LLMs in the Lab: Where Large Language Models Help and Where They Create Risk
Contributed by Kris Carlson, COO, Former ICAC Commander, Digital Forensics Investigator, and Testifying Expert
Series context. Part 3 of the Forensics and Futures series examines how large language models (LLMs) are being integrated into forensic and investigative workflows. Following Part 2’s focus on forensic readiness and defensibility, this installment addresses where LLMs add value, where they introduce new risk, and how organizations should govern their use in 2026. [1]
Why LLMs Are Fundamentally Different From Traditional Forensic Tools
Large language models are being adopted in investigative environments for one primary reason: scale. Modern investigations routinely involve millions of messages, emails, documents, and transcripts. LLMs enable navigation of that volume within timeframes that human-only review cannot sustain.
Common capabilities include summarizing large text collections, clustering conversations, identifying recurring themes, and surfacing entities across datasets. These functions make LLMs attractive for reviewing chat logs, collaboration platforms, email corpora, document repositories, and transcription outputs.
The distinction that matters is this: LLMs do not extract evidence; they interpret content. Traditional forensic tools acquire artifacts and preserve them in a verifiable state. LLMs operate one layer above that process, generating linguistic interpretations of the data rather than discovering new data.
LCG perspective. LLMs do not discover evidence. They generate interpretations that help examiners manage volume. Treating those interpretations as evidence is where technical convenience becomes legal risk.
Where LLMs Fit in a Defensible Forensic Workflow
When properly constrained, LLMs function best as pre-analytic or analytic-assist tools rather than decision engines. Defensible use cases mirror how investigators already work when handling large volumes of information.
Appropriate applications include:
- Evidence triage and prioritization
- Thematic clustering of communications
- Entity recognition across text-heavy datasets
- Hypothesis generation for examiner-led review
These uses support human judgment rather than replace it. Problems emerge when LLM output is treated as a finding rather than as an input requiring validation and corroboration. This distinction becomes critical in litigation, regulatory inquiries, and criminal matters, where conclusions must be explainable and reproducible.
Hallucination, Bias, and the Illusion of Coherence
Unlike deterministic forensic tools, LLMs are probabilistic systems. They generate responses that are linguistically coherent, not necessarily factually correct. Peer-reviewed research consistently documents risks of hallucination, embedded training bias, contextual misinterpretation, and over-generalization. [2]
In forensic contexts, the most dangerous failures are not obvious errors. They may exist as plausible narratives that align with investigator expectations and therefore go unchallenged. The fluency of LLM output can create an illusion of analytical rigor where none exists. This is not an edge-case risk. It is a structural characteristic of how LLMs function.
Explainability and the Expert Testimony Problem
Large language models are often described as “black box” systems, meaning their internal decision-making processes are not transparent or directly observable. While these systems can generate useful outputs, it is typically not possible to fully explain how a specific result was reached, what factors influenced it, or whether the same input would produce the same output at a later time. In investigative and legal contexts, this lack of transparency has significant implications. Conclusions or guidance derived from a system that cannot be independently explained, tested, or reproduced may be difficult, if not impossible, to validate, challenge, or defend, particularly where evidentiary standards, due process, and expert reliability are required.
Under Federal Rule of Evidence 702, expert testimony must be grounded in reliable principles and methods that can be explained and defended. If an examiner cannot articulate how an LLM operates at a functional level, what data it was exposed to, what constraints were applied, and how outputs were validated, reliance on that output becomes vulnerable under Daubert reliability challenges. [3][4]
An examiner cannot credibly defend a methodology that amounts to “the model said so.” Courts expect process, not mystique.
Validation Is Not Optional
Forensic tools are traditionally validated through repeatable testing. LLMs complicate this expectation due to non-deterministic outputs, model updates outside examiner control, prompt sensitivity, and context window limitations. NIST has made clear that AI systems used in high-risk or consequential contexts must be risk-managed, documented, and subject to meaningful human oversight. [5]
In practical terms, forensic use of LLMs must be:
- Documented with sufficient detail to reconstruct the analysis
- Repeatable where possible through controlled prompts and datasets
- Explicitly constrained in scope
- Reviewed and validated by a qualified examiner
Absent these controls, LLM-assisted analysis becomes difficult to defend under scrutiny.
Authentication and Attribution Risks
LLMs are particularly ill-suited for tasks that imply authorship determination, intent attribution, or factual reconstruction. While they can summarize the text, they cannot reliably determine who authored it, whether it is authentic, or whether it reflects the intent.
Treating LLM-generated summaries as evidence risks collapsing analysis and attribution into a single, unjustified step. Under Federal Rule of Evidence 901, authentication must be grounded in evidence, not inference layered on inference. [7]
Confidentiality and Data Security Warning Regarding Web-Based AI Tools
Users should exercise extreme caution when entering sensitive, confidential, or case-related information into web-based artificial intelligence tools, including publicly accessible large language models. Information submitted to these systems may be transmitted to and stored on third-party servers outside the user’s control. Depending on the provider’s terms of service, submitted content may be logged, retained, reviewed, or used to improve the system.
Uploading case facts, client communications, privileged materials, personal data, or investigative findings to a web-based AI platform may result in loss of confidentiality, waiver of privilege, or unintended disclosure. Once information is shared with an external AI service, it may no longer be possible to control, retrieve, or delete that data fully.
Accordingly, web-based AI tools should not be used to process or analyze sensitive case information unless the platform has been expressly vetted and approved for confidentiality, data handling, and security compliance, and such use is consistent with applicable legal, ethical, and contractual obligations.
Examiner-Centered Controls That Matter
- Constrain Use Cases Explicitly
Organizations should clearly define what LLMs may and may not be used and what LLMs may and may not be used for, AND where human review is mandatory. This prevents scope creep and gradual erosion of defensibility.
- Preserve Inputs, Prompts, and Outputs
To defend LLM-assisted analysis, investigators must preserve the source data provided to the model, the prompts or instructions used, the model identity and version where available, generated outputs, and examiner interpretations. Without this record, reconstruction and explanation are impossible.
- Treat LLM Output as Hypothesis, Not Conclusion
LLM outputs should prompt follow-up examination, corroboration with original artifacts, and independent validation. They should never replace the examiner’s judgment.
Governance and Regulatory Signals
While U.S. law has not yet directly regulated the use of LLMs in investigations, governance expectations are emerging rapidly. The NIST AI Risk Management Framework emphasizes accountability, transparency, and human oversight for AI systems used in consequential contexts. [5]
The EU Artificial Intelligence Act goes further, signaling heightened scrutiny for AI systems that influence legal, employment, or regulatory outcomes. [8]
Organizations deploying LLMs without governance are creating future compliance and defensibility problems today.
Why This Matters to Organizations
LLMs are already embedded in eDiscovery platforms, internal investigation tools, compliance analytics, and security operations. Organizations that fail to explicitly govern their use risk unverifiable conclusions, compromised expert testimony, discovery challenges, and regulatory exposure.
Forensic readiness in 2026 includes AI literacy, documentation discipline, and governance controls, not just technical extraction capability.
Frameworks and Guardrails
Relevant authorities include the NIST AI Risk Management Framework, NIST scientific foundations of digital investigation, Federal Rules of Evidence 702 and 901, Daubert reliability standards, and peer-reviewed research on LLM hallucination and adversarial risk. [2][3][4][5][6][7][9]
Together, they reinforce a consistent principle: LLMs can assist analysis, but they do not establish truth.
Quick Checklist
- Use LLMs for triage and pattern detection, not conclusions.
- Preserve prompts, inputs, and outputs for defensibility.
- Require examiner review and independent corroboration. [5]
Final Thought
Large language models offer real operational value in forensic analysis, but only when their role is clearly bounded. In 2026, the credibility of LLM-assisted investigations will depend less on model sophistication and more on whether human judgment, documentation, and validation remain firmly in control. [1]
References (endnotes)
[1] LCG Discovery. Forensics and Futures 2026 Series Outline. Internal planning document (on file with LCG Discovery).
[2] Ji, Z., Lee, N., Frieske, R., et al. Survey of Hallucination in Natural Language Generation. arXiv:2301.12867 (2023).
https://arxiv.org/pdf/2301.12867.pdf
[3] Legal Information Institute, Cornell Law School. Federal Rules of Evidence, Rule 702: Testimony by Expert Witnesses.
https://www.law.cornell.edu/rules/fre/rule_702
[4] U.S. Supreme Court. Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).
https://www.law.cornell.edu/supct/html/92-102.ZS.html
[5] National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1 (2023).
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
[6] Wei, J., Haghtalab, N., Steinhardt, J., et al. Jailbreak: Attacking Large Language Models via Prompt Injection. arXiv:2307.02483 (2023).
https://arxiv.org/pdf/2307.02483.pdf
[7] Legal Information Institute, Cornell Law School. Federal Rules of Evidence, Rule 901: Authenticating or Identifying Evidence.
https://www.law.cornell.edu/rules/fre/rule_901
[8] European Union. Regulation (EU) 2024/1689 of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act). Official Journal of the European Union (2024).
https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202401689
[9] National Institute of Standards and Technology (NIST). Digital Investigation Techniques: A NIST Scientific Foundation Review. NIST Interagency Report 8354 (2022).
https://nvlpubs.nist.gov/nistpubs/ir/2022/NIST.IR.8354.pdf
This article is for general information and does not constitute legal advice.
