Artificial Intelligence in Digital Forensics: Precision, Accuracy, and Validation

Artificial Intelligence (AI) has become an umbrella term for complex software programs powered by intricate mathematical rules and algorithms. Unfortunately, the term has been marketed so heavily as a buzzword that it has blurred the lines of what AI technologies truly entail. AI technologies are the tools and techniques—such as machine learning, natural language processing (NLP), computer vision, and deep learning—that enable AI systems to function. Think of AI as the goal, and these technologies as the methods to achieve it.

For instance, deep learning can be used to play chess by learning to evaluate positions and make decisions based on the opponent’s moves. Similarly, machine learning is often used with image datasets for object detection, such as identifying license plates or performing facial recognition. NLP powers smart assistants like Siri, Alexa, or Cortana by breaking down language into linguistic features. Even self-driving cars rely on computer vision for lane tracking, pedestrian detection, and recognizing traffic signs.

But what does this mean for digital forensics? Let’s focus on the measurement aspects of AI technologies and their implications for precision, accuracy, and validation.

Validating AI Technologies in Digital Forensics

When AI technologies are integrated into digital forensics, validation becomes crucial. Testability, traceability, peer review, error rates, general acceptance, and sound methodologies—these principles underpin the reliability of any forensic process.

Testability and Traceability

Testability is paramount in meeting the Daubert Standards. Can the AI technology or algorithm in a digital forensic tool be explained? In other words, can you trace the results back to the source and validate them using traditional digital forensic methods? If the answer is yes, and the results remain consistent every time, then the tool meets the criteria.

Traceability stems from the NIST framework, emphasizing the ability to follow results back to their origin. This principle ensures that forensic practitioners can explain how conclusions were reached, providing a foundation of credibility.

Peer Review and Publication

Has the technique, methodology, or tool been validated by someone other than the expert witness? Peer review platforms such as the Computer Forensics Tool Testing (CFTT) platform and the DFRWS led DFIR Review support this principle by offering avenues to validate findings, research, and development in open forums. These platforms provide free forensic test images with well-documented artifacts, enabling practitioners to independently verify results.

Error Rates

No tool is perfect, and every AI model should have a documented error rate. For example, in ATRIO MK II, our forensic toolset, You Only Look Once (YOLO) models, are used for object detection. To measure accuracy, we use Mean Average Precision (mAP), which encapsulates precision and recall across multiple classes. mAP evaluates how well the model detects objects at an Intersection over Union (IoU) threshold of 0.50, ensuring that results are measurable and transparent.

To simplify, think of mAP as training a dog to recognize objects like lions, tigers, and bears:

• Precision: How accurate is the dog’s identification? Does it correctly identify tigers as tigers, or does it mistake them for bears?

• Recall: How many objects does the dog actually find? If there are five lions, does it detect all five or just one?

Organizations using AI technologies should provide this level of transparency. Ask vendors: How accurate is the model based on precision and recall?

General Acceptance

Is the technique generally accepted within the relevant scientific community? AI technologies are increasingly integrated into digital forensics tools, with features such as translation, object detection, and transcription. At ArcPoint, we document our models and provide transparency in their development and validation.

Sound Principles and Methodologies

Underlying principles and methodologies must be sound and reliable. Validation ensures that methodologies produce consistent and repeatable results. Vendors share responsibility for transparency. Ask how they validated their tools, and ensure your findings are cross-validated with traditional methods.

Responsible AI in Digital Forensics

AI technologies are becoming commonplace in digital forensics, but leveraging them requires practicality, responsibility, and validation. By adhering to strong principles and maintaining accountability, we can improve the quality of casework and foster greater acceptance of AI-driven practices.

At the end of the day, it’s all about one word: validate.

Interested in how our AI technology integrated into ATRIO MK II makes your workflows more efficient? Let’s chat! 👇 Contact us today!