From Digital Artifacts to Judicial Findings: Applying the J-DEAF Framework Through AI-Assisted Forensic Review

By Dr. Shani Roffeh, Adv

Published October 22, 2026

Judicial Competence in Digital Evidence — Research Article III

Abstract

The increasing reliance on digital forensic evidence in judicial proceedings raises a fundamental question: how can courts distinguish between technologically accurate findings and expert conclusions that extend beyond what the underlying evidence establishes?

The first article in this research series identified the judicial knowledge gap arising from the distinction between digital data, forensic findings, expert interpretation, and legal conclusions. The second introduced the Judicial Digital Evidence Assessment Framework (J-DEAF), a proposed seven-domain analytical protocol designed to assist legal professionals in evaluating the reliability and evidentiary significance of digital expert opinions.

This third article moves from conceptual development to practical application.

Using a hypothetical forensic investigation involving the alleged unauthorized disclosure of confidential documents, the article demonstrates how J-DEAF can identify inferential gaps, incomplete examinations, unsupported attribution, and uncertainties concealed within apparently conclusive forensic reports.

Particular attention is devoted to the Invisible Evidence Problem: the possibility that automated forensic tools may filter, classify, exclude, or fail to display potentially relevant information before the examiner begins substantive interpretation.

The article further introduces S.N.R-IQ, an AI-assisted forensic review system currently operating in beta, as an emerging technological implementation relevant to structured evaluation of digital expert evidence.

Although S.N.R-IQ can already analyze forensic expert reports and identify potential discrepancies between technical findings and expert conclusions, its effectiveness has not yet been established through independent empirical validation or systematic deployment in law firms.

The article proposes a transparent model of human-supervised, AI-assisted evidentiary assessment and outlines the methodological safeguards necessary for future evaluation.

Keywords: Digital Evidence; Digital Forensics; J-DEAF; S.N.R-IQ; Artificial Intelligence; Expert Testimony; Federal Rule of Evidence 702; Judicial Competence; Forensic Transparency; Inferential Reliability; Automated Evidence Filtering.

1. Introduction: From Analytical Principles to Practical Examination

Digital forensic evidence occupies an increasingly important position in contemporary litigation.

Courts routinely encounter expert reports derived from computer examinations, mobile devices, cloud services, electronic communications, network logs, and automated forensic applications.

These reports frequently contain technically sophisticated findings presented in a manner that may suggest scientific certainty.

Yet technological sophistication does not necessarily establish the correctness of the conclusions drawn from those findings.

A forensic application may accurately identify a file, record an authentication event, recover deleted information, or reconstruct a sequence of system operations. Whether those observations establish human conduct, knowledge, intention, or responsibility is a separate question.

The first article in this series, The Judicial Knowledge Gap in Digital Evidence: From Technical Findings to Legal Conclusions, examined the distinction between four analytical levels:

(1) Underlying digital data.

(2) Forensic findings generated through examination.

(3) Expert interpretation of those findings.

(4) Judicial conclusions concerning disputed facts.

The second article, Developing the J-DEAF Assessment Framework: A Structured, Testable Approach to Digital Expert Evidence under U.S. Evidentiary Standards, proposed a seven-domain protocol for examining the relationship between these levels.

Those domains are source, acquisition, integrity, methodology, interpretation, transparency, and evidentiary significance.

The present article addresses the next question:

How can this analytical framework be applied to an actual examination process, and what role might artificial intelligence play in identifying gaps that legal professionals may otherwise overlook?

The objective is not to replace forensic expertise or judicial discretion.

Rather, it is to explore whether a structured, technologically assisted process can make the reasoning underlying digital expert opinions more visible, reviewable, and open to challenge.

2. The Legal Foundation: Reliability Requires More Than Technical Accuracy

2.1 Federal Rule of Evidence 702

Under Federal Rule of Evidence 702, expert testimony must satisfy requirements concerning the expert's qualifications, the sufficiency of the factual basis, the reliability of the principles and methods employed, and the reliable application of those principles and methods to the facts.

The 2023 amendment expressly addresses the requirement that an expert opinion reflect a reliable application of the methodology to the facts of the case.

The Advisory Committee's explanation emphasizes that courts must assess whether the proponent has established the applicable admissibility requirements by a preponderance of the evidence.

This distinction is particularly important in digital forensic examinations.

The fact that a recognized forensic application produced a particular result does not automatically establish that the examiner's interpretation of that result is reliable.

A tool may correctly identify an authentication event without establishing the identity of the individual who initiated it.

Similarly, a recovered file may be authentic without demonstrating that its owner knowingly created, accessed, or transmitted it.

The court's responsibility is therefore not limited to determining whether the technology functioned correctly.

It must also examine whether the expert's conclusions remain within the boundaries supported by the available information.

2.2 The Analytical Gap Identified in Joiner

In General Electric Co. v. Joiner, 522 U.S. 136 (1997), the Supreme Court recognized that a court may reject expert testimony when the analytical gap between the data and the opinion is too great.

The principle is not confined to digital evidence.

Nevertheless, digital forensic examinations provide particularly important examples because technical observations can acquire meanings that extend substantially beyond their original function.

A computer log may establish that a system recorded an event.

An expert may interpret that event as evidence of human activity.

A party may then present the expert's interpretation as proof of misconduct.

Each transition requires an evidentiary foundation.

The central function proposed for J-DEAF is to make those transitions explicit and subject to systematic examination.

3. A Hypothetical Case: The Confidential Document Investigation

3.1 The Allegation

Consider a hypothetical civil proceeding involving the alleged unauthorized disclosure of confidential corporate documents.

An employee is accused of transferring sensitive commercial information from a company computer to an external recipient.

The employer commissions a digital forensic examination.

The resulting expert report identifies several findings:

A confidential document existed on the employee's assigned workstation.

The file was accessed during the relevant period.

System records indicate activity involving an external storage or communication mechanism.

The employee's account was authenticated during the relevant timeframe.

A forensic application generated a timeline associating these events.

The expert concludes that the employee personally transferred the confidential document to an unauthorized third party.

The employer relies on that conclusion as a central component of its case.

3.2 The Apparent Strength of the Evidence

At first glance, the report appears persuasive.

The workstation was examined using forensic software.

The acquired data was preserved.

Hash values were recorded.

The examination produced a detailed technical timeline.

The report includes screenshots, extracted records, and descriptions of the examination process.

None of these features is inherently problematic.

Indeed, they may support the technical reliability and reproducibility of particular findings.

The difficulty arises when the conclusion concerning the employee's personal conduct is treated as established merely because the underlying digital records were accurately recovered.

3.3 The Missing Inferential Steps

The report may leave several questions unanswered.

Was the workstation used exclusively by the employee?

Could the account have been accessed remotely?

Did the recorded file access result from deliberate human action or an automated process?

Does the external-device record demonstrate that the particular confidential document was transferred?

Was there independent evidence of transmission to the alleged recipient?

Were alternative explanations considered and documented?

These questions do not establish that the expert's conclusion is incorrect.

They establish that the strength of the conclusion depends on information and reasoning that must be examined independently.

That distinction is central to J-DEAF.

4. Applying the Seven J-DEAF Domains

4.1 Source: What Did the System Actually Record?

The first domain examines the origin and meaning of the underlying digital information.

A system record should not be interpreted without understanding the mechanism responsible for generating it.

In the hypothetical investigation, the examiner must identify the relevant operating system, logging mechanism, application behavior, and event definitions.

For example, an event indicating that a file was opened may have a different meaning depending on whether it originated from an application log, a file-system artifact, or an automated indexing process.

The evaluator should ask:

What event does the source actually record?

What technical conditions generate that event?

Can the same record arise without the conduct alleged?

The objective is to establish the meaning of the original observation before assigning evidentiary significance to it.

4.2 Acquisition: What Information Was Collected?

The second domain examines the scope and limitations of the forensic acquisition.

An examination may preserve the acquired material accurately while omitting potentially relevant categories of information.

For example, the examiner may have acquired the workstation's local storage without obtaining cloud synchronization records, remote-access logs, or server-side communication data.

Such omissions do not necessarily invalidate the examination.

However, they may limit the conclusions that can reasonably be drawn.

The evaluator must distinguish between evidence that does not exist, evidence that was unavailable, and evidence that was never examined.

These are materially different circumstances.

4.3 Integrity: What Does Preservation Establish?

Hash verification and documented chain-of-custody procedures can provide important assurance that acquired data has remained unchanged.

They do not, however, establish the truth of every proposition derived from that data.

A verified forensic image may accurately preserve a misleading record.

A correctly extracted log may contain an event whose meaning has been misunderstood.

The integrity inquiry must therefore remain separate from the interpretation inquiry.

The relevant question is not whether hash verification is valuable.

It is whether the expert has attributed to that verification conclusions that it cannot independently support.

4.4 Methodology: Was the Examination Appropriate to the Question?

The fourth domain concerns the methods and tools employed.

Digital forensic applications perform a wide range of functions, including extraction, indexing, parsing, classification, timeline generation, and recovery of deleted information.

Their reliability must be considered in relation to the particular task performed.

A tool that reliably identifies a file-system artifact does not necessarily determine the identity or intention of a human actor.

The evaluator should therefore examine the tool version, relevant settings, verification procedures, known limitations, and whether the method was appropriate for the proposition under investigation.

4.5 Interpretation: Where Does Observation Become Inference?

This domain addresses the central analytical difficulty.

The examiner may observe that an account was authenticated and that a file-related event occurred.

The conclusion that the employee personally transmitted the document requires additional reasoning.

That reasoning may depend on assumptions concerning account control, device access, remote connectivity, automated activity, and the relationship between the recorded events.

A structured assessment should identify each assumption and determine whether it is supported by evidence.

The presence of a plausible alternative explanation does not automatically defeat the expert's conclusion.

Its importance depends on the evidence supporting or excluding that explanation and on the applicable legal standard.

4.6 Transparency: Can the Examination Be Meaningfully Reviewed?

Transparency requires more than a lengthy technical report.

A report may contain hundreds of pages of extracted information while providing little explanation of how the examiner selected relevant artifacts or excluded alternative interpretations.

Meaningful review may require access to examination notes, tool settings, source records, verification procedures, and descriptions of analytical limitations.

Access remains subject to lawful disclosure restrictions, confidentiality protections, and the circumstances of the proceeding.

The essential question is whether another qualified examiner could meaningfully assess the reasoning that connects the data to the conclusion.

4.7 Evidentiary Significance: What Has Actually Been Established?

The final domain returns to the proposition that the evidence is offered to prove.

In the hypothetical case, the proposition is not merely that a file existed or that a system event occurred.

It is that a particular employee personally transferred confidential information without authorization.

The evaluator must determine which elements of that proposition are supported, which remain uncertain, and what corroboration may be necessary.

A properly limited conclusion might establish that the records are consistent with a transfer involving the workstation or account, while acknowledging that personal attribution remains dependent on additional evidence.

Such a conclusion may still be legally significant.

Its value lies in accurately communicating what the evidence can and cannot establish.

5. The Invisible Evidence Problem: Automated Filtering Before Expert Interpretation

5.1 The Problem of Pre-Interpretive Selection

A further difficulty arises before the expert begins interpreting the extracted information.

Modern forensic applications may process large quantities of digital material through automated classification, filtering, indexing, deduplication, and prioritization.

These operations are often necessary to make extensive datasets manageable.

However, they may also influence which information becomes visible to the examiner.

The examiner's conclusions may therefore depend not only on the underlying evidence, but also on the configuration and behavior of the software used to organize it.

This creates a distinct analytical problem.

How can an expert assess information that was never displayed, and how can a court evaluate the significance of information whose exclusion was not documented?

5.2 Three Categories of Evidentiary Absence

A useful analytical distinction can be drawn between three situations.

First: Information not acquired.

Relevant data may remain outside the examination because of technical limitations, unavailable credentials, inaccessible systems, or the defined scope of the investigation.

Second: Information acquired but filtered or excluded from the examiner's working view.

The underlying material may exist within the acquired dataset but be omitted from the displayed results because of search terms, filters, classification rules, or processing limitations.

Third: Information examined but omitted from the final expert report.

The examiner may have considered certain information but decided not to include it in the report.

These situations require different forms of scrutiny.

An acquisition limitation concerns the completeness of the available material.

A filtering limitation concerns the relationship between the acquired material and the information presented for examination.

A reporting omission concerns the relationship between the examination performed and the conclusions communicated.

They should not be treated as equivalent.

5.3 Why This Matters for Judicial Assessment

Consider a forensic report that identifies a series of file-access events suggesting deliberate activity.

If the software automatically excluded background synchronization records, the examiner might not have reviewed information relevant to an alternative explanation.

The problem is not that automated filtering is inherently unreliable.

Filtering is often essential to effective forensic work.

The problem arises when the scope and consequences of filtering are unknown or insufficiently documented.

Accordingly, the J-DEAF acquisition, methodology, and transparency domains should examine whether relevant processing decisions can be identified and assessed.

This includes asking whether the available documentation explains the scope of the extraction, the applicable filters, and any material categories that may have been excluded.

5.4 The Limits of Retrospective Review

Not every omitted item can be reconstructed.

Some data may never have been collected.

Other information may have been lost because of system behavior, retention policies, or technical limitations.

A reviewer should therefore avoid assuming that an incomplete record can always be made complete.

Where the original data or processing records are unavailable, the limitation itself should be documented.

An absence of information should not be automatically interpreted as evidence supporting either party.

6. Mapping the Inferential Chain

The practical value of J-DEAF lies in making the relationship between evidence and conclusion visible.

In the hypothetical investigation, the reasoning can be organized into four levels.

This model does not determine whether the employee is responsible.

It identifies the questions that must be answered before the technical findings can support that conclusion.

The framework also avoids a misleading numerical approach to reliability.

A strong acquisition process cannot compensate automatically for an unsupported attribution.

Likewise, a well-documented chain of custody does not eliminate uncertainty concerning human intention.

Each inferential transition must be examined on its own terms.

7. From Manual Assessment to AI-Assisted Review: Introducing S.N.R-IQ

7.1 The Rationale for Technological Assistance

The structured examination proposed by J-DEAF may require legal professionals to compare extensive expert reports, technical records, methodological descriptions, and disputed factual propositions.

This work can be demanding, particularly where reports contain large volumes of specialized terminology or multiple layers of inference.

Artificial intelligence offers a potential means of assisting the review process.

Rather than attempting to replace forensic examiners or judges, an AI-assisted system may help organize the relevant propositions, identify potential inconsistencies, and direct human attention toward questions requiring further examination.

The central objective is not automated adjudication.

It is improved visibility of the reasoning connecting technical findings to legal assertions.

7.2 S.N.R-IQ: An Existing Beta-Stage System

S.N.R-IQ is an AI-assisted evidence analysis system developed to support the examination of digital forensic expert reports and related evidentiary reasoning.

At the time of writing, the system is operating in beta.

Its existing capabilities include analyzing forensic expert reports and identifying potential gaps between the technical findings described in those reports and the conclusions advanced by the examiner.

This functionality is directly relevant to the problem addressed by J-DEAF.

The system provides an opportunity to investigate whether AI-assisted analysis can support the structured identification of inferential weaknesses, unsupported assumptions, and questions requiring professional clarification.

However, an important distinction must be maintained.

The existence of analytical functionality does not establish that the system's outputs are consistently correct, that it performs better than experienced legal professionals, or that its use improves judicial decision-making.

S.N.R-IQ has not yet undergone systematic deployment in law firms, and no independent controlled study establishing its effectiveness is presented in this article.

Accordingly, its current significance is as an operational beta-stage technology suitable for further structured evaluation.

7.3 The Relationship Between J-DEAF and S.N.R-IQ

J-DEAF and S.N.R-IQ should be understood as distinct but potentially complementary developments.

J-DEAF is a proposed legal-analytical framework.

S.N.R-IQ is a technological system capable of performing AI-assisted analysis of forensic expert reports.

The framework provides a structured set of questions concerning the relationship between data, methodology, interpretation, and legal significance.

The system offers a potential means of assisting the application of those questions to complex documentary material.

The two should not be conflated.

J-DEAF can be applied manually without S.N.R-IQ.

Similarly, the existence of S.N.R-IQ does not establish that every aspect of its operation has been validated against all seven J-DEAF domains.

A complete mapping between the framework and the system's implemented functions is a necessary part of future technical and empirical evaluation.

7.4 The Proposed Human-Supervised Review Model

A future AI-assisted workflow may involve several stages.

First, the legal reviewer identifies the precise proposition that the forensic opinion is intended to establish.

Second, the relevant expert report and supporting materials are examined.

Third, S.N.R-IQ performs an AI-assisted analysis, identifying potential inconsistencies, unsupported inferential transitions, or questions requiring further investigation.

Fourth, a qualified human reviewer evaluates the system's observations against the original material.

Finally, the reviewer determines whether additional forensic examination, clarification, or expert evidence is required.

This workflow is proposed as a research model, not as a representation that every stage is currently automated within S.N.R-IQ.

The distinction is essential because a system may identify a potentially unsupported conclusion without having access to all underlying source data.

In such circumstances, it may identify a documentation or reasoning gap, but it cannot necessarily establish that the original forensic conclusion is factually incorrect.

8. The Risks of AI-Assisted Forensic Review

The introduction of artificial intelligence into evidentiary assessment creates its own methodological challenges.

8.1 False Positives and False Negatives

An AI system may identify an inferential gap where the expert's reasoning is adequately supported.

Conversely, it may fail to identify a significant methodological weakness.

Both errors are relevant.

An effective evaluation must therefore measure not only the number of potential issues identified, but also their correctness, significance, and relevance.

8.2 Hallucination and Unsupported Technical Assertions

Generative AI systems may produce plausible explanations that are not supported by the material supplied.

In forensic review, such errors may be particularly serious because technical language can create an appearance of authority.

Every material observation generated by an AI-assisted system should therefore be traceable to identifiable source material or clearly marked as a hypothesis requiring verification.

8.3 Automation Bias

Legal professionals may assign excessive weight to system-generated conclusions.

A finding presented by sophisticated software may appear more objective than an equivalent human observation.

This risk is especially important where the system is intended to assist users who do not possess advanced technical expertise.

The reviewer must remain responsible for assessing the relevance and accuracy of the system's observations.

8.4 Confidentiality and Data Protection

Forensic reports may contain personal information, confidential communications, trade secrets, privileged material, or sensitive investigative records.

Any practical deployment of AI-assisted review must address lawful processing, access controls, confidentiality, data retention, and the handling of potentially privileged information.

These obligations depend on the jurisdiction, the nature of the proceedings, and the technical architecture of the system.

8.5 Reproducibility and Version Control

AI-assisted analysis may be affected by model versions, configuration changes, processing settings, and differences in supplied material.

A defensible research protocol should record relevant system versions, input documents, analytical settings where available, and the resulting outputs.

Without adequate documentation, later reviewers may be unable to determine why different results were produced.

9. A Proposed Empirical Evaluation of AI-Assisted J-DEAF Review

9.1 Research Question

The next stage of this research should examine whether AI-assisted assessment improves the ability of legal professionals to identify inferential gaps in digital forensic expert reports.

The proposed research question is:

Does the use of S.N.R-IQ in a structured J-DEAF assessment improve the accuracy, completeness, or efficiency of identifying unsupported forensic inferences compared with conventional legal review?

This question is empirical.

It cannot be answered merely by demonstrating that the system generates analytical observations.

9.2 Proposed Comparative Design

A controlled study could compare three groups.

Group A: Legal professionals conducting conventional review without a structured J-DEAF worksheet or AI assistance.

Group B: Legal professionals using the manual J-DEAF assessment protocol.

Group C: Legal professionals using J-DEAF with assistance from S.N.R-IQ.

Participants would examine standardized forensic reports containing a mixture of adequately supported conclusions, genuine inferential weaknesses, and deliberately constructed ambiguities.

The study should use an independently developed reference standard and assess whether participants correctly distinguish between reliable conclusions and unsupported extensions.

9.3 Proposed Outcome Measures

The primary outcome should be the accurate identification of material inferential weaknesses.

Secondary outcomes may include:

False-positive and false-negative rates.

Identification of plausible alternative explanations.

Recognition of missing or filtered information.

Quality of clarification questions directed to forensic experts.

Time required to complete the assessment.

Confidence calibration.

Agreement between independent evaluators.

The study should also include cases in which the expert's conclusions are well supported.

Otherwise, a system that simply encourages generalized skepticism might appear successful without improving evidentiary reasoning.

9.4 Research Safeguards

The research design should be established before collecting outcome data.

Appropriate safeguards would include independent expert review of the test materials, blinded assessment where feasible, predefined evaluation criteria, and documentation of system versions.

If practicing lawyers or other human participants are recruited, applicable ethics and confidentiality requirements must be considered.

Commercial involvement and the researcher's relationship to S.N.R-IQ should be disclosed.

Results should distinguish between improved performance on standardized exercises and demonstrated improvements in actual litigation.

The latter cannot be inferred automatically from the former.

10. Implications for Legal Practice and Judicial Education

The proposed approach has implications beyond the examination of individual expert reports.

Digital evidence is increasingly encountered by legal professionals whose principal expertise lies outside computer science.

A family lawyer may need to assess the significance of messaging metadata.

An employment lawyer may encounter an expert opinion concerning workplace computer activity.

A criminal defense attorney may need to examine mobile-device extraction results.

A commercial litigator may confront allegations based on cloud records or electronic document histories.

In each setting, the challenge is not necessarily to become a digital forensic examiner.

It is to recognize the distinction between a technical observation and the legal proposition that the observation is offered to establish.

A structured analytical framework may help legal professionals formulate more precise questions and recognize when specialized clarification is necessary.

AI-assisted systems may eventually support that process by helping reviewers organize complex material and identify issues warranting attention.

However, the educational value of such systems must be evaluated independently.

A system that produces detailed criticism without sufficient accuracy could increase confusion rather than reduce it.

The objective should therefore be calibrated professional judgment, not the automatic generation of objections to expert evidence.

11. Discussion: The Boundaries of Technological Assistance

The development of J-DEAF and the emergence of S.N.R-IQ raise a broader question concerning the future relationship between artificial intelligence and judicial reasoning.

The problem addressed in this research series is not simply that judges and lawyers may lack specialized technical knowledge.

It is that the translation of technical findings into legal conclusions requires an identifiable and defensible chain of reasoning.

That chain may contain assumptions, methodological limitations, missing information, or alternative explanations.

The introduction of artificial intelligence does not eliminate those difficulties.

Indeed, AI-assisted analysis may create an additional interpretive layer that itself requires scrutiny.

A responsible approach must therefore apply comparable demands of transparency, traceability, and critical evaluation to the AI-assisted review process.

The relevant question is not whether an AI system produces an impressive analysis.

It is whether the analysis identifies material issues accurately, explains their evidentiary relevance, and enables meaningful human verification.

The proposed contribution of combining structured legal assessment with technological assistance lies in making those questions more systematic and potentially easier to examine.

Whether that contribution translates into measurable improvements remains a subject for future research.

12. Conclusion

Digital forensic evidence can be technically accurate while supporting conclusions that exceed what the underlying information establishes.

The first article in this series identified the judicial knowledge gap associated with that distinction.

The second proposed J-DEAF as a structured, testable framework for examining the relationship between technical findings, expert interpretation, and legal propositions.

This third article has demonstrated how the framework can be applied to a hypothetical forensic investigation involving alleged unauthorized disclosure of confidential information.

It has also identified the Invisible Evidence Problem as a significant issue for further examination: information may be unavailable, filtered, or omitted before its evidentiary significance is considered.

Finally, the article has introduced S.N.R-IQ as an existing beta-stage AI-assisted system capable of analyzing forensic expert reports and identifying potential gaps between technical findings and expert conclusions.

Its present capabilities provide a basis for future empirical investigation, but not yet for claims of independently established effectiveness.

The next research stage should examine whether structured AI-assisted review produces measurable improvements over conventional and manually structured legal assessment.

The objective is not to transfer judicial judgment from humans to machines. It is to ensure that neither human expertise nor technological authority substitutes for a demonstrable connection between evidence and conclusion.

A digital finding becomes legally persuasive not because it is generated by sophisticated technology, but because the reasoning connecting that finding to the disputed proposition can withstand meaningful scrutiny.

References

[1] Roffeh, S. (2026). The Judicial Knowledge Gap in Digital Evidence: From Technical Findings to Legal Conclusions. October 17, 2026. https://www.roffeh.com/articles/judicial-knowledge-gap-digital-evidence

[2] Roffeh, S. (2026). Developing the J-DEAF Assessment Framework: A Structured, Testable Approach to Digital Expert Evidence under U.S. Evidentiary Standards. October 19, 2026. https://www.roffeh.com/articles/j-deaf-assessment-framework-digital-expert-evidence

[3] Federal Rule of Evidence 702, including the 2023 amendment and Advisory Committee Note. https://www.law.cornell.edu/rules/fre/rule_702

[4] Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993).

[5] General Electric Co. v. Joiner, 522 U.S. 136 (1997).

[6] Kumho Tire Co. v. Carmichael, 526 U.S. 137 (1999).

[7] National Institute of Standards and Technology. (2022). Digital Investigation Techniques: A NIST Scientific Foundation Review. NISTIR 8354.

[8] National Institute of Standards and Technology. (2006). Guide to Integrating Forensic Techniques into Incident Response. NIST SP 800-86.

[9] National Institute of Standards and Technology. (2025). Informational Scientific Primers for Officers of the Court: Intended to Strengthen Use of Forensic Science Evidence. NIST SP 1500-28.

[10] Goodison, S. E., Davis, R. C., & Jackson, B. A. (2015). Digital Evidence and the U.S. Criminal Justice System: Identifying Technology and Other Needs to More Effectively Acquire and Utilize Digital Evidence. RAND Corporation / National Institute of Justice.

[11] Scientific Working Group on Digital Evidence (SWGDE). Best Practices for Computer Forensic Examinations, Version 2.0.

[12] Scientific Working Group on Digital Evidence (SWGDE). Requirements for Report Writing in Digital and Multimedia Forensics.

[13] Federal Rules of Evidence 901 and 902.

[14] S.N.R-IQ. Evidence Intelligence for Lawyers. Beta-stage system information. https://www.roffeh.com/eviq

Research and Technology Disclosure

The author is associated with the development of the J-DEAF research framework and S.N.R-IQ. The system is described in this article as an existing beta-stage technology. No independent validation, comparative performance advantage, judicial endorsement, or demonstrated improvement in legal outcomes is claimed. The case study is hypothetical and does not constitute an empirical evaluation of the system.