Deep Instinct wins in efficacy, scalability, speed, and total cost of ownership
Enterprise data storage has become a lucrative attack vector for cybercriminals. The explosive growth of data, especially for large financial institutions that have deep stores of sensitive material, has made them attractive targets. Banks face numerous challenges in securing their storage environments, including the following:
- An ever-evolving threat landscape: Cybercriminals are constantly developing new and more sophisticated methods to infiltrate storage systems, particularly with the emergence of generative AI and LLM-aided malware tools.
- Performance and latency issues: Traditional security solutions often struggle to efficiently scan large volumes of data without impacting system performance and causing unacceptable latency.
- High infrastructure costs: Infrastructure costs can be expensive because of the sheer number of servers required to scan storage data.
- Complexity of deployment and management: Implementing and managing storage security solutions can be cumbersome, especially when using products not purpose-built for the task.
Deep Instinct, a leading provider of deep learning-based cybersecurity solutions, recently had the opportunity to prove the value of its data storage protection capabilities to a top-five global bank facing these challenges. The bank needed a solution to secure its network attached storage (NAS) against the growing threat of ransomware and other malware. Deep Instinct’s prevention-first approach was especially intriguing.
Bank puts Deep Instinct and Trellix head-to-head
They evaluated Deep Instinct Prevention for Storage (DPS) head-to-head against the incumbent, Trellix ENS (Endpoint Security). Trellix had been struggling to complete scans within designated maintenance windows, and the bank wanted to reduce its total infrastructure spend from running over 3,200 scanning servers while strengthening overall storage security.
The bank compared DPS and Trellix across several key criteria: efficacy, scalability, scan speed, ease of deployment & operation, and TCO. The evaluation used the latest versions of both products under identical conditions — the same hardware, network speed, and data set.
Here are the results of the comparison.
Efficacy: In the evaluation, Deep Instinct prevented >99% of known and unknown threats across multiple file types and sizes. Trellix, on the other hand, had much lower efficacy. Additionally, Deep Instinct had a significantly lower false positive rate, enabling the bank’s security team to focus on real threats instead of chasing false positives.
Scalability/Speed: DPS outperformed Trellix in terms of performance and scan speed, scanning files ~10x faster, on average. This is a significant advantage in network attached storage environments with inherent latency, as our local deep learning models don’t require cloud calls, making it extremely efficient. DPS’s remarkable speed enables full-volume scans that weren’t feasible with Trellix.
Ease of Deployment: The bank gave high marks to DPS for ease of deployment and operation because it is purpose-built for storage security. This includes streamlined implementation and management, intuitive policy configuration, scan stats, and visibility. In contrast, Trellix ENS is essentially an endpoint security product repurposed for storage, making deployment and management cumbersome.
Total Cost of Ownership: Based on the performance data, the bank determined that it could secure its storage environment with ~500 DPS servers versus the ~3,200 required by Trellix, an astonishing reduction of ~2,700 servers while offering superior, prevention-first cybersecurity. This reduction has a significant ROI upside — worth millions in infrastructure and licensing costs.
The switch to Deep Instinct
Upon completion of the head-to-head evaluation, the choice was clear: the bank decided to replace its Trellix solution and standardize on Deep Instinct Prevention for Storage across its NAS infrastructure.
Here’s why. DPS is tough to beat for organizations securing their storage systems and data against known and unknown threats without compromising performance or driving up costs. It is purpose-built for storage and powered by a static analysis engine based on deep learning, delivering unmatched efficacy, speed, efficiency, and ease of use.
The hype around Deep Instinct’s cyberstorage offering is real — and is now validated in one of the world's largest and most demanding enterprise environments.
Request a demo with us to see it for yourself.