Enable Trusted Business Use.

A comprehensive suite of privacy-preserving mechanisms built into your data pipeline.

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https://www.gotrust.tech

What We Offer

Five Pillars of Privacy Enhancing Techniques

Every technique works together within GoTrust's governance framework.

Data Anonymization

Irreversibly transform personal identifiers so individuals cannot be re-identified — even against external auxiliary data.

Data Tokenization & Minimization

Replace sensitive values with format-preserving tokens and enforce purpose-based rules that limit which fields are ever collected, stored, or shared.

Dynamic Data Masking

Mask sensitive values in real time based on user role, request context, and access policy.

Data Provisioning

Automate controlled dataset delivery to authorised users based on governance policies — every provision is role-scoped, logged, and revocable.

Data Fabrication

Generate statistically faithful synthetic datasets that mirror real data distributions and edge cases — with zero personal data in the output, eliminating re-identification risk entirely.

Before vs. After

What changes when you add GoTrust

Five techniques. Five transformations. Every row is a risk your team no longer carries.

Without GoTrust

With GoTrust

Data Anonymization

PII stored in plain text across databases. Re-identification is possible when cross-referenced with external datasets — even 'anonymised' exports carry residual risk.

Data Anonymization

k-anonymity, l-diversity, and differential privacy applied at source. Re-identification becomes mathematically infeasible. Every output carries a verifiable privacy-risk score.

Data Tokenization & Minimization

Sensitive values — card numbers, national IDs, health records — travel in plain text through pipelines. More fields collected than any downstream process actually needs.

Data Tokenization & Minimization

k-anonymity, l-diversity, and differential privacy applied at source. Re-identification becomes mathematically infeasible. Every output carries a verifiable privacy-risk score.

Dynamic Data Masking

Everyone queries the same raw dataset. Analysts, contractors, and admins see identical PII regardless of role or need. Separate masked copies multiplied across environments.

Dynamic Data Masking

Each user receives a contextually masked view — in real time, from a single governed source. No duplicate datasets. Masking logic changes with role; the data does not.

Data Provisioning

Data shared via manual exports, email attachments, or ad-hoc scripts. No expiry windows. No audit trail. No visibility into who holds what version, or for how long.

Data Provisioning

Policy-gated, role-scoped dataset delivery. Every provision is logged, timestamped, and traceable. Access is automatically revoked when the window expires — no manual cleanup.

Data Fabrication

Real production data used in dev, QA, and ML pipelines. Consent overhead, breach exposure, and compliance risk accumulated on every test cycle. GDPR-scope widens with each copy.

Data Fabrication

Statistically faithful synthetic data mirrors distributions, correlations, and edge cases — with zero PII in the output by construction. No consent. No re-identification risk. No compliance drag.

ONE GOVERNANCE LAYER. THREE MECHANISMS.

Consistent policy. Single audit log. Every action governed.

GoTrust PET Engine Automates Data Set Delivery Based on your Governance Policy

Policy-Driven Access

Grant or restrict dataset access using fine-grained governance policies tied to user roles, departments, and data classification levels.

User-Scoped Delivery

Automatically tailor the dataset view each user receives — masking, filtering, or truncating fields they are not permitted to see.

Continuous Monitoring

Track every provisioning event in real time with alerts for anomalous access patterns, bulk downloads, or policy violations.

Seamless Integration

Connect to your existing data catalog, identity provider, and warehouses. Provisioning policies apply across all connected sources.

Audit-Ready Logs

Generate compliance-ready reports on all data access events, exportable for GDPR, HIPAA, or internal audit requirements.

Time-Bound Permissions

Set expiry windows on data access grants. Permissions automatically revoke after the defined period, reducing standing exposure.

HOW IT WORKS

Privacy Enhancement Techniques activate automatically across your data lifecycle.

Step 1 — Data Ingestion

Raw data enters GoTrust from source systems via connectors or APIs.

Step 2 — Classification

Fields are automatically classified by sensitivity level using detection rules and ML tagging.

Step 3 — Policy Application

PET rules are applied — anonymization, masking, minimization — based on data class and context.

Step 4 — Provisioning

Processed data is provisioned to authorized users within their permitted scope.

Step 5 — Audit & Review

Every action is logged. Compliance reports are generated continuously and on demand.

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Talk To Our Expert

Schedule a session with our team to see how GoTrust unifies consent, privacy automation, DSPM, and governance into one intelligent compliance platform.

Request Demo

info@gotrust.tech

India

Noida

303, Tower C, ATS Bouquet, Noida Sector 132, U.P.

mumbai

1st Floor, Raheja Platinum, WeWork, K, Marol, Andheri East, Mumbai, Maharashtra 400059

UAE

DIFC Innovation Hub, Gate Avenue, Zone D, Co-working Space Level 1 Al Mustaqbal St, Dubai

Netherlands

Cuserpark Amsterdam, De Cuserstraat 91, 1081CN, Amsterdam, Netherlands

info@gotrust.tech

India

Noida

303, Tower C, ATS Bouquet, Noida Sector 132, U.P.

mumbai

1st Floor, Raheja Platinum, WeWork, K, Marol, Andheri East, Mumbai, Maharashtra 400059

UAE

DIFC Innovation Hub, Gate Avenue, Zone D, Co-working Space Level 1 Al Mustaqbal St, Dubai

Netherlands

Cuserpark Amsterdam, De Cuserstraat 91, 1081CN, Amsterdam, Netherlands

info@gotrust.tech

India

Noida

303, Tower C, ATS Bouquet, Noida Sector 132, U.P.

mumbai

1st Floor, Raheja Platinum, WeWork, K, Marol, Andheri East, Mumbai, Maharashtra 400059

UAE

DIFC Innovation Hub, Gate Avenue, Zone D, Co-working Space Level 1 Al Mustaqbal St, Dubai

Netherlands

Cuserpark Amsterdam, De Cuserstraat 91, 1081CN, Amsterdam, Netherlands

Before vs. After

What changes when you add GoTrust

Five techniques. Five transformations. Every row is a risk your team no longer carries.

Without GoTrust

With GoTrust

Data Anonymization

PII stored in plain text across databases. Re-identification is possible when cross-referenced with external datasets — even 'anonymised' exports carry residual risk.

Data Anonymization

k-anonymity, l-diversity, and differential privacy applied at source. Re-identification becomes mathematically infeasible. Every output carries a verifiable privacy-risk score.

Data Tokenization & Minimization

Sensitive values — card numbers, national IDs, health records — travel in plain text through pipelines. More fields collected than any downstream process actually needs.

Data Tokenization & Minimization

k-anonymity, l-diversity, and differential privacy applied at source. Re-identification becomes mathematically infeasible. Every output carries a verifiable privacy-risk score.

Dynamic Data Masking

Everyone queries the same raw dataset. Analysts, contractors, and admins see identical PII regardless of role or need. Separate masked copies multiplied across environments.

Dynamic Data Masking

Each user receives a contextually masked view — in real time, from a single governed source. No duplicate datasets. Masking logic changes with role; the data does not.

Data Provisioning

Data shared via manual exports, email attachments, or ad-hoc scripts. No expiry windows. No audit trail. No visibility into who holds what version, or for how long.

Data Provisioning

Policy-gated, role-scoped dataset delivery. Every provision is logged, timestamped, and traceable. Access is automatically revoked when the window expires — no manual cleanup.

Data Fabrication

Real production data used in dev, QA, and ML pipelines. Consent overhead, breach exposure, and compliance risk accumulated on every test cycle. GDPR-scope widens with each copy.

Data Fabrication

Statistically faithful synthetic data mirrors distributions, correlations, and edge cases — with zero PII in the output by construction. No consent. No re-identification risk. No compliance drag.