Why autonomous evidence analysis endangers admissibility and accuracy

Contributed by Thane Russey, VP, Strategic AI Programs, LCG Discovery Experts

Series context

The Beyond Automation series examines how increasing reliance on automation, analytics, and artificial intelligence is reshaping investigative practice. Earlier installments explored efficiency gains and emerging dependencies. Part 3 confronts a more complicated truth: in an AI-first investigative environment, the most significant risk is no longer volume or speed, but silent distortion of evidence integrity.

The emerging integrity crisis

Digital forensics has always been grounded in a simple premise: artifacts reflect reality. Logs, timestamps, metadata, file fragments, and system states provide a factual substrate for investigators to reconstruct events. Automation has long assisted this process by accelerating parsing, correlation, and search while preserving determinism.

AI changes the nature of assistance. Instead of executing predictable, rule-based tasks, AI systems classify, infer, summarize, suppress, and sometimes generate content. In doing so, they no longer merely handle evidence. They transform it.

This transformation creates an integrity crisis that most investigative teams are not yet equipped to manage. Evidence may remain technically available. Reports may look polished. Workflows may appear defensible. Yet underlying artifacts may be altered, deprioritized, or mischaracterized by opaque models whose behavior cannot be fully reconstructed or explained in court. [2][3][4]

AI misclassification and artifact suppression

Modern AI-driven forensic tools increasingly rely on automated triage. Machine learning models decide which artifacts are relevant, which are noise, and which can be ignored. At scale, this appears efficient. In practice, it introduces risk in three critical ways.

First, misclassification is inevitable. Models are trained on historical data that encodes assumptions about what matters. Novel attack techniques, uncommon applications, jurisdiction-specific artifacts, or atypical user behavior are more likely to be mislabeled or excluded.

Second, suppression is often invisible. Unlike keyword searches that can be rerun or audited, AI-driven prioritization frequently hides deprioritized artifacts entirely. Examiners may never know what they did not see.

Third, confidence replaces curiosity. When systems label results as high confidence, human reviewers are less likely to challenge exclusions. Over time, this erodes the investigative instinct that historically surfaced anomalies and edge cases.

In an AI-first workflow, the absence of evidence may reflect model behavior rather than factual reality.

Chain of custody in a world of transformation

Traditional chain-of-custody frameworks assume evidence is preserved, copied, and analyzed without substantive alteration. [5] Hashing, write blockers, logging, and access controls are in place to ensure fidelity between original artifacts and analytical outputs.

AI complicates this assumption. When models normalize logs, extract features, summarize content, or convert unstructured data into embeddings, the artifacts examiners interact with are no longer the originals. The operative evidence becomes a derivative representation.

This raises foundational questions:

• When does analysis become alteration?
• Which version of the data is the evidence: the original artifact or the AI-transformed output?
• How is provenance documented when model weights, prompts, or inference parameters evolve?

Without rigorous controls, AI systems fracture the chain of custody into parallel realities: pristine originals preserved for formality and transformed derivatives used to draw conclusions. Courts are increasingly attentive to this gap.

Hallucination risk in forensic triage and narrative generation

The use of generative AI for investigative summaries, timelines, and narratives introduces an additional risk: hallucination.

Generative models are optimized for coherence, not truth. [8][9] When asked to explain gaps, reconcile inconsistencies, or infer intent, they may fabricate plausible-sounding connections unsupported by evidence.

In triage contexts, hallucination can:

• Attribute actions to users without corroboration
• Smooth over missing artifacts
• Imply causality where coincidence exists

In reporting contexts, the risk intensifies. AI-generated narratives may blend factual artifacts with inferred explanations, blurring the line between evidence and interpretation. Once embedded in reports, these narratives are difficult to unwind under cross-examination.

Standards strain: FRE 702, Daubert, and ISO/IEC 27037

Existing forensic standards were not written with autonomous analysis in mind, yet courts continue to apply them.

Under FRE 702 and Daubert, expert testimony must be based on reliable principles and methods applied reliably to the facts of the case. [2][3][4] When AI systems cannot fully explain how conclusions were reached, establishing reliability becomes difficult.

ISO/IEC 27037 emphasizes the identification, collection, acquisition, and preservation of digital evidence. [5] AI-driven transformation strains the preservation principle by inserting non-deterministic processes between acquisition and analysis.

ISO/IEC 27041 further reinforces the expectation that investigative methods be demonstrably fit for purpose. [6] That expectation becomes harder to satisfy as models change across versions, retraining cycles, and prompt variations.

As AI adoption accelerates, the gap between tool capability and standard expectations widens. Without explicit governance, organizations risk improving efficiency while undermining admissibility.

The human examiner as a safeguard

In an AI-first world, human examiners are not an inefficiency. They are the control.

Human expertise provides:

• Contextual judgment when artifacts defy expected patterns
• Adversarial thinking that challenges model output
• The ability to explain reasoning in human terms to courts and regulators

This does not require rejecting AI. It requires redefining roles. AI should assist with scale and correlation. Humans must retain authority over interpretation, inclusion, exclusion, and narrative formation.

The safeguard is not better models alone. It is governance that mandates human review, enforces explainability thresholds, and preserves access to raw artifacts.

What forensic readiness looks like now

For organizations operating in an AI-first investigative environment, forensic readiness requires new controls:

• Explicit documentation of where AI transforms evidence
• Preservation of original artifacts alongside derivatives
• Auditability of model versions, prompts, and parameters
• Policies prohibiting autonomous narrative generation without human validation
• Training examiners to challenge AI output rather than defer to it

These controls are no longer optional. They are becoming prerequisites for defensibility.

Final thought

Automation promised speed. AI promises insight. But without discipline, both can erode trust.

The integrity crisis facing digital forensics is not hypothetical. It is unfolding quietly as autonomous systems reshape evidence before humans ever see it.

This is precisely how silent distortion creeps in, not through hallucination, but through unchallenged repetition, transformation, and misplaced confidence.

The organizations that navigate this transition successfully will be those that recognize a simple truth: in digital forensics, accuracy and admissibility matter more than efficiency. In the age of AI, integrity remains a human responsibility.

References (Endnotes)

[1] LCG Discovery. Beyond Automation Series Framework. Internal research note.

[2] 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

[3] Supreme Court of the United States. Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).
https://tile.loc.gov/storage-services/service/ll/usrep/usrep509/usrep509579/usrep509579.pdf

[4] Supreme Court of the United States. Kumho Tire Co., Ltd. v. Carmichael, 526 U.S. 137 (1999).
https://tile.loc.gov/storage-services/service/ll/usrep/usrep526/usrep526137/usrep526137.pdf

[5] ISO/IEC. ISO/IEC 27037:2012 – Guidelines for identification, collection, acquisition, and preservation of digital evidence.
https://www.iso.org/standard/44381.html

[6] ISO/IEC. ISO/IEC 27041:2015 – Guidance on assuring suitability and adequacy of incident investigative method.
https://www.iso.org/standard/44405.html

[7] The Sedona Conference. TAR 1 Reference Model: An Established Framework Unifying Traditional and GenAI Approaches to Technology-Assisted Review. Sedona Conference Journal, Vol. 25, No. 1 (2024).
https://www.thesedonaconference.org/publication/tar-reference-model

[8] National Institute of Standards and Technology. 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

[9] National Institute of Standards and Technology. Generative Artificial Intelligence Profile. NIST AI 600-1 (2024).
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

[10] National Institute of Standards and Technology. Digital Evidence Preservation: Considerations for Evidence Handlers. NIST IR 8387 (2022).
https://nvlpubs.nist.gov/nistpubs/ir/2022/NIST.IR.8387.pdf

[11] National Institute of Justice. Digital Evidence Policies and Procedures for Law Enforcement. U.S. Department of Justice (2020).
https://www.ojp.gov/pdffiles1/nij/254661.pdf

[12] European Union Agency for Cybersecurity (ENISA). ENISA Threat Landscape 2025 (v1.2, January 2026).
https://www.enisa.europa.eu/publications/enisa-threat-landscape-2025