AI & Digital Forensics - Review of Veksler et al. (2025) Practitioner-driven framework for AI adoption...

Veksler et al. (2025) Practitioner-driven framework for AI adoption in digital forensics

This report presents guidelines for digital forensics examinersʼ use of artificial intelligence (AI), based exclusively on Practitioner-driven framework for AI adoption in digital forensics by Veksler et al. (2025). The research sought to “identify key factors influencing AI adoption in DF, highlight existing best practices, and propose actionable guidelines to ensure AI tools align with real world forensic requirements” (Veksler et al., 2025, p. 2). 

The authors describe their research methods as follows: “We conducted 28 surveys and 10 interviews with digital forensic experts” (p. 3). Their research examined practitionersʼ experiences, the feasibility of broader adoption, actions to support responsible implementation, and stake holdersʼ responsibilities (p. 2). 

The scope encompasses “both technical and practical dimensions, including prominent challenges, perception and feasibility, trust, functionality and deployment, required capabilities and resources, XAI, validation, evaluation, and datasets” (p. 4).

The resulting framework addresses five areas: “(1) Tool Validation; (2) Datasets; (3) Assessment and Presentation of Evidence; (4) Tool Selection; and (5) Tool Development” (p. 11).

The recommendations below organize the authorsʼ statements into examiner-facing guidelines. They should be understood within the studyʼs stated limitation: “the overall sample size remains relatively small, which limits the generalizability of the results” (p. 14).

Tool Selection and Appropriate Uses

• Select tools through critical evaluation rather than advertised functionality alone. The authors state that tool selection “should follow a more structured and critical evaluation process” and that “practitioners should strive to select tools with available detailed documentation” (p. 13).

• Select tools whose operation and results can be explained. Before selecting a tool, examiners should ask: “Do I understand the general idea of how this tool operates and why it produces a specific result?” and “Can I clearly explain it to a non-technical audience?” (p. 13).

• Consider open-source tools when they provide greater transparency, but establish their security. The authors state that practitioners may select “open-source options to achieve greater transparency, provided they can establish its security” (p. 13).

• Favor incremental, verifiable integration of AI into examinations. The authors recommend “a phased approach that starts by integrating AI into smaller, verifiable components of the forensic workflow.” Those components “should allow for manual or traditional dual-tool validation” (p. 13).

• Recognize triage automation as a practitioner-preferred application, rather than treating this preference as a prohibition on other uses. The study found that “Practitioners favored DFAI that supports triage automation, as this directly increases investigative efficiency” (p. 6).

• Confirm that the required operational environment can be reproduced. The authors state: “Practitioners must ensure they can replicate the required operational environment.” Otherwise, tool performance “may be inconsistent, unstable, or produce erroneous results” (p. 13).

• Evaluate third-party data sharing and protect sensitive information. The authors direct practitioners to “determine whether using the DFAI tool necessitates sharing data with third parties.” When sharing cannot be avoided, “data must be properly anonymized and protected” (p. 13).

Validation and Performance Assessment

• Conduct local, independent validation before use, even when a vendor has validated the tool. The authors state that “practitioners must conduct local and independent validation before use” and that validation must occur “in the same environment where the tool will be applied” (p. 13).

• Use comparative testing and repeated test cases. The authors recommend: “Crossvalidation with traditional and DFAI tools is recommended to ensure consistent performance.” Additionally, “Validation should be repeated multiple times for each test case to establish repeatability and robustness” (p. 13).

• Extend traditional validation to address AI-specific risks. The proposed AI Testing Suite contains four components: “Bias Estimation,” “Precision Testing,” “Elusion Testing,” and “Hallucination Testing” (pp. 11–12).

1.  Bias testing should include “testing the tool on multiple varied datasets to identify performance disparities” (p. 11).
2.  Precision testing determines whether a tool produces “overly inclusive or unjustified results” (p. 12).
3. Elusion testing evaluates robustness against “adversarial or anti-forensic inputs” (p. 12). 
4. Hallucination testing verifies that outputs are “accurate and input-based results” (p. 12).

• Match performance metrics to the algorithm and investigative purpose. The authors state that “metrics should be matched to the algorithm type.” They distinguish triage tools, which “may prioritize recall and scalability,” from evidence analysis tools, which “must place greater emphasis on precision” (p. 11).

• Document performance measures and their limitations. The framework calls for disclosure of “performance metrics and error rates,” including “their definitions, estimation methodologies, and the operational factors that may affect them” (p. 9).

Dataset Selection and Management

• Use diverse, rigorously assessed datasets. The authors recommend “open-source, publicly available, and rigorously verified datasets.” They further state that all data should be “selected, reviewed, and annotated by multiple independent parties” (p. 12).

• Keep training and test data separate. The authors specify: “Datasets should have two nonoverlapping categories of data: training and test.” Using the same data for both purposes “may result in overestimated accuracy, leading to overconfidence in the evidence analysis” (p. 12).

• Require dataset documentation, integrity verification, and maintenance. Documentation should identify “the acquisition methods, tools, and devices used, data characteristics, and overall dataset structure.” Datasets should be “regularly revised and updated” and, when made publicly available, “accompanied by hash values to verify data integrity” (p. 12).

• Assess older datasets before reuse. Before using an existing dataset, “it should be determined whether it is technologically relevant.” For image, video, or audio data, reuse may be appropriate when “label accuracy is verified through a quality-control check or reassigned” (p. 12).

Examiner Competence and Evidence Presentation

• Obtain specialized AI training. The authors state: “Forensic practitioners must receive training beyond standard forensic practices to include specialized instruction in AI” (pp. 12–13). Training should cover “foundational knowledge of AI algorithms, their limitations, and responsible usage,” with emphasis on “practical, handson experience” (p. 13).

• Establish reliability before introducing AI-generated evidence in court. Practitioners should explain the toolʼs intended function and methodology, followed by validation reports documenting “the testing scenarios, performance metrics, error rates, and results obtained from the AI Test Suite” (p. 13).

• Use explainable AI as a supplement, not as proof of correctness. The authors state that XAI “should not be mistaken for ground truth or used as a means of validating the correctness of the tool itself.” Rather, it should serve as “a supplementary mechanism” supporting interpretation and explanation (p. 13).

• Avoid presenting unsupported AI-generated findings as conclusive evidence. The authors caution that “AI-generated evidence should not be considered conclusive” unless independently verified or supported by rigorously demonstrated, indisputable tool accuracy. Instead, it should form “part of a broader body of evidence, ideally supported by other corroborating materials” (p. 13).

Issues and Challenges

• Insufficient AI-specific validation. The authors conclude that traditional validation methods “alone are insufficient for a thorough validation,” given AIʼs “inherent uncertainty and probabilistic nature” (p. 8).

• Algorithmic bias despite high accuracy. AI models may “inherit or amplify biases present in their training data,” producing “unreliable or discriminatory outcomes, even if the overall accuracy is high” (p. 11).

• Hallucinated or misleading outputs. The authors warn that AI algorithms, particularly generative or interpretive systems, “can produce misleading or false information results” (p. 12).

• Adversarial and anti-forensic manipulation. AI models are “vulnerable to subtle perturbations,” creating a need to evaluate their resistance to “adversarial or anti-forensic inputs” (p. 12).

• Push-button forensics and inadequate examiner competence. Participants cautioned that “without proper training and expertise, DFAI risks becoming push-button forensics.” They also identified “the possibility of defense challenging an examinerʼs competency” (p. 9).

• Difficulty explaining results and limitations of XAI. The authors report that “XAI outputs are o en technical, require interpretation, and do not address inherent biases or limitations within AI models” (p. 7).

• Misunderstood or incomplete performance metrics. The authors found that “the same definitions may be interpreted differently depending on the respondentsʼ backgrounds.” Furthermore, practitionersʼ preferred metrics “may not fully reflect the behavior of the DFAI algorithm” (p. 14).

• Dataset inaccuracies, inadequate diversity, and bias. The authors warn that “inaccuracies or biases in the dataset can critically undermine the reliability of DFAI validation.” A lack of diversity can cause “overfitting to specific artifacts or scenarios” (p. 12).

• Benchmark overfitting. Gold-standard datasets can encourage developers to fine-tune methods “to perform well on known benchmarks,” rather than establish performance on previously unseen data (p. 14).

• Incomplete dataset transparency and difficult validation. The study identifies “nondisclosed datasets or incomplete descriptions of datasets” as obstacles that complicate validation and undermine trust. One participant described validating every dataset element as an “overwhelming task” (p. 9).

• Sensitive data and third-party interference. Forensic data “o en contains sensitive or personal information, requiring strong privacy protections” (p. 14). The authors also emphasize “minimizing third-party and providersʼ access to the results produced by forensic tools” (p. 10).

• Inconsistent performance and limited repeatability. Performance can vary “across hardware, so ware, and input conditions,” causing “inconsistent results” and undermining repeatability. Even tools performing the same function in the same environment can produce different outputs (p. 14).

• Operational constraints. The authors identify time-sensitive investigations, “limited access to advanced computing resources,” and the need to work “offline or with limited connectivity” as constraints affecting practical deployment (p. 14).

• Lack of formal policies and inconsistent validation practices. The authors identify an “overall lack of formalized, DFAI-specific policies” (p. 11). Case-by-case validation also risks “inconsistencies, missed procedures, and even direct contradictions across similar DFAI applications” (p. 14).

• Courtroom skepticism and limited judicial precedent. The authors state: “The lack of judicial precedents means that AI-generated evidence is o en treated with skepticism in court.” Judicial hesitation can leave acceptance dependent on rulings “that have not yet occurred” (p. 14).

• Barriers to coordinated implementation. The authors identify “persistent communication gaps between academia and practitioners,” limited organizational incentives, funding restrictions, and “significant time and resource pressures” as impediments to implementing the framework (p. 14).

AI Use Statement

Perplexity AI was used in the development of this information.

References

Veksler, M., Akkaya, K., & Uluagac, S. (2025). Practitioner-driven framework for AI adoption in digital forensics. Forensic Science International: Digital Investigation, 55, Article 302030. https://doi. org/10.1016/j.fsidi.2025.302030

Perplexity AI. (n.d.). Perplexity [Large language model].https://www.perplexity.ai/

 

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Kardasz F. (2026, October). AI & Digital Forensics - Review of Veksler et al. (2025) Practitioner-driven framework for AI adoption...      https://kardasz.blogspot.com/2026/10/ai-digital-forensics-review-of-veksler.html