Autonomous intelligence in public trust: ethical frameworks for large language models in federal cybersecurity threat hunting

Abstract

The integration of large language models (LLMs) into federal cybersecurity threat hunting promises significant advances in speed, scale, and analytical capability, particularly in environments characterized by high data volumes and persistent nation-state adversaries. As U.S. federal agencies confront increasingly sophisticated cyber threats, LLM-enabled systems offer the potential to augment human analysts through automated threat intelligence processing, cross-agency pattern recognition, and continuous monitoring of complex attack surfaces. However, the deployment of autonomous or semi-autonomous AI systems within national security contexts raises profound ethical and governance challenges that extend beyond traditional cybersecurity concerns. This paper examines the ethical tensions arising from hypothetical and emerging LLM deployment in federal threat hunting operations, focusing on conflicts between autonomy and human oversight, accuracy and civil liberties, transparency and operational security, and the allocation of responsibility when AI systems err. Using the SolarWinds supply chain compromise as an illustrative case study, the analysis demonstrates how LLM capabilities could both mitigate detection failures and introduce new risks if deployed without robust safeguards. The paper argues that effective integration of LLMs into federal cybersecurity requires an adaptive ethical governance framework grounded in human-centered design, proportionality, accountability, and democratic oversight. It advances a hybrid human-AI model that preserves human judgment in consequential decisions while leveraging AI for pattern recognition and data synthesis. By situating LLM-enabled threat hunting within broader debates on public trust and responsible innovation, this work contributes to the growing literature on AI ethics in high-stakes government and national security applications.