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

Roshin Sleeba C, Hiran V Nath

arXiv:2609.12681v1cs.CR

TL;DR

Frontline officers must make consequential cyber-incident decisions during the initial hour, often with limited expertise and incomplete information. This survey compares decision-support architectures and finds RAG-based systems offer a pragmatic balance, while identifying reliability risks and gaps in existing benchmarks.

  • Problem

    First-hour cybercrime decisions by frontline officers can affect investigations, but officers may have limited cybersecurity expertise and incomplete incident information.

    Source: §1 · p. 1

  • Method

    The paper surveys playbooks, LLM and RAG systems, and agentic AI architectures through the constraints of frontline law-enforcement use.

    Source: §1 · p. 1, §5 · p. 8

  • Results

    RAG-based LLM systems offer a better pragmatic balance of contextual adaptability and lower deployment barriers, while LLMs pose risks of hallucination and procedural inconsistency.

    Source: §10 · p. 12

  • Takeaways & Limitations

    Current cybersecurity benchmarks are insufficient for first-hour law-enforcement support, and the paper argues for evaluating naive-query robustness and evidence-preserving, legally sound guidance.

    Source: §1 · p. 2, §8 · p. 10

    LLMs’ linguistic variability and observed performance drops challenge the reliability of benchmark-based evaluations of real-world input robustness.

    Source: §9 · p. 10

Abstract

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The 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. These are mainly because of the volatile nature of digital artifacts that might lead to procedural errors and evidence attrition. This paper provides a systematic survey of decision-support architectures designed to assist first responders of a cybercrime, categorizing them into playbooks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) frameworks, and Agentic AI systems. The survey critically considers the constraints of limited technical proficiency and inconsistent forensic infrastructure in a practical scenario. Our analysis identifies RAG-based systems as a relatively viable intermediate solution due to their natural language adaptability. However, significant risk factors like prompt sensitivity and the potential for confident hallucinations in legal contexts pose a major challenge. Furthermore, we review current benchmarks in cybersecurity and demonstrate that they are not sufficient to capture the specific safety and legal requirements of law enforcement, focusing on the initial hour of the cybercrime. We conclude by arguing for the necessity of a new evaluation benchmark focused on naive query robustness and evidence preservation, so as to ensure that AI-driven guidance aligns with the mandatory demands of judicial proceedings.

1. Introduction

Frontline officers often make time-critical cybercrime decisions without specialist support, and errors can compromise evidence and investigations. The survey compares decision-support approaches through this operational lens and identifies gaps in existing benchmarks.

1. Introduction

  • Officers must assess incomplete, often non-technical incident descriptions and decide what to isolate, document, preserve, or escalate under time and legal constraints.Improper shutdown or failure to preserve volatile evidence can irreversibly compromise an investigation.Source: §1 · p. 1
  • The survey compares playbooks, LLM- and RAG-based systems, and agentic AI for first-hour cybercrime response from frontline officers’ perspective.It evaluates their strengths and limitations under severe time and information constraints.Source: §1 · pp. 1–2
  • LLM-based systems can interpret incomplete, ambiguous, or non-technical queries, but may hallucinate or give overconfident recommendations in high-stakes law-enforcement scenarios.Source: §1 · p. 2
  • Existing cybersecurity LLM benchmarks do not capture first-hour law-enforcement decision-support needs, motivating a benchmark for naive queries and evidence-preserving, legally sound guidance.Source: §1 · p. 2

2. Motivation: The Significance of the Initial Hour in Cyber Attacks

The initial hour matters because evidence can disappear and early response decisions shape later investigative viability. Reported systemic failures and operational constraints underscore the need for timely, context-sensitive action.

2.1. Documented Systemic Failures

  • Cybercrime investigations face substantial attrition, while variable log-retention policies can erase indicators needed to reconstruct events.The section cites reports that about 87% of reported cases are closed immediately and nearly 98% of cyber-enabled crimes receive no further action.Source: §2.1 · p. 2
  • Chain-of-custody failures can exclude evidence from judicial proceedings, while the Silk Road case illustrates how seizing a powered-on, logged-in laptop preserved live evidence.The preserved material included logs and cryptographic materials that could have been lost if the device had been powered down.Source: §2.1 · p. 3

2.2. The Fundamental First-Hour Challenge

  • Volatile evidence, including memory contents and active network sessions, may be irrecoverable after shutdown, cleanup, or other well-intentioned actions.Source: §2.2 · p. 3
  • Conflicting incident-response recommendations can leave officers uncertain whether to shut down a system or isolate it from the network first.Guidance may also conflict with forensic reconstruction needs or operational logging practices.Source: §2.2 · p. 3
  • Time pressure, chain-of-custody risks, and limited technical proficiency make immediate, contextualized natural-language guidance valuable to frontline officers.Source: §2.2 · p. 3

3. Early Stage Incident Response Approaches

Early-stage incident-response research spans static playbooks, LLM and RAG systems, and agentic AI frameworks. The survey presents their development as a progression from static to agentic approaches.

3. Early Stage Incident Response Approaches

  • The surveyed approaches include static incident-response playbooks, LLM and RAG systems, and agentic AI frameworks capable of semi-autonomous decision-making.Source: §3 · p. 3
  • Figure 2 presents an inferred progression in cybersecurity incident response from static to agentic paradigms.Source: §3 · p. 3, Figure 2 · p. 3
  • The paper reports cross-validating its Impact/Focus metrics against longitudinal World Economic Forum reports to align research trends with industry realities.Source: §3 · p. 3

3.1. Playbook-Driven Systems

The section surveys playbooks ranging from standardized procedures and human-readable guidance to executable and model-based workflows, then considers intelligent playbook generation. Their structures can improve consistency and manageability, but approaches may require technical expertise or target security analysts rather than law enforcement.

3.1. Playbook-Driven Systems

  • Playbooks are standardized operational procedures spanning high-level mandates to executable workflows, intended to support consistent response, especially during major incidents’ first hour.The section organizes the approaches into a taxonomy of playbook systems.Source: §3.1 · p. 3, Figure 3 · p. 4

3.1.1. Global Standards, Strategic Frameworks and Country-Specific standards

  • Country-specific guidelines adapt incident-response frameworks to national legal policies, and Table 1 summarizes representative national implementations.The section cautions that a single “One For All” policy may not always be ideal.Source: §3.1.1 · p. 4, Table 1 · p. 4

3.1.2. Tactical and Human-Centric Playbooks

  • Tactical playbooks offer human-readable workbook guidance for responders facing technical uncertainty and procedural pressure.Examples include IACD and SOTER, with SOTER workflows modeled using BPMN.Source: §3.1.2 · p. 4

3.1.3. Executable Workflows and Automation Standards

  • CACAO and COPS encode playbooks in machine-readable formats to support repeatable execution and interoperability, but emphasize technical execution over contextual reasoning.CACAO provides a structured schema; COPS uses YAML serialization for exchange and automated execution across heterogeneous platforms.Source: §3.1.3 · p. 4

3.1.4. Model-Based Systems

  • Model-based systems such as FRIPP and SASP formalize playbook design through executable meta-models or semantic ontologies, strengthening structural rigor but requiring upfront modeling effort and domain expertise.SASP uses standards including RDF and OWL to establish a standardized vocabulary.Source: §3.1.4 · pp. 4–5

3.1.5. Intelligent Playbook Generation

  • Intelligent playbook-generation systems such as Ic-secure use deep learning to capture alert context and recommend workflow modules, but target security analysts rather than law enforcement officers.These systems aim to reduce design effort and improve contextual alignment.Source: §3.1.5 · p. 5

3.2. LLM and RAG Based Incident Response and Reporting Systems

The section reviews LLM and RAG systems for incident analysis, response planning, timeline reconstruction, alert enrichment, and reporting. These approaches can incorporate retrieved context and support specialized workflows, but some rely on structured evidence or expert-formulated queries.

LLMs and RAG in Incident Response

  • LLMs are discussed for open-ended incident analysis and response planning, while RAG can incorporate current threat intelligence and system knowledge into incident logs.The cited work describes decoder-only models as suited to open-ended reasoning and generation, and GPT-4o and GPT-3.5 for tasks including containment, isolation, eradication, and recovery.Source: §3.2 · p. 5

Timeline Reconstruction

  • GenDFIR [29] uses a RAG agent and LLM to retrieve and semantically enrich structured incident events for timeline reconstruction, assuming evidence has already been collected through sophisticated forensic workflows.Its pipeline preprocesses heterogeneous events into a structured document.Source: §3.2 · p. 5

RAG-Based Investigation Support

  • AutoBnB-RAG coordinates LLM agents through an Incident Captain to access RAG-Wiki and RAG-News during uncertain investigations or failed procedures.RAG-Wiki includes curated documentation from 125 webpages, with 53.6% derived from Wikipedia; RAG-News contains synthetic, LLM-generated incident reports, and added context improves decision quality.Source: §3.2 · p. 5

CTI-RAG Alert Enrichment

  • CTI-RAG [31] retrieves from standardized CTI APIs and vector databases to enrich SIEM alerts and generate incident-specific response actions, with relevance and groundedness assessed on real-world and simulated alerts.The evaluation combines automated LLM judging with cybersecurity-expert cross-validation.Source: §3.2 · p. 5

Payload Analysis and Investigation Q&A

  • CyberRAG classifies an input payload, generates a retrieval query, and synthesizes retrieved domain knowledge with the classification into a human-readable report.RAG-based investigation and attribution Q&A can use knowledge graphs and CTI narratives, but assumes investigators can formulate precise, hypothesis-driven questions [33].Source: §3.2 · p. 5

3.3. Automated IR Agents

The section surveys automated incident-response agents, including machine-learning and reinforcement-learning systems, multi-agent LLM workflows, and task-specific forensic agents. Despite reported automation capabilities, these systems commonly depend on technical inputs and are aimed at specialist users rather than frontline law enforcement.

Agentic Strategies

  • Automated IR agents can analyze vulnerabilities and generate context-aware fixes, while agentic strategies aim to automate anomaly management, including real-time intervention.The section also describes a five-generation framing, from single LLM reasoners to autonomous cybersecurity pipelines.Source: §3.3 · p. 5

Machine-Learning and Reinforcement-Learning Agents

  • Several agent-based approaches depend on data collected from sources such as firewalls and intrusion-detection systems; one reinforcement-learning system reportedly outperformed a rule-based engine.Other reviewed systems use layered cognitive analysis and reinforcement-learning response policies, or propose an 11-dimensional notation framework for agentic AI functions.Source: §3.3 · pp. 5–6

Collaborative LLM Agents

  • Multi-agent LLM systems such as IRCopilot [42], CyberSleuth [43], and CORTEX [44] coordinate reasoning for incident response, forensics, or alert investigation, while long, high-stakes investigations challenge traditional single-agent approaches [42] [43] [44].CyberSleuth analyzes packet captures and application logs to identify a targeted service, exploited CVE, attack success, and produce structured forensic reports for SOC analysts.Source: §3.3 · p. 6

First-Responder Fit

  • Systems analyzed depend on technical inputs such as PCAP files, security logs, and raw network traffic, and produce forensic reports, triage decisions, or attack reconstructions.Their intended users are specialist SOC analysts, DFIR investigators, and blue teams; these formats and data types may not suit police seeking legally admissible evidence summaries and simplified narratives.Source: §3.3 · p. 6

4. Evaluating the limitations of Playbooks, LLMs & RAG, and Agentic AI

The section contrasts limitations across playbooks, LLM/RAG systems, and agentic AI. The constraints include interoperability and maintenance challenges, model and retrieval reliability risks, and agentic systems’ security and deployment burdens.

4.1. Playbook-driven

  • Unstructured playbooks hinder machine-readable response, compliance verification, reporting, and inter-organizational sharing, while structured workflows face portability, conversion, and model-to-execution consistency challenges.Maintaining consistency between workflow models and executable implementations can be difficult, with governance and structural divergence risks [23, 24, 25].Source: §4.1 · p. 6

4.2. LLMs and RAG

  • LLM and RAG systems face context-length and short-term-memory limits, hallucinations, retrieval errors from selecting the wrong context chunk, privacy risks, and a need for human oversight.The reviewed evaluations are often limited to controlled environments and may not capture real-world complexity [25] [34] [26] [42].Source: §4.2 · p. 6

4.3. Agentic AI (AAI)

  • Agentic AI expands the attack surface because a breach in one agent can propagate, and adversarial data poisoning, prompt injection, or evasion can mislead agents toward suboptimal states.These risks accompany AAI’s design goal of addressing detection latency and LLM adaptability limits.Source: §4.3 · p. 6, Figure 4 · p. 7
  • Agentic workflows raise privacy risks and can lack governance explainability and court reproducibility, complicating ethical traceability and human oversight.Unapproved Shadow AI use may expose private company information to public models; multi-agent interactions also increase model-inversion and data-extraction risks.Source: §4.3 · p. 7
  • Infrastructure is a major challenge to practical deployment, alongside an acute shortage of skilled professionals to manage diverse multiagent architectures and the dual-use dilemma of defensive tools being repurposed for malicious activities.Source: §4.3 · p. 7

5. Comparative Evaluation from a First-Responder Perspective

The comparison evaluates response architectures by operational feasibility for frontline officers facing limited expertise, time, and forensic infrastructure. RAG-based assistance appears relatively viable, balancing contextual adaptability with lower infrastructure requirements than fully agentic systems.

5. Comparative Evaluation from a First-Responder Perspective

  • Operational feasibility must account for incomplete or unavailable logs, network traces, and endpoint telemetry, which can make structured-input systems inapplicable to early response.The survey warns that evaluations ignoring this variability risk overestimating real-world feasibility.Source: §5 · p. 8
  • Playbooks support procedural determinism and chain of custody, but detailed flowcharts can burden officers and static guidance may not adapt when expected artifacts are absent.The survey assumes baseline financial resources, excluding exceptionally funded institutions as few in number.Source: §5 · p. 8
  • Agentic systems require logging infrastructure, standardized APIs, automated artifact acquisition, and continuous telemetry, and may be too complex for nontechnical responders.In resource-variable environments without guaranteed log preservation, these systems might not be feasible.Source: §5 · p. 8
  • RAG-supported LLMs appear relatively viable for initial response, offering contextual adaptability with lower infrastructure demands than fully agentic systems.The survey frames this as a practical balance, not intrinsic superiority in every situation.Source: §5 · p. 8

6. Are LLMs reliable to gather incident response?

The section examines LLM reliability for first-hour incident-response questions and reports sensitivity to prompt wording. Semantically equivalent ransomware-shutdown queries produced inconsistent responses, motivating a benchmark tailored to officers' needs.

6. Are LLMs reliable to gather incident response?

  • LLM answers depend on context and prompt wording, with semantically equivalent prompts capable of producing different responses.The cited RobustAlpaca analysis found the best-case reward could be twice the worst-case reward for equivalent prompts.Source: §6 · p. 8
  • For Llama-2-13b-chat on RobustAlpaca, the best-case reward could be twice the worst-case reward for semantically equivalent prompts.Source: §6 · p. 8
  • DeepSeek gave inconsistent answers to five representative equivalent yes/no prompts about immediately shutting down a system during suspected ransomware infection.The passage warns that officers may be misled if unaware of this LLM behavior and calls for a benchmark focused on first-hour police response.Source: §6 · p. 8

7. Evaluation Benchmarks and Methodologies

The section groups cybersecurity benchmarks into knowledge and theoretical understanding, cyber threat intelligence analysis, and practical offensive and defensive tasks. It reviews their scope and notes limits relevant to incident response and first responders.

7.1. Knowledge and theoretical understanding

  • Knowledge benchmarks commonly use MCQs or question-answering; CyberMetric covers nine cybersecurity domains with over 10,000 pairs, while SecEval uses 2,000 MCQs.SECURE focuses on industrial control systems, and the cited study reports generally higher performance by ChatGPT-4 and Gemini-Pro across most tasks than open-source models.Source: §7.1 · p. 9
  • The cited study reports theoretical success of 70–89%, falling to 20–40% in complex multistep adversarial scenarios and 22% in robotic targets.The section says these broad cybersecurity benchmarks focus on acquired knowledge rather than attack-handling capability.Source: §7.1 · p. 9

7.2. Cyber Threat Intelligence (CTI) Analysis

  • CTI benchmarks test tasks such as vulnerability categorization, severity prediction, ATT&CK extraction, and incident attribution, often against curated labels.AthenaBench adds a live-source evaluation pipeline and risk-mitigation-strategy task, but its GPT-generated scenarios are highly technical and may not fit first-responder needs.Source: §7.2 · p. 9

7.3. Practical offensive and defensive benchmarks

  • Practical benchmarks include CAIBench and CYBERSECEVAL, while IRBench assesses incident response across 130 subtasks and 27 case-machine categories.IRBench requires an automated system such as IRCOPILOT or a human-in-the-loop to execute instructions and return results; existing benchmarks may miss real-time context loss and hallucination challenges.Source: §7.3 · pp. 9–10

8. Conclusion on Existing Benchmarks

The section identifies gaps in cybersecurity benchmarks for evaluating LLM support to law enforcement and reviews methods for improving response robustness. It notes that linguistic variability can undermine confidence in benchmark-based evaluations.

8. Conclusion on Existing Benchmarks

  • Current benchmarks inadequately assess jargon-free guidance for non-specialists, hallucination consequences, alignment with police procedures, and legal admissibility.The section argues for evaluation that prioritizes operational safety and clarity over raw technical throughput.Source: §8 · p. 10
  • Proposed robustness strategies include chain-of-thought prompting, negation-based explanations, self-consistent sampling, paraphrase voting, ranked voting, and lookahead planning.These approaches seek to address inconsistent outputs or reduce hallucination risk through reasoning, aggregation, or action simulation.Source: §9 · p. 10
  • Latent Adversarial Paraphrasing trains on learnable latent perturbations intended to improve robustness to language variation while preserving semantic coherence.Source: §9 · p. 10
  • Observed performance drops under linguistic variation challenge whether high benchmark scores capture robustness to real-world input variation.Source: §9 · p. 10

10. conclusion and future scope

The survey identifies a usability–infrastructure gap between advanced AI systems and frontline law enforcement conditions. It finds RAG-based LLMs a pragmatic balance, while highlighting hallucination risks and the need for safer evaluation and deployment.

10. conclusion and future scope

  • A usability–infrastructure gap limits agentic AI adoption where logging practices are inconsistent and frontline technical maturity is low.The survey studies decision-support systems for early-stage cybercrime response and traces the shift from playbooks to agentic architectures.Source: §10 · pp. 11–12
  • RAG-based LLM systems offer a better pragmatic balance of contextual adaptability and lower deployment barriers, but hallucination and procedural inconsistency remain significant risks.An overconfident incorrect recommendation can irreversibly lose volatile evidence or violate legal chains of custody.Source: §10 · p. 12
  • Current benchmarks insufficiently assess the clarity, safety, and legal soundness of guidance for first responders interpreting ambiguous, non-technical queries.The survey calls for a specialized evaluation paradigm that measures these capabilities.Source: §10 · p. 12
  • Future work should develop robustness-centric benchmarks and domain-specific RAG systems grounded in cybercrime cases, procedures, and jurisdiction-specific legal frameworks.The proposed systems should include explainability and traceability so recommendations can be audited and justified in court-admissible contexts.Source: §10 · p. 12
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