Rethinking digital forensic evidence

If you work in digital forensics or incident response, you already understand one of the basic premises: if you can’t prove the integrity of the evidence, nothing else matters.

As engineers, we spend a lot of time fine-tuning systems for performance, scale, and reliability. But there’s one area where shortcuts just aren’t acceptable: data integrity. Whether you’re supporting digital forensics, cybersecurity investigations, compliance workflows, or large-scale data preservation, the ability to definitively prove that data hasn’t changed is foundational.

Hashing has been the backbone of that proof for decades. It’s reliable, defensible, and well understood. Traditional hashing does exactly what it’s supposed to do: it processes data sequentially, producing a single final hash value that reflects the entire data set and proves there was no alteration of the evidence. That sequential dependency is essential for security, but it also creates an unavoidable bottleneck ...