring, providing continuous oversight. By integrating AI-driven automation with the IVAM framework, our solution enhances investigation quality, improves response accuracy, and increases SOC efficiency in addressing modern cybersecurity threats.
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In the citing paper
“These capabilities are coordinated by control loops, the most influential of which are ReAct (Yao S. et al., ICLR 2023 [89]), which interleaves natural-language reasoning with tool invocations [25]; plan-and-solve, which produces a complete plan before execution; and reflection, which critiques prior outputs before committing [28,32].”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [28]
In the citing paper
“And they raise governance questions about accountability when autonomous decisions cause harm [27,28].”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [28]
In the citing paper
“SOAR and threathunting surveys [27,28] focus on workflow automation rather than autonomous reasoning.”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [28]
In the citing paper
“First, action verification: an autonomous response that hallucinates a quarantine target is potentially catastrophic, so reflection passes and confidence thresholds gate execution [28].”
From Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap (2026) · cites this paper as [28]
In the citing paper
“Rather than having one LLM plan, execute, and evaluate tasks, the systems now orchestrate a series of specialized agents sharing context, critiquing one another, and modularizing the incident-response pipeline [30], [31], [32].”
From The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines (2026) · cites this paper as [31]
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Karras, A., Theodorakopoulos, L., Karras, C., Theodoropoulou, A., Kalliampakou, I., & Kalogeratos, G. (2025) · Information · doi:10.3390/info16110957 · Cited by 41
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Metadata
- Authors
- Ismail, Kurnia, R., Brata, Z. A., Nelistiani, G. A., Heo, S., Kim, H., & Kim, H.
- Affiliations
- School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea
- SmartM2M. Co., Ltd., 701, 702, Building A, Centum Skybiz, 97 Centumjungang-ro, Haeundae-gu, Busan 48058, Republic of Korea
- Pusan National University (KR)
- Corresponding author
- Kim, Howon
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2025-04-29
- Volume
- 16
- Issue
- 5
- Pages
- 365
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W4409965657
- Cited by
- 50
- References
- 29
- Citations by year
- 2026: 32 · 2025: 18
- FWCI
- 20.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Information (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- MSIT (Ministry of Science and ICT), Korea (RS-2020-II201797)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Keywords
- security orchestration, automation and response, security operations center, hyperautomation, agentic large language models, incident response, AI-generated code, incident investigation, active monitoring
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