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IT & OT Security and Cyber Protection for Manufacturing

Protect Your IP, Brand, and Revenue from Cyberattacks and Data Exfiltration


The manufacturing sector has an especially challenging task when it comes to cyber protection. Security managers in manufacturing organizations are dealing with new cyber risks associated with the systems that control operations of a production facility. This is in addition to traditional IT cybersecurity risks on the business operations side. Today, interconnectivity between the OT and the IT environments is common, which exposes the OT side to the Internet and all its risks for the first time. This is one of the reasons the manufacturing sector is now the second most targeted industry for cyber-attacks, falling behind only the financial services and insurance industry.



  • Centralize and streamline access to cybersecurity data
    across both IT and OT to drive AI-based analytics for identifying and managing cybersecurity risks
  • Identify, correlate, and detect threats
    targeting IoT/ICS infrastructure
  • Eliminate unnecessary access rights and excessive privileges
    to increase data and IP protection
  • Detect and prevent malicious activity
    by intruders, employees, and supply-chain partners


Detect APT attacks and attack vectors and predict data exfiltration by performing entity centric anomaly detection.

Provide risk-based dashboard for closely monitoring high-risk entities and investigation using detailed anomaly timeline based on users, accounts, alerts, and activities associated with the entity.

Support customized and unique DLP and data classifications, metadata model and big data infrastructure.



Utilizing machine learning and trained on a massive database of real-world incidents, the Gurucul platform is constantly learning from the environment it’s protecting, whether it’s IT or OT, on premise, in the cloud, or hybrid. The platform can scale to the largest IoT inventory of devices to identify, prioritize, and alert on risks before they become breaches. Gurucul helps overcome your resource constraints with the information your SOC team needs to resolve incidents.

Most manufacturers are required to follow some cybersecurity and privacy standards, laws, regulations, or requirements. Gurucul’s platform helps to facilitate compliance with standards and regulations such as ISO/IEC 27001, NIST 800-53, DFARS, ITAR, PCI DSS, the IoT Cybersecurity Act, and others.



Data Exfiltration

Data Exfiltration

Identify data exfiltration and protect intellectual property by ingesting data sources such as DLP and data classification to uncover important data locations, access, and application activity. Risk scoring DLP alerts is a primary benefit of machine learning because it significantly reduces alert fatigue and prioritizes ‘find-fix’ resources to prevent data breaches.

IP Protection

IP Protection

Gurucul provides OOTB anomaly models which can identify known patterns such as sensitive documents downloaded and copied to USB, large amounts of source code checked out from source code repositories and file uploads to cloud storage or emails to personal accounts, or access to competitor / job websites, etc.

IOT Analytics

IOT Analytics / Device Compromise

Detect advanced persistent threat (APT) attacks and attack vectors and predict data exfiltration by performing entity-centric anomaly detection. The Gurucul platform correlates a wide range of parameters associated with an entity, including endpoint security alerts, vulnerability scan results, risk levels of users and accounts used, targets accessed, packet level inspection of the requested payloads, and more. This correlation facilitates detection of any anomalous activities or events.

Partner Account Compromise

Vendor / Partner Account Compromise

Identify high-risk profiles with risk-based analytics, data mining, anomaly, and behavior detection. Help security teams by creating a baseline using profiling attributes from HR records, events, access repository, log management solutions and more. This enables manufacturers to easily detect and predict abnormal supply-chain partner behavior associated with potential sabotage, data theft or misuse.