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Data protection concept with digital elements

Data Protection Services

Safely leverage AI tools without risking your organization's cybersecurity

Comprehensive Data Protection

Our Data Protection service partners your team to protect sensitive data. We will train your people and stand up the tools necessary to tag, track, protect, and monitor sensitive data.

In today's AI-driven landscape, organizations face unprecedented challenges in protecting their data while still leveraging powerful AI tools. LeastTrust helps you navigate this complex terrain with a comprehensive, data-centric approach to security.

Data Classification & Tagging

Implement robust data classification systems to identify and tag sensitive information across your organization.

Employee Training

Develop a security-conscious culture through comprehensive training programs tailored to your organization's needs.

Data Governance

Establish clear policies and procedures for data access, usage, retention, and disposal.

AI-Safe Implementation

Leverage AI tools safely with proper guardrails and monitoring to prevent data leakage and security incidents.

Data protection concept

Evolving Cyber Strategy

The journey from perimeter defense to data-centric security

Past1990s-2000s

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
Present2010s

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)
Future2020s

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 and data security concept

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:

  • PrivacySocial Security numbers, credit card data (easily identified with regex patterns)
  • SensitiveCUI (Controlled Unclassified Information), requires manual procedures
  • ProprietaryTrade secrets, arbitrary tags (most difficult to classify)
  • PublicAll other data

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

1

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
2

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
3

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
4

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
5

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 Presentation

Ready 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.

Call Us Directly

Speak with our security experts immediately during business hours.

551-751-0010

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