Antigranular Enterprise (AGENT)
Unlock the Full Potential of Your Data — Securely
Balancing Data Utilization with Privacy & Compliance
Organisations generate vast amounts of sensitive data—customer records, financial transactions, healthcare insights — but privacy concerns and regulatory restrictions prevent them from fully leveraging this data.
Traditional methods
Traditional methods like data masking, tokenization, and synthetic data provide some protection but often come with major trade-offs:
Data masking & tokenization reduce exposure but don't protect against re-identification attacks.
Synthetic data mimics real datasets but lacks fidelity for deeper insights.
As a result
Businesses struggle to extract value from their data while ensuring compliance with GDPR, HIPAA, CCPA, and other regulations.
Antigranular Enterprise (AGENT): Privacy-First Data Collaboration
AGENT is a cutting-edge, scalable platform that integrates Differential Privacy (DP) to enable secure, privacy-preserving data analytics — without exposing raw data. It allows data scientists and machine learning models to work on sensitive data without compromising individual privacy.
How AGENT Works
AGENT operates as a middleware layer between your data sources and users, applying real-time privacy protection — without disrupting workflows.
1
Privacy-Preserving Analytics
2
Regulatory Compliance & Auditability
3
Seamless Integration & Scalability
Built for Your Current Stack
Enhancing data privacy doesn’t require re-engineering your workflows. AGENT integrates directly with your existing infrastructure, allowing data scientists to utilize it immediately, meaning no modifications are required to SQL queries or Jupyter Notebooks.
Users can annotate sensitive data cells with %%ag in Jupyter for automatic DP enforcement.
The interface queries sensitive datasets securely via a standard JDBC connector, dynamically applying DP safeguards.
Secure Data Collaboration
Enable multiple teams — or even different organisations — to analyze shared datasets without exposing raw data. Allow marketing, finance, and product teams to analyze customer trends without exposing raw data. Share valuable insights between partner companies, healthcare institutions, or research organisations without risking data leaks.
External Reporting
Generate GDPR- & HIPAA-compliant reports without compromising privacy. Ensure regulatory compliance without manual anonymization or redaction. Provide secure financial reporting or protecting patient confidentiality while sharing critical research findings. Maintain logs of all queries and privacy budgets to demonstrate compliance during audits.
Machine Learning on Sensitive Data
Train AI models on customer data without risk of exposing personal details. Allow different organisations to collaborate on AI development without sharing raw data and ensure that each training iteration maintains privacy through dynamic privacy budget allocation.
Offshore Team Enablement
Allow offshore teams to access critical data without violating cross-border privacy laws, ensuring they see only what they need. Offshore teams can perform analytics without ever seeing row-level data, which reduces legal risk and minimizes exposure to penalties.
