Glasswall has introduced Glasswall Foresight, a new AI-powered security solution designed to predict and identify file-based cyber threats before they can harm enterprise systems. The platform combines advanced machine learning with deep analytics generated from Content Disarm and Reconstruction (CDR) technology, enabling organizations to detect emerging malware and previously unknown threats, including sophisticated zero-day attacks. As cybercriminals increasingly use files such as PDFs, documents, and spreadsheets to deliver malicious payloads, organizations require more proactive and intelligent protection strategies. Consequently, Glasswall developed Foresight to give security teams greater visibility into file-based risks and improve their ability to respond to evolving attack techniques.
Unlike traditional sandboxing tools or AI models that rely heavily on internet-based threat intelligence feeds, Glasswall Foresight analyzes the internal structure of files directly through deterministic analysis generated during the CDR process. Therefore, the system produces insights based on actual file behavior and structure rather than relying on external execution environments. This approach allows organizations to detect suspicious patterns even when dealing with unknown threats that may bypass traditional detection technologies.
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The solution is now integrated into Glasswall Meteor, the company’s automated file cleansing platform designed for both on-premises environments and cloud storage systems. Through this integration, Foresight adds a new layer of intelligence to existing file security workflows. While Glasswall’s CDR technology already ensures files are sanitized and safe for use, Foresight analyzes structural indicators to determine whether a file might have originally contained malicious content. As a result, security teams gain valuable contextual insights into the potential risk of files entering their environment.
In practical terms, this capability enables organizations to prioritize high-risk files and enhance their incident investigations with deeper threat context. Additionally, security teams can refine their zero-trust policies and feed structured risk intelligence directly into SIEM platforms and SOC workflows. By doing so, organizations can significantly improve operational visibility and strengthen their threat detection strategies across the enterprise.
Glasswall Foresight achieves this intelligence by analyzing hundreds of thousands of indicators from millions of file samples. Using this large dataset, the system generates probabilistic threat classifications that indicate the likelihood of a file being malicious. Importantly, this capability applies even to files associated with unknown or previously unseen malware variants. Consequently, organizations gain a better understanding of their file-based threat landscape and can respond more effectively to emerging risks.
Another key advantage of the solution is its compatibility with secure environments where internet connectivity may be restricted. Because Foresight relies on trusted CDR telemetry rather than external AI models, it functions effectively in offline or air-gapped environments. This capability makes it particularly valuable for government agencies, critical infrastructure organizations, and highly regulated industries where external connections are limited for security reasons.
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The system also demonstrates strong operational efficiency. Production-ready machine learning models deliver extremely low false-positive rates just 0.015 percent for PDF files and similarly low levels for widely used enterprise formats such as DOCX and XLSX. Consequently, security analysts experience fewer false alerts, reducing SOC workload and minimizing investigation fatigue.
By integrating seamlessly with Glasswall Meteor, Foresight strengthens the company’s zero-trust security approach while expanding its capabilities beyond file remediation to include predictive threat intelligence. Organizations can maintain preventative, policy-driven security controls while simultaneously gaining deeper insights into file activity across their environments.
“File-based threats remain among the most effective and persistent attack vectors facing organizations in the public and private sectors. Yet, traditional threat intelligence and detection tools struggle to keep pace with unknown and zero-day attacks,” said Paul Farrington, Product and Marketing Director at Glasswall.
“With Glasswall Foresight, we apply machine learning to the deep structural insights generated by our Content Disarm and Reconstruction technology to give security teams a clearer understanding of malicious file activity entering their environments, including offline or air-gapped environments where traditional approaches fall short. The combination of knowing we can secure a file and knowing whether it has ever been compromised provides significant value. Many organizations invest substantial sums in sandboxing infrastructure, which is slow, prone to disruption, and expensive. Glasswall’s Zero Trust CDR, combined with Foresight, offers a clear path to reducing both these expenditures and the associated operational overhead.”
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