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 during verification. 

As DFIR (digital forensics and incident response) data volumes have continued to grow and digital forensic investigators need to image and verify terabytes at a time, we keep running into the same issue: sequential hashing is the slowest part of the job.

The practical problem forensic investigators face

Forensic hardware has become incredibly fast. Modern digital forensic imagers can acquire data at very high throughput rates, from increasingly large storage devices.

But once digital forensic evidence is acquired from a device, teams still have to wait, sometimes for a long time, for verification to be completed.  Digital forensic investigators often image large drives quickly, then wait hours for sequential hash verification to complete, leaving them unable to proceed with their analysis or hand off the evidence until the verification is complete.  In other words, the hardware isn’t the bottleneck - the hash verification model is.  The OpenText team recognized an opportunity to improve performance through parallel hash verification.

A more scalable way to verify trust

Hashing works by processing data in a strict sequence. Each block of data updates an internal hash state, and the final hash value depends on every block that came before it. This sequential dependency is intentional - it’s what makes cryptographic hashes tamper-evident.

However, this same property creates a performance limitation:

• Verification must occur end-to-end, even if only a small portion of data needs validation
• Multiple CPU cores cannot be effectively utilized
• Large forensic images or archives take hours to verify, even on powerful systems

In situations where terabytes of data are routinely copied, stored, and re-verified, this becomes a serious operational constraint.

The approach described in OpenText’s recently issued United States Patent (Systems and Methods for Enabling Parallelized Verification of Forensic Copies Using Non-Parallelizable Algorithms) introduces a simple but powerful idea: store intermediate hash states during initial hashing and use them to facilitate parallel hash verification later.  The data stream is broken into blocks, and intermediate hash states are recorded at defined offsets during initial hashing. These intermediate states are then stored alongside the data copy.  Later, during verification, each block can be independently re-hashed, in parallel, and validated against its corresponding stored hash state.

In other words:

• Hashing remains cryptographically sound
• Verification is no longer forced to be sequential
• Multiple processors can validate different sections simultaneously

This allows verification workloads to scale horizontally across CPU cores or compute nodes, rather than being limited to a single execution path.

This isn’t about inventing a new hash algorithm. It’s about engineering a better system around proven cryptography. From a software engineering standpoint, the benefits are immediate and tangible. It’s about engineering systems that perform at the scale today’s workloads demand.  It’s a reminder that some of the most impactful advances don’t come from rewriting the rules, but from applying them more intelligently.  That’s how integrity keeps pace with scale.

Main benefits of parallel hash verification

1. Massive Performance Gains: The most immediate benefit of parallel hash verification is speed.  By verifying data blocks independently, verification time can be significantly reduced, especially on modern multi-core systems.  Instead of waiting for a single thread to grind through an entire image, multiple verification processes operate concurrently.  The result is faster workflows without weakening integrity guarantees.
2. True Scalability with Modern Hardware: Traditional hashing algorithms were designed in an era of single-core processors. Parallel hash verification aligns hashing workflows with how modern systems compute.  This makes the approach future-proof. As processors continue to add cores rather than clock speed, parallel hash verification scales naturally without requiring changes to the underlying cryptographic algorithms.
3. Utilizes standard, proven hash algorithms: A key strength of this approach is that it does not rely on any novel hash algorithms that might require extra forensic scrutiny.  In regulated or forensic environments, this distinction is critical. This method improves performance without compromising trust.
4. Early Detection of Data Corruption: Parallel hash verification enables faster detection of corruption or data that’s been tampered with, often before a full verification is completed.  This is especially valuable when working with unstable storage media, remote transfers, or aging archives, where the risk of failure is higher.
5. Better Cyber Resilience: By reducing verification time and enabling incremental validation, parallel hash verification improves overall system resilience.  In environments where verification must be performed before data can be used, such as incident response, legal review, or compliance audits, this results in faster decision-making.

Data volumes are growing faster than verification methods were designed to handle. Meanwhile, regulatory scrutiny, litigation risk, and cyber threats demand stronger, not weaker, integrity guarantees.  Parallel hash verification bridges this gap. The OpenText patent represents more than a performance optimization - it reflects a shift in how integrity verification can scale responsibly in the age of big data and multi-core computing

That’s the kind of innovation that matters in enterprise environments: practical, incremental, and grounded in real workflow challenges.  At OpenText, this is the lens we apply to engineering challenges: preserving trust, respecting fundamentals, and designing for the realities our customers face every day.

Who uses parallel hash verification and why it matters

This capability wasn’t invented as an academic exercise. It directly addresses how DFIR teams work in real investigations.  When verification finishes sooner, investigators can benefit from faster turnaround between acquisition and analysis.  That’s significant when:

• A ransomware investigation is time-sensitive
• Legal or compliance deadlines are tight
• Evidence needs to be handed off quickly

OpenText Forensic Equipment is built for speed and reliability. Parallel hash verification was designed specifically to complement our high-performance forensic hardware. When OpenText Forensic Equipment acquires data, it hashes the data sequentially during acquisition, captures intermediate hash states at defined offsets, and stores those intermediate states securely with the forensic copy. Nothing about the integrity model changes. The evidence is still hashed using proven hash algorithms, and the final hash value is generated exactly as courts expect. The difference shows up later - during verification.

DFIR teams don’t just need fast tools.  They need tools they can trust under scrutiny.  By integrating parallel hash verification into OpenText Forensic Equipment, such as the OpenText Forensic TX2 Imager, we give customers:

• Faster verification at scale
• Earlier detection of corruption or tampering
• More predictable forensic workflows
• Confidence that integrity hasn’t been traded for speed

It’s a core capability that quietly improves every investigation without changing how teams think about evidence.

Faster verification. Proven integrity. No compromises.

Parallel hash verification is a good example of how we approach engineering at OpenText.  We look to remove friction where it no longer serves a purpose, especially when it slows down the people doing the hardest work.  By inventing parallel hash verification and integrating it into our forensic hardware, we’ve made evidence verification faster, more scalable, and better aligned with modern DFIR realities - without ever compromising trust.

That’s the standard our customers expect. And it’s the one we engineer toward.

Talk to an expert about how OpenText Forensic Equipment can improve the speed, scalability, and reliability of your evidence workflows.

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