# A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

arXiv: https://arxiv.org/abs/2309.07064 · HTML: https://mlsanity.com/paper/2309.07064/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 105 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation](https://mlsanity.com/paper/2012.01987/) (2020)
- [Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff](https://mlsanity.com/paper/1908.00192/) (2019)
- [Malicious Behavior Detection using Windows Audit Logs](https://mlsanity.com/paper/1506.04200/) (2015)
- [A complete formalized knowledge representation model for advanced digital forensics timeline analysis](https://mlsanity.com/paper/1903.01396/) (2019)
- [Forensic Investigation of Social Media and Instant Messaging Services in Firefox OS: Facebook, Twitter, Google+, Telegram, OpenWapp and Line as Case Studies](https://mlsanity.com/paper/1706.08062/) (2017)
- [Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments](https://mlsanity.com/paper/2007.11246/) (2020)
- [Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events](https://mlsanity.com/paper/2012.01972/) (2020)

## Built on by (most cited first, 8 of 8)

- [GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions](https://mlsanity.com/paper/2312.16220/) (2023)
- [Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response](https://mlsanity.com/paper/2408.01999/) (2024)
- [Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era?](https://mlsanity.com/paper/2407.17870/) (2024)
- [A Novel Reinforcement Learning Model for Post-Incident Malware Investigations](https://mlsanity.com/paper/2410.15028/) (2024)
- [Exploring the Robustness of AI-Driven Tools in Digital Forensics: A Preliminary Study](https://mlsanity.com/paper/2412.01363/) (2024)
- [Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI](https://mlsanity.com/paper/2512.06922/) (2025)
- [Towards Open Standards for Systemic Complexity in Digital Forensics](https://mlsanity.com/paper/2512.12970/) (2025)
- [AI Forensics Across White-, Grey-, and Black-Box Access: A Process Model and Research Agenda for Post-Incident Investigation of AI Systems](https://mlsanity.com/paper/2608.03520/) (2026)

## Abstract (arXiv)

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.