Glasswall Halo can be deployed on-premises, in private cloud environments, or within hybrid architectures, depending on organisational requirements and security policies.

Glasswall Halo analyses files at a structural level and reconstructs them according to strict file format standards. Instead of scanning for known malware signatures, it removes any content that does not conform to the file specification, preventing zero-day and unknown threats.

Glasswall Foresight is engineered to maintain extremely low false positive rates while delivering high-confidence threat scoring. In production environments, it has demonstrated a false positive rate as low as 0.015% for PDF files, reducing alert fatigue and accelerating security investigations.

Foresight is designed to detect malicious intent in file-based threats, including zero-day malware, polymorphic variants, and previously unseen attacks embedded within common file formats. It focuses on structural indicators of compromise rather than relying on known threat signatures.

Glasswall Foresight enhances existing security controls by adding predictive file intelligence. It can complement antivirus, sandboxing, and endpoint protection platforms by providing earlier visibility into unknown file threats. In high-assurance environments, it can also serve as a primary file inspection and risk scoring mechanism.

No. Glasswall Foresight is signatureless. Traditional antivirus tools depend on known malware signatures, which can miss new or modified threats. Foresight evaluates file structure and telemetry using machine learning models, allowing it to identify unknown and zero-day malware without prior signature knowledge.

Sandboxing detonates files in a controlled environment to observe behaviour, which can introduce delays, infrastructure overhead, and evasion risks. Glasswall Foresight does not detonate files. Instead, it analyses structural file characteristics and generates a probabilistic threat score instantly, reducing latency and working in offline or air-gapped environments.

Foresight uses proprietary machine learning models trained on deep structural telemetry extracted during CDR inspection. Instead of looking for known malware signatures, it analyses how a file is constructed and identifies structural anomalies that indicate malicious intent. This allows it to detect previously unseen and zero-day threats without waiting for threat intelligence updates.