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Advanced Data Protection for Research IP and Patient Data
Life sciences companies run on intellectual property: compound libraries, formulations, trial protocols, regulatory submissions and manufacturing processes. One leaked file can cost years of research and a competitive lead.
Clinical trials also generate patient and trial data covered by HIPAA, GDPR and FDA 21 CFR Part 11, and that data is shared daily with CROs, trial sites, partners and regulators.
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 Research and Clinical Data
ISE identifies and classifies research documents, trial data and patient records in motion, structured or unstructured. It applies real-time policies based on how sensitive the data is and the clearance of the person sharing it.
02
Research IP Protection Across Partners and GenAI
Researchers work with CROs, academic partners and licensing teams, and more of them use GenAI tools every day. ISE controls what leaves through email, cloud storage and AI assistants, so proprietary research stays inside approved boundaries.
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
Audit-Ready Records for Regulated Data
Every enforcement decision is logged with its evidence, which keeps trial and patient data ready for audits and inspections without manual reconstruction.
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
Pharma: Protecting Research IP and Patient Data in Email
How a pharmaceutical company keeps research IP and patient data from leaking through email with ISE.
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See More →Tarique M. at MIT Review: Introduction
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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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