Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement
Survey of playbooks, LLMs, RAG, and agentic AI for law-enforcement cyber first responders finds RAG most viable but benchmarks inadequate for legal requirements.
The paper surveys decision-support architectures (playbooks, LLMs, RAG frameworks, agentic AI) for frontline law enforcement during the first hour of a cyber incident, where volatile digital artifacts risk procedural errors and evidence attrition. RAG-based systems are identified as a relatively viable intermediate solution, though prompt sensitivity and confident hallucinations in legal contexts pose major risks. The authors find current cybersecurity benchmarks insufficient for law enforcement safety and legal demands, and argue for a new benchmark focused on naive query robustness and evidence preservation.
- First-hour responder mistakes can irreversibly compromise investigations and prosecutions
- RAG judged most viable interim solution despite prompt sensitivity and hallucination risks
- Existing cybersecurity benchmarks fail law-enforcement safety and legal requirements
- Authors call for benchmark covering naive query robustness and evidence preservation
From the original article
1 min read at arxiv.orgThe actions of frontline law enforcement officers in the initial hour of a cyber incident play a vital role in determining the ultimate success of an investigation. The minor mistakes they commit might result in irreversible critical impacts. The integrity of the investigation can be compromised, and the prosecution of cyber criminals can be hindered due to minor mistakes that happen in the initial hour.
By Roshin Sleeba C · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.