---
title: "N-able: Enhancing Software Update Automation in the N-sight RMM Solution — ZEBR.ai"
description: "Reducing manual patch management processes increases the level of protection"
url: "https://zebr.ai/en/blog/n-able-enhancing-software-update-automation-in-the-n-sight-rmm-solution"
markdown_url: "https://zebr.ai/en/blog/n-able-enhancing-software-update-automation-in-the-n-sight-rmm-solution.md"
language: "en"
published: "2022-12-06T16:05:43+00:00"
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[Home](https://zebr.ai/en/) [Blog](https://zebr.ai/en/blog) N-able: Enhancing Software Update Automation in the N-sight RMM Solution

# N-able: Enhancing Software Update Automation in the N-sight RMM Solution

[automation](https://zebr.ai/en/blog?tag=automation) [N-able](https://zebr.ai/en/blog?tag=n-able) [patch management](https://zebr.ai/en/blog?tag=patch-management) [press release](https://zebr.ai/en/blog?tag=press-releases)

Reducing manual patch management processes increases the level of protection

06/12/2022 · 3 min read · ZEBR.ai editorial team

*Reducing manual patch management processes increases the level of protection*

**PRAGUE, December 6, 2022** – **ZEBRA SYSTEMS**, the distributor of **N-able** solutions in the Czech and Slovak markets, announced that N-able is further **enhancing the level of software update automation** within its **N-sight RMM** solution. The solution will enable managed service providers (MSPs) to accelerate and streamline patch management processes for their customers, both for Microsoft systems and third-party applications.

The enhanced patching engine in N-sight RMM responds to Microsoft's new approach to releasing updates, which primarily uses patch classification without specifying their severity. N-sight RMM now enables patches to be applied automatically across all Microsoft classification categories. Another enhancement supports automatic update approval for the 100 most widely used third-party applications detected by the N-sight platform, including Adobe Acrobat and Reader. Finally, the new engine makes it faster and easier to search for patches based on classification categories.

The number of vulnerabilities increases every year: while in 2016, for example, “only” just under 7,000 vulnerabilities were discovered, in 2021 the National Vulnerability Database (NVD) recorded more than 20,000 vulnerabilities, the highest number ever recorded. Their severity is also increasing, as demonstrated by the Log4Shell vulnerability, which affected not just a single application but an entire library used by a large number of applications. An automated patch management tool can effectively protect organizations against highly dangerous attacks.

“According to some surveys, the average time from a patch being released to its deployment is several days, and the most common reason for such a delay is the manual method of installation,” said **Ondřej Šabata, Sales Engineer for N-able solutions at ZEBRA SYSTEMS**. “Enhancing automated patching capabilities in the N-able N-sight RMM solution gives our partners the ability to minimize this time and protect their customers’ infrastructures more effectively. At the same time, thanks to alerts for any installation issue and device status monitoring, they can be sure that all critical patches are actually deployed on all devices, because a unified environment is just as important as installation speed. For all other updates, they can use manual approval while still maintaining a unified environment and a consistent level of security across all devices.”

The enhanced patch management will be available within the **[N-sight RMM](https://www.zebra.cz/produkty-n-able/n-able-n-sight-rmm/)** solution in January 2023.

**About N-able**

For more than 20 years, N-able has been a provider of tools for managed service providers (MSPs), who use them to manage, monitor, and secure their customers’ IT infrastructures. N-able solutions are used by more than 22,000 MSPs to manage over 500,000 corporate networks and 7 million endpoints. For more information, visit [http://www.n-able.com](http://www.n-able.com/).

Article author: ZEBR.ai editorial team

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