[Principles]

Data Privacy by Design

Enterprise AI must be built on a foundation of trust. Our Data Privacy by Design principle ensures that privacy, security, and compliance are embedded into every solution from the very beginning—not added as an afterthought.

Our Approach to Data Privacy

At Govishi, we believe that enterprise AI must be built on a foundation of trust. Our Data Privacy by Design principle ensures that privacy, security, and compliance are embedded into every solution from the very beginning—not added as an afterthought.

Hypothesis → Impact
Data Sovereignty
Before: External processing
After: 100% within perimeter ↑ Control
Hypothesis → Impact
Compliance Risk
Before: Manual checks
After: Automated assessment ↓ 85%
Hypothesis → Impact
Audit Time
Before: Weeks
After: Hours ↓ 90%

Core Privacy Commitments

Your Data Stays Within Your Control

Your data never leaves your perimeter unless explicitly approved.

Edge-First Architecture

Edge-first architecture where possible to minimize data movement.

Clear Data Lineage

Clear data lineage tracking for all AI training and inference.

Open-Source Components

Open-source and auditable components where possible.

Full Visibility

Full visibility into model training data sources.

Explainable AI

Explainable AI approaches that document decision paths.

Automated Privacy Assessments

Automated privacy impact assessments.

Granular Access Controls

Granular access controls and audit trails.

Configurable Policies

Configurable retention and deletion policies.

End-to-End Encryption

End-to-end encryption for data in transit and at rest.

Secure Model Deployment

Secure model deployment within your security perimeter.

Regular Testing

Regular penetration testing and vulnerability assessments.

Our Privacy Framework in Action

01
Discovery Phase

Before any AI solution is designed, we conduct a comprehensive privacy assessment to understand data sensitivity classifications, regulatory requirements, existing privacy controls, and potential privacy risks.

02
Design Phase

Privacy considerations are embedded into solution architecture with data minimization principles, privacy-preserving techniques, anonymization strategies, and clear data flows with privacy checkpoints.

03
Implementation Phase

Privacy controls are built into the technical implementation with secure coding practices, privacy-focused testing, audit logging for all data access, and role-based access controls.

04
Operational Phase

Ongoing privacy governance ensures continued compliance with regular privacy reviews, monitoring for privacy violations, continuous improvement of privacy controls, and documentation for regulatory compliance.

Industry-Specific Privacy Expertise

Our privacy frameworks are tailored to meet the unique requirements of regulated industries:

Financial Services

Compliance with GLBA, PCI-DSS, and regional banking regulations. Secure handling of personally identifiable financial information. Transaction data protection and fraud prevention privacy controls.

Healthcare

HIPAA and HITECH compliance built into healthcare AI solutions. PHI protection through advanced anonymization techniques. Patient consent management frameworks.

Retail and Consumer

GDPR, CCPA, and emerging privacy regulation compliance. Consumer preference and consent management. Privacy-first personalization techniques.

Legal & Professional Services

Attorney-client privilege protection. Confidential document handling protocols. Secure collaboration with sensitive client information.

Partner With Us for Privacy-First AI

Our commitment to Data Privacy by Design means you can deploy AI with confidence, knowing that privacy and compliance are never compromised in pursuit of innovation.