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Advanced Data Protection for Designs, IP and Supply Chain Data
Manufacturers protect product designs, CAD files, process recipes, supplier pricing and production schedules, and share them every day across plants, engineering teams and suppliers.
With operations spread over many sites and a supply chain built on constant data exchange, tracking where that information goes by hand is close to impossible. A single leaked design can hand a competitor years of R&D.
Industry security solutions tailored to your needs
Eliminates blind spots
100% autonomous coverage across every
actor, every channel, every threat vector.
Award winning
DLEP solution
GC Cybersecurity’s ISE™ (Information Security Enforcer) is the “Best of Breed” Data Leak and Exfiltration Prevention solution.
Prevents accidental
or malicious activities
Regulated industries face severe challenges in Data Leak Prevention (DLP) and Data Privacy Compliance
01
Automated Classification of Engineering and Production Data
ISE identifies and classifies designs, drawings, specifications and production data in motion, including unstructured files, and applies real-time policies based on how sensitive each piece of information is.
02
Controlled Sharing Across Plants and Suppliers
ISE enforces granular policies on which engineering and supply chain data can go to which suppliers, contractors and sites. Collaboration keeps moving while sensitive IP stays protected.
03
CASB – Cloud and Web Application Security
As more sensitive content moves to the cloud and key applications move to the web, sensitive information has to be protected at every moment. Precise cloud and web security policies and automated classification cover operations from start to finish.
04
Central Control for Distributed Operations
One policy set covers every site and channel, with central visibility into who moved which data and where it went.
Generative AI’s increasing access within enterprises creates significant risk of sensitive data leaks through authorized users inadvertently or intentionally sharing proprietary information with external AI systems
Automated data protection system monitors and analyzes user interactions with Deep-AI tools, using real-time recognition and domain aware policies to detect, prevent, and alert enterprises to potential data leakages before sensitive information leaves secure environments
Enterprises can confidently embrace GEN-AI technologies while maintaining regulatory compliance, protecting intellectual property, preventing reputational damage, and reducing security team burden through automated detection and prevention that works transparently without disrupting legitimate user workflows
Malicious insiders exploit their legitimate access to exfiltrate sensitive data via email channels, bypassing traditional security controls and causing significant financial, reputational, and compliance damage
Automated and advanced e-mail data protection systems employ Deep-AI, behavioral analytics, and information classification to detect suspicious patterns, unauthorized transfers, and policy violations in real-time before sensitive data leaves the enterprise
Enterprises gain continuous protection against insider threats with minimal false positives, maintain regulatory compliance, protect intellectual property, prevent data breaches, and reduce security team workload through accurate automated detection that stops data loss while allowing legitimate business email communications to continue unimpeded
Malware and ransomware now steal sensitive data before they encrypt systems, using compromised endpoints and legitimate channels to move data out of the enterprise without triggering traditional perimeter defenses
Automated data protection classifies sensitive data-in-motion in real time and detects abnormal exfiltration from compromised hosts and processes, blocking unauthorized transfers before data leaves the environment, whatever channel the malware uses
Enterprises achieve dramatically improved resilience against evolving cyber threats, significantly reduced attack surface, minimized data breach impact, faster incident response times, continuous regulatory compliance, and preserved business continuity without overwhelming security teams or disrupting legitimate business operations
Enterprises struggle to identify and protect massive volumes of diverse sensitive data moving across networks, applications, and user devices, leaving critical information vulnerable to breaches and compliance violations. Simple manual Keyword and Lexical Matches are error prone resulting in high False Positives and are highly susceptible to Malicious Data Leak scenarios
Automated classification systems leverage DEEP-AI, and domain-aware pattern recognition to accurately identify, categorize, and tag sensitive structured and unstructured data-in-motion, in any format, in real-time without human intervention, enabling immediate policy enforcement before information reaches unauthorized destinations
Enterprises achieve comprehensive visibility into data flows, maintain continuous regulatory compliance, reduce security team burden, prevent costly data breaches, enable granular access controls, and support informed business decisions through precise risk awareness without impacting legitimate data transfers or user productivity
Enterprises face significant risks when employees accidentally or maliciously transfer large sensitive files through unauthorized channels, bypassing security controls and potentially causing data breaches, compliance violations, and intellectual property loss
Automated file transfer protection systems continuously monitor network traffic, file repositories, and communication channels to detect unusual file movements, classify sensitive data in real-time, and enforce granular policies that block unauthorized transfers while allowing legitimate business operations
Enterprises gain comprehensive protection against data loss without burdening security teams, maintain regulatory compliance, preserve intellectual property, prevent costly breaches, reduce incident response time, and maintain business productivity by stopping only truly risky transfers while allowing approved workflows to continue unimpeded
Modern enterprises struggle with fragmented security systems that generate overwhelming alerts without domain context, leading to detection gaps, delayed remediation of data security incidents, and increased risk of breaches despite significant security investments
Automated cyberSOC-powered platforms integrate Deep-AI-driven analytics, comprehensive visibility across environments, and orchestrated response capabilities to detect sensitive data violations in real-time, prioritize actual threats, and automatically implement remediation actions based on security policies
Enterprises achieve dramatically faster threat detection and response times, reduced security team burnout, continuous compliance with evolving regulations, minimized data breach impacts, improved security ROI, and comprehensive protection across hybrid environments while maintaining business operations through intelligent, context-aware automated remediation workflows
Challenge
Generative AI’s increasing access within enterprises creates significant risk of sensitive data leaks through authorized users inadvertently or intentionally sharing proprietary information with external AI systems.
Solution
Automated data protection system monitors and analyzes user interactions with Deep-AI tools, using real-time recognition and domain aware policies to detect, prevent, and alert enterprises to potential data leakages before sensitive information leaves secure environments.
Benefit
Enterprises can confidently embrace GEN-AI technologies while maintaining regulatory compliance, protecting intellectual property, preventing reputational damage, and reducing security team burden through automated detection and prevention that works transparently without disrupting legitimate user workflows.
Case study
Manufacturing: Autonomous IP Protection Across 40+ Sites
How a manufacturer protects designs and production IP across more than 40 sites with ISE.
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Tarique Mustafa on where most organizations are misreading the cybersecurity problem in the AI era — and why treating GenAI risk as a feature gap rather than an architectural one leaves sensitive data exposed.
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