SigaPlatform

Uniquely monitors raw electrical signals directly from the physical process layer (sensors and actuators), operating completely separate from the IT/OT network, making it impervious to network-based attacks.

Detects cyberattacks and operational faults by observing real-time deviations in the physical behavior of industrial processes, which traditional network-based tools might miss.

Utilizes advanced Artificial Intelligence and Machine Learning algorithms to learn normal process behavior and instantly pinpoint unusual patterns indicative of threats.

Provides unprecedented, highly detailed insights directly from the critical assets' electrical signals, offering deep visibility into complex operational processes.

Designed to provide an uncompromised first line of cyber defense, ensuring the integrity and continuous operation of critical infrastructure even during sophisticated cyberattacks.

Strengthens industrial safety by identifying, assessing, and mitigating risks to equipment and sensor operations, contributing to overall physical safety.

Instantly identifies and alerts on cyberattacks that manipulate physical control signals at the lowest levels of industrial systems.

Generates accurate and actionable alerts, minimizing false positives and enabling rapid, focused incident response.

Due to its out-of-band monitoring, SigaGuard provides an independent and uncompromised record of physical process behavior for post-incident investigation.

Applicable across numerous critical industries including Energy, Water, Chemicals, Manufacturing, Oil & Gas, Building Management Systems (BMS), Data Centers, Mining, and Transportation.

Designed for straightforward and adaptable deployment within existing industrial environments.

Operates independently of specific industrial protocols, ensuring broad compatibility with diverse systems.

Provides regular reports on normal operations and sends immediate alerts (e.g., SMS, control room indications) upon detecting anomalies.

Offers a Process Attack Simulation tool for training and preparing organizations for real-world OT cyberattack scenarios without disrupting operations.

The SigaML² suite uses multiple layers of machine learning to provide early alerts and support critical decision-making during the Incident Response phase.

Capable of detecting sophisticated attacks like False-Data Injection (similar to Stuxnet) by comparing Level 0 data with data from Levels 1-4 (PLCs, HMIs).

Their Level 0 monitoring approach is recognized by NIST (National Institute of Standards and Technology) as a best practice for OT cybersecurity.