Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligence
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
In an era marked by unprecedented digital complexity, the cybersecurity landscape is evolving at a breakneck pace, challenging traditional defense paradigms. Advanced Persistent Threats (APTs) reveal inherent vulnerabilities in conventional security measures and underscore the urgent need for continuous, adaptive, and proactive strategies that seamlessly integrate human insight with cutting edge AI technologies. This manuscript explores how the convergence of agentic AI and Frontier AI is transforming cybersecurity by reimagining frameworks such as the cyber kill chain, enhancing threat intelligence processes, and embedding robust ethical governance within automated response systems. Drawing on real-world data and forward looking perspectives, we examine the roles of real time monitoring, automated incident response, and perpetual learning in forging a resilient, dynamic defense ecosystem. Our vision is to harmonize technological innovation with unwavering ethical oversight, ensuring that future AI driven security solutions uphold core human values of fairness, transparency, and accountability while effectively countering emerging cyber threats.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 0 of its 19 references that are in the map. First posted on arXiv 28 Feb 2025.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2026A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypescited by 22
- 2025Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Modelscited by 4
- 2025Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deploymentcited by 1
- 2025Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelinescited by 0
- 2026A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes8 Jan 2026
- 2025Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment28 May 2025
- 2025Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines22 May 2025
- 2025Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models9 May 2025
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