---
title: "GFI Software: one-third of IT professionals see great potential in using AI for security audits — ZEBR.ai"
description: "Beyond incident detection and response, artificial intelligence is increasingly being used for analysis and security audits of corporate IT networks"
url: "https://zebr.ai/en/blog/gfi-software-one-third-of-it-professionals-see-great-potential-in-using-ai-for-security-audits"
markdown_url: "https://zebr.ai/en/blog/gfi-software-one-third-of-it-professionals-see-great-potential-in-using-ai-for-security-audits.md"
language: "en"
published: "2026-06-16T08:52:34+00:00"
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# GFI Software: one-third of IT professionals see great potential in using AI for security audits

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Beyond incident detection and response, artificial intelligence is increasingly being used for analysis and security audits of corporate IT networks

16/06/2026 · 3 min read · ZEBR.ai editorial team

*Beyond incident detection and response, artificial intelligence is increasingly being used for analysis and security audits of corporate IT networks*

**PRAGUE, June 16, 2026** – [**GFI Software**](http://www.gfi.com/), a global leader in AI-based security and communications solutions for small and medium-sized businesses (SMBs), announced that according to its survey, 36% of the partners surveyed see **growing potential for AI** not only in security itself, but also **in preparing materials for security audits**. A faster preparation process primarily leads to a higher level of security and significant time savings.

Modern tools include AI features that can now analyze, identify risks, report, and automatically create more effective rules in much less time than manual methods. With the support of artificial intelligence, tools for email security and archiving, vulnerability scanning, and centralized IT solution management can reduce activities necessary for conducting audits or creating more effective rules from days to just hours. This enables IT administrators to perform more frequent checks and respond more quickly to potential risks.

Key findings include:

- 95% of the IT partners surveyed see the greatest value of AI in detecting and evaluating security threats, 51% in actively responding to incidents, and 36% also in reporting and preparing for security audits.
- The primary tools used to prepare for security audits are vulnerability scanners (71%), network traffic optimization tools (42%), and email archiving solutions (39%). A centralized platform for managing network tools is used by 47% of respondents.
- Respondents see the greatest benefit of AI in connection with security network monitoring and auditing in the resulting higher level of security (62%) and time savings in preparing documentation (30%).

“We recommend conducting regular security audits for organizations of all sizes – their aim is to identify weak points in corporate networks before they turn into security threats,” said **Jozef Kačala, Vice President of Global Sales Engineering at GFI Software**. “Artificial intelligence integrated into GFI Software products significantly accelerates and simplifies the entire process, making it possible to minimize security risks at their source.”

GFI Software provides several suitable tools for this purpose. For example, **GFI Archiver AI** generates rules for checks based on a prompt, performs the check on the mail server, and can present its findings within a few hours. And while manually creating email protection rules takes up to 40 hours, the artificial intelligence in **GFI MailEssentials AI** generates a complete set of rules in 5 minutes.

**GFI LanGuard AI** provides an overview of the current state of network security and the history of all relevant network changes over time, and enables users to navigate the information in greater detail – from security sensors covering the entire network to the results of individual security checks. Everything is topped off by the central **GFI AppManager AI** console, which can provide a specific answer about users, licenses, security settings, and system status within 30 seconds based on a standard query.

Article author: ZEBR.ai editorial team

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