Proprietary Data Protection
Protect the 90% of your data that isn't regulated but drives your business—trade secrets, IP, strategic plans, and competitive intelligence. Focus on what matters most: your competitive advantage.
Why Proprietary Data Requires Different Protection
HIPAA, PCI, and GDPR don't protect trade secrets. They don't protect your customer lists, strategic plans, product roadmaps, or competitive intelligence. This unregulated proprietary data is what drives your business value—and what insiders target most. Traditional security focuses on compliance. We focus on protecting what matters: your competitive advantage.
Our data-centric approach helps you identify, classify, control access to, monitor, and defend your crown jewels. From establishing "reasonable measures" for legal defensibility to detecting unauthorized access by insiders, we help you protect the 90% that regulations ignore.
Proprietary Data Identification
Identify what's truly proprietary—trade secrets, IP, competitive intelligence, strategic plans. Not all data is equal. Classify and tag what drives your business value.
Access Controls & Least Privilege
Who needs access to your crown jewels? Implement need-to-know controls, monitor access patterns, and detect anomalies before data walks out the door.
DLP for Proprietary Data
Traditional DLP focuses on PII. We configure DLP to detect trade secrets, proprietary methodologies, customer intelligence, and strategic data exfiltration.
Legal Defensibility
Document "reasonable measures" to protect trade secrets. Support IP litigation under DTSA and Economic Espionage Act. Forensic readiness for insider investigations.
Evolving Cyber Strategy
The journey from perimeter defense to data-centric security
Perimeter Defense
Traditional "Castle and Moat" approach focused on defending the network perimeter.
- Firewalls as primary defense
- Clear network boundaries
- Antivirus solutions
- Password-based authentication
- Treats all data the same inside the walls
Identity-Centric Security
Zero Trust Architecture where "Never trust, always verify" is the guiding principle.
- Identity as the new perimeter
- Multi-factor authentication
- Least privilege access
- Microsegmentation
- Data governed by access (file, folder, or SQL field)
Data-Centric Security
Data as the Control Point with context, classification, and governance at its core.
- Data discovery & tagging
- Continuous context & classification
- AI-driven rule creation & alerting
- Data lineage tracking and logging
- Data governed by context (unstructured search and inference)
AI Readiness & Data-Centric Security
How prepared is your organization for the shift to data-centric security?
AI Accelerates Data Search and Inference
In the past, unstructured data would be very difficult to find ("security by obscurity") - AI skips the data structuring necessity, making all your data potentially accessible and analyzable.
How to Fuel AI While Ensuring Optimal Security
Classify and tag all data with essential tags including:
Inference Cannot Be Underestimated
S.E.C. Mosaic Theory: Data classified as non-material, non-public can infer material, non-public information when combined with other data points.
Implementation Roadmap
A systematic approach to implementing data-centric security in your organization
Maintain Existing Defenses
Don't retire historical defenses. They are considered necessary cyber hygiene and will continue to deter attacks and data leaks.
- Continue using firewalls, antivirus, and other perimeter defenses
- Leverage existing telemetry and logging to complement data tracking
- Maintain access tracking and usage monitoring
- Enforce retention policies and audit trails
Classify Your Data
Set a goal to classify as much data as economically viable. Start with regex-based privacy data, then move to sensitive and proprietary information.
- Use SaaS tools to automate privacy data tagging (Microsoft Purview, Varonis, Netrix, Cavelo)
- Implement human tagging for non-repetitive patterns or sensitive data
- Develop policies and procedures to classify, watermark, and secure data
- Create a corporate culture that recognizes and self-classifies data
Train Your Team
Develop and educate corporate culture that recognizes and self-classifies data. Test, measure, audit, and iterate for improvement over time.
- Implement comprehensive training programs for all employees
- Create incentives to drive intended behavior
- Establish clear guidelines for data handling
- Regularly test and assess employee knowledge and compliance
Leverage AI for Classification
Eventually, manual tagging logs can be utilized for supervised learning and model training that will offload and assist the task.
- Use context, edits, and tracking history as valuable datasets for machine learning
- Implement supervised machine learning for tagging
- Continuously improve classification models
- Automate routine classification tasks while maintaining human oversight
Measure and Improve
Continuously measure, audit, and track classification progress to ensure effectiveness and identify areas for improvement.
- Establish key performance indicators for data protection
- Regularly audit classification accuracy and coverage
- Track incidents and near-misses to identify patterns
- Continuously refine your approach based on results and emerging threats
Want to learn more?
Download our comprehensive guide on AI readiness and data-centric security.
Download PresentationReady to Secure Your Data?
Get in touch with our security experts to discuss your specific needs and how we can help protect your valuable assets.