Exploring AI in Digital Forensics: Why Validation Matters
By: Amy Moles
Welcome to the world of AI in digital forensics, a topic I enjoyed discussing at the eCrime Symposium. Before diving into the technical aspects, let me share a bit about myself and ArcPoint Forensics.
From Ybor City to Forensics Innovation
ArcPoint Forensics calls the historic streets of Ybor City in Tampa, Florida, home. Founded in 2020, our company was born from years of experience in digital forensics, incident response, and forward-deployed support for government agencies and the Department of Defense.
We saw a persistent gap in the industry for field-ready triage tools, leading us to develop ATRIO, a suite designed to empower investigators with fast, actionable insights. But innovation requires responsibility, and this is where understanding and validating AI becomes essential.
The Importance of Understanding AI Technologies
Artificial Intelligence (AI) is more than just a buzzword; it encompasses technologies like machine learning, natural language processing (NLP), and computer vision, all built to enhance specific applications. For example:
Deep Learning: Playing chess by learning and predicting outcomes.
Machine Learning: Training algorithms to detect patterns like license plates or fraud.
NLP: Smart assistants like Siri that break down language.
Computer Vision: Powering self-driving cars with object detection.
Each application serves a specific purpose, and it is critical for forensic examiners to understand these tools and their intended functions.
Why NIST Standards Matter
The National Institute of Standards and Technology (NIST) has long been a cornerstone for precision and reliability. Their frameworks, especially those focused on digital forensics, provide the guidelines for ensuring reproducibility, accuracy, and integrity. These include:
Evidence Acquisition: Ensuring digital evidence is preserved in its original state.
Tool Validation: Using programs like the Computer Forensics Tool Testing (CFTT) to verify software capabilities.
As AI integrates into forensic tools, these standards guide us in maintaining trust and reliability.
The Daubert Standard: A Roadmap for Admissibility
The Daubert Standard requires expert testimony to be testable, peer-reviewed, and generally accepted within the scientific community. For AI-integrated tools, this means:
Testability: Can we trace an AI tool's findings back to the source and validate them with traditional methods?
Peer Review: Resources like DFIR Review help ensure methodologies are vetted.
Error Rate Transparency: Vendors must document error rates and provide clarity around performance metrics like Mean Average Precision (mAP).
General Acceptance: AI tools must be transparent and designed for broad community trust.
A Collaborative Path Forward
AI in digital forensics requires a partnership between vendors and users. Vendors like ArcPoint must ensure transparency in their tools, provide robust documentation, and offer investigators training. Meanwhile, users must validate findings and critically assess AI outputs, both in the lab and courtroom.
Together, we can foster trust not only in AI technologies but in the tools themselves, paving the way for responsible innovation in digital forensics.
Want to Learn More?
Dive deeper into this topic and watch the full presentation from the eCrime Symposium.
By working together and embracing validation, we can uphold the standards of digital forensics in an era of rapid technological advancement.
Want to learn more about ATRIO’s Triage Capabilities? Request a demo!
