Personal computers have been targeted with malicious software for almost as long as they’ve been around. Unsurprisingly, the threats have undergone a continuous evolution as criminals strive to achieve their objectives, and endpoint protection has evolved with them.
Today the threats are not just from malicious software, although that threat is still very real. Threat actors use increasingly sophisticated strategies to gain access to victims’ devices and evade detection using legitimate software for their attacks. Endpoint protection has morphed from relatively simple anti-virus to advanced behavioral analysis and more recently Endpoint Protection and Response (EDR).
State-of-the-art analysis has led to the adoption of cloud-based services, begging the question: is there still a role for endpoint-based detection? The short answer is that both local and cloud-based detection have their benefits, and optimal solutions incorporate both.
Let’s look at some of the key features of endpoint protection (including EDR), and the relative strengths of local vs cloud detection:
Signature-based malware detection
Traditional detection uses file signatures and remarkably efficient algorithms to protect devices against known malware variants. According to av-atlas.org, in 2025 there are approximately 960 million malware variants infecting untold numbers of computers each and every day. VirusTotal, an online service that analyzes suspicious files using scores of antivirus engines including Emsisoft, has a dataset of more than 2 billion files that have been analyzed.
Given these numbers, it’s clear that local signature-based detection with regular updates plays a critical role in a strong cybersecurity strategy — offering the best performance for identifying known threats quickly and efficiently. This detection should be run locally as the latency inherent with a cloud solution may give the malware an upper hand while negatively impacting the user experience.
Behavioral analysis
In terms of detection and remediation, real-life numbers prove the ongoing value of traditional antivirus as a defensive strategy. However, cyber criminals have responded to this success, and new defensive layers have been developed as a result: over the last decade pretty much every endpoint protection product on the market has implemented some sort of behavioral analysis. This functionality looks for specific patterns of activity shown to be indicative of malicious intent.
While complementing signature-based detection, the challenge in identifying malicious intent is that legitimate software sometimes triggers false positive detections. Updates to existing applications are notorious for not having digital signatures to identify them as being legitimate, and making multiple read/write operations – very similar to the behavior of malware. While false positives annoy end users, tuning detection filters to eliminate them may allow some malicious software to go undetected, so striking the right balance is key.
Reducing false positives to an acceptable level is particularly well suited to Machine Learning (ML), a technology that has gained a lot of traction in recent years. Trained on massive datasets, endpoint-based ML helps differentiate malicious from benign behavior and reduces false positives by an order of magnitude. And while training a new model with a dataset is very computationally intensive, using the model to test new data isn’t, and is well suited to residing locally.
EDR
Endpoint Detection and Response (EDR) has developed over the last 10 years or so as part of an evolving multi-layered protection strategy in response to the increasing complexity of threats. Although the acronym was quickly adopted, adapted and sullied by marketing, it’s proven to be a core component of a robust security architecture. It continuously collects endpoint data for analysis and can be configured to generate alerts or automatically take appropriate action like isolating a device or killing a malicious process. The data collected by EDR systems is useful for detecting early signs of an attack — such as unauthorized access — and for conducting thorough forensic analysis after an incident has occurred.
When data from multiple endpoints is viewed as a whole, troublesome trends and activity that might otherwise be missed can rise to the surface and become apparent. The data collected across all endpoints, the storage of that data for an extended period of time, and the ability to view and drill down into all of the data as a whole make EDR ideally suited for the cloud.
Management & Visibility
For home or small office use, installing endpoint protection on a handful of devices isn’t a major undertaking, and in some cases requiring central management can be an unnecessary burden. However, once you grow past a handful of devices, the ability to deploy, configure and monitor endpoints from a central application is a game-changer. Not only can identical configuration templates be created and deployed, all devices can be monitored from a single view. Activity that appears across multiple endpoints is much easier to identify, investigate, and remediate. EDR data captured and stored in the cloud is much less susceptible to malicious compromise or corruption, supporting post-incident forensic investigation. In a nutshell, cental management of multiple devices is best suited to a cloud-based architecture.
Summary
While cloud-based solutions have become the norm in modern IT environments, local endpoint detection still plays a vital role in maintaining a robust security posture.
Each detection method offers distinct strengths:
Emsisoft Endpoint Protection: Award-Winning Security Made Simple
Experience effortless next-gen technology. Start Free Trial- Local protection provides fast, efficient defense against known threats with minimal latency.
- Cloud-based solutions offer scalability, centralized visibility, and advanced analytics ideal for detecting complex, coordinated attacks.
A hybrid approach — combining the speed of local detection with the intelligence and scalability of the cloud — delivers the most effective, multi-layered endpoint protection. This strategy ensures organizations are equipped to prevent, detect, and respond to threats in real time, regardless of how or where they emerge.