Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement

A systematic survey of decision-support architectures for first responders, highlighting the limitations of current AI and the need for law-enforcement-specific benchmarks.

Roshin Sleeba C; Hiran V Nath· 2026· DOI 10.48550/arXiv.2609.12681

The core problem

The initial hour following a cyber incident is critical for law enforcement investigations. Actions taken by frontline officers during this period can determine the success or failure of an investigation. Minor mistakes, often due to the volatile nature of digital artifacts, can lead to procedural errors, evidence attrition, and compromised prosecutions. This paper addresses the gap in decision-support tools designed specifically for first responders with limited technical proficiency and inconsistent forensic infrastructure. It systematically surveys existing architectures—playbooks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) frameworks, and Agentic AI systems—and evaluates their suitability for the law enforcement context. The authors highlight the need for solutions that align with judicial requirements and preserve evidence integrity from the very first hour.

The survey reveals that RAG-based systems emerge as a relatively viable intermediate solution due to their natural language adaptability, which allows officers to query in plain language. However, significant risk factors are identified: prompt sensitivity can lead to inconsistent outputs, and the potential for confident hallucinations in legal contexts poses a major challenge. Traditional playbooks are rigid and may not adapt to dynamic incidents. LLMs, while flexible, lack the grounding in specific legal and procedural knowledge. Agentic AI systems, though promising for automation, are not yet mature for high-stakes law enforcement use. Current cybersecurity benchmarks are found insufficient to capture the specific safety and legal requirements of law enforcement, especially concerning the initial hour of a cybercrime.

The initial hour following a cyber incident is critical for law enforcement investigations. Actions taken by frontline officers during this period can determine the success or failure of an investigation. Minor mistakes, often due to the volatile nature of digital artifacts, can lead to procedural errors, evidence attrition, and compromised prosecutions. This paper addresses the gap in decision-support tools designed specifically for first responders with limited technical proficiency and inconsistent forensic infrastructure. It systematically surveys existing architectures—playbooks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) frameworks, and Agentic AI systems—and evaluates their suitability for the law enforcement context. The authors highlight the need for solutions that align with judicial requirements and preserve evidence integrity from the very first hour.

The study conducts a systematic survey of decision-support architectures for cybercrime first responders. It categorizes these architectures into four types: traditional playbooks, LLMs, RAG frameworks, and Agentic AI systems. The analysis critically considers practical constraints such as limited technical proficiency among officers and inconsistent forensic infrastructure. The authors evaluate each category based on its ability to assist in the initial hour, focusing on natural language adaptability, risk factors like prompt sensitivity and hallucinations, and alignment with legal and safety requirements. Additionally, the paper reviews current cybersecurity benchmarks to assess their adequacy for law enforcement needs, particularly regarding naive query robustness and evidence preservation.

The analysis underscores the critical gap between existing AI decision-support tools and the needs of law enforcement first responders. The volatile nature of digital evidence means that even minor procedural errors can have irreversible consequences. RAG systems offer a balance between flexibility and accuracy but require robust safeguards against prompt sensitivity and hallucinations. The authors argue for a new evaluation benchmark that focuses on naive query robustness—ensuring systems can handle imprecise or non-technical queries—and evidence preservation, so that AI-driven guidance aligns with mandatory judicial proceedings. Such a benchmark would need to incorporate legal standards, chain-of-custody requirements, and the practical constraints of frontline policing. The paper concludes that without such benchmarks, AI tools risk undermining investigations rather than supporting them.

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