StepAhead enables enterprises to safely unlock the full value of their sensitive data for analytics, application development, and AI/ML model training.

Our on-premise differential privacy solution—Tarmiz—anonymizes data at the source using a unique hybrid approach that blends the rigor of differential privacy with the fidelity of original datasets. Instead of obscuring sensitive information by adding noise to every query or generating synthetic stand-ins, Tarmiz injects mathematically governed noise directly into the underlying dataset. The result is new, high-accuracy anonymized data that preserves statistical structure and utility of the original, while providing strong privacy guarantees. 

Tarmiz supports a wide range of data types—not just numeric attributes. And unlike other commercial DP tools that apply a single accuracy/privacy tradeoff to an entire dataset, Tarmiz allows attribute-level control over epsilon or "noise" values. Each data field can be tuned independently, producing an anonymized dataset with higher overall accuracy, while enabling teams to tailor privacy levels to the specific requirements of different use cases.

With Tarmiz, enterprises can anonymize once and use freely—no collaboration environments or clean rooms, no ongoing monitoring, no restrictive governance workflows. Teams across research, product development, and AI can finally access sensitive data safely, accelerating innovation without increasing compliance risk.

StepAhead helps CISOs, data leaders, and engineering teams:
• Enable secure internal data use across the organization
• Train and fine-tune AI models on privacy-protected, high-fidelity data
• Unlock new data commercialization opportunities through compliant, shareable datasets
• Replace costly, complex clean-room approaches with a faster, simpler alternative

Our mission is to make advanced privacy protection effortless—and to let enterprises step confidently into the AI future with data that is both safe and useful.

StepAhead Data Anonymization

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