# A cybersecurity AI agent selection and decision support framework

arXiv: https://arxiv.org/abs/2510.01751 · HTML: https://mlsanity.com/paper/2510.01751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 143 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents](https://mlsanity.com/paper/2408.07199/) (2024)
- [Agent S: An Open Agentic Framework that Uses Computers Like a Human](https://mlsanity.com/paper/2410.08164/) (2024)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation](https://mlsanity.com/paper/2203.08994/) (2022)
- [Building Ethically Bounded AI](https://mlsanity.com/paper/1812.03980/) (2018)
- [Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research](https://mlsanity.com/paper/2411.04788/) (2024)
- [AI Agent for Education: von Neumann Multi-Agent System Framework](https://mlsanity.com/paper/2501.00083/) (2024)
- [Correctness Comparison of ChatGPT-4, Gemini, Claude-3, and Copilot for Spatial Tasks](https://mlsanity.com/paper/2401.02404/) (2024)
- [From Screens to Scenes: A Survey of Embodied AI in Healthcare](https://mlsanity.com/paper/2501.07468/) (2025)
- [Autonomous Intelligent Cyber-defense Agent (AICA) Reference Architecture. Release 2.0](https://mlsanity.com/paper/1803.10664/) (2018)
- [Ethics and Responsible AI Deployment](https://mlsanity.com/paper/2311.14705/) (2023)
- [EmBARDiment: an Embodied AI Agent for Productivity in XR](https://mlsanity.com/paper/2408.08158/) (2024)
- [Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence](https://mlsanity.com/paper/2401.00286/) (2023)
- [When Autonomous Intelligent Goodware will Fight Autonomous Intelligent Malware: A Possible Future of Cyber Defense](https://mlsanity.com/paper/1912.01959/) (2019)
- [Enabling A Network AI Gym for Autonomous Cyber Agents](https://mlsanity.com/paper/2304.01366/) (2023)
- [AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity](https://mlsanity.com/paper/2310.12162/) (2023)
- [Intelligent Autonomous Agents are Key to Cyber Defense of the Future Army Networks](https://mlsanity.com/paper/1812.08014/) (2018)
- [Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems](https://mlsanity.com/paper/2307.03913/) (2023)
- [Towards an Active, Autonomous and Intelligent Cyber Defense of Military Systems: the NATO AICA Reference Architecture](https://mlsanity.com/paper/1806.08657/) (2018)
- [Doers, not Watchers: Intelligent Autonomous Agents are a Path to Cyber Resilience](https://mlsanity.com/paper/2201.11111/) (2022)
- [Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?](https://mlsanity.com/paper/2309.10149/) (2023)

## Abstract (arXiv)

This paper presents a novel, structured decision support framework that systematically aligns diverse artificial intelligence (AI) agent architectures, reactive, cognitive, hybrid, and learning, with the comprehensive National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) 2.0. By integrating agent theory with industry guidelines, this framework provides a transparent and stepwise methodology for selecting and deploying AI solutions to address contemporary cyber threats. Employing a granular decomposition of NIST CSF 2.0 functions into specific tasks, the study links essential AI agent properties such as autonomy, adaptive learning, and real-time responsiveness to each subcategory's security requirements. In addition, it outlines graduated levels of autonomy (assisted, augmented, and fully autonomous) to accommodate organisations at varying stages of cybersecurity maturity. This holistic approach transcends isolated AI applications, providing a unified detection, incident response, and governance strategy. Through conceptual validation, the framework demonstrates how tailored AI agent deployments can align with real-world constraints and risk profiles, enhancing situational awareness, accelerating response times, and fortifying long-term resilience via adaptive risk management. Ultimately, this research bridges the gap between theoretical AI constructs and operational cybersecurity demands, establishing a foundation for robust, empirically validated multi-agent systems that adhere to industry standards.