Synthetic Data Expert,
SAS
In an AI-first era where data drives everything from healthcare diagnostics to financial forecasting, enterprises are trying to leverage the power of sensitive data while protecting its privacy. Recent data breaches, costing companies billions in damages and eroding public trust, have highlighted the urgency of this challenge. The imminent need to protect sensitive and personal information in an increasingly digital world has reached a crucial juncture. Regulatory pressures, and the growing complexity of modern data ecosystems make it essential for enterprises to adopt innovative strategies to ensure compliance while fostering innovation – ones that go beyond traditional anonymization and offer mathematically guaranteed privacy. Among these strategies, synthetic data is emerging as a transformative solution that balances privacy with the demand for actionable insights.
The Data Privacy Dilemma
We live in an age where enterprises are caught between two powerful forces: the pressure to innovate with AI and the absolute necessity to protect privacy. The old ways of handling data – anonymization and redaction – are soon going to be eclipsed by synthetic data generation to meet the stringent demands of today’s regulatory landscape. As regulators increasingly recognize Differential Privacy (DP) as the gold standard for data protection, synthetic data trained with DP will soon become a necessity. SAS embeds DP as a default in its synthetic data generators, ensuring a higher privacy standard with mathematical guarantees.
As enterprises race to harness artificial intelligence’s potential, they consistently encounter a critical bottleneck: the scarcity of usable, privacy-compliant data. Synthetic data generation is emerging as the key to unlocking this deadlock. Beyond solving privacy concerns, it is evolving into a tool for AI fairness and model accuracy. Organizations can now engineer datasets that correct historical biases and ensure balanced representation across demographic groups. This capability is redefining AI ethics, model robustness, and fairness—key concerns in today’s AI-driven decision-making.
Recognizing the growing need for privacy-centric data solutions, SAS has strengthened its synthetic data capabilities with the recent acquisition of the principal software assets of Hazy, a pioneer in synthetic data technology. This strategic move enhances SAS’ robust data and AI portfolio, further equipping its customers with cutting-edge synthetic data generation capabilities as AI adoption accelerates.
As part of this commitment, SAS is also introducing the upcoming SAS Data Maker solution—a low-code/no-code platform designed to unlock the potential of existing data by augmenting or generating synthetic data quickly. Unlike standalone synthetic data tools, SAS Data Maker backed by the vast SAS analytics ecosystem, can allow enterprises to move from data to synthetic data to insights – within a single, unified platform. This eliminates data silos and accelerates AI-driven innovation. With enterprises facing increasing regulatory scrutiny and growing demand for high-quality AI training data, SAS Data Maker will empower organizations to innovate securely and efficiently.
The Future of Synthetic Data
As AI adoption accelerates, synthetic data will shift from being a niche privacy solution to becoming a fundamental component of data science and provisioning workflows. Organizations will increasingly use it to fast-track AI projects, enhance data for specific use cases, and overcome accessibility barriers.
At the same time, synthetic data platforms—historically specialized in domains like tabular data, images, or text—will evolve to support broader data types and multimodal AI applications. Enterprises will see more integrated approaches where synthetic data seamlessly powers predictive modeling, computer vision, and large language models, making it a cornerstone of AI development.
The rise of synthetic data in 2025 marks a pivotal moment in the evolution of AI and data privacy. Far beyond a mere technological trend, it represents a fundamental shift in how enterprises approach innovation while committing to data privacy. As enterprises face mounting pressure to deliver AI-powered solutions in an increasingly privacy-conscious world, synthetic data emerges as the bridge between these seemingly competing demands.
This shift represents a commitment to a future where innovation flourishes without compromising privacy rights. It’s a future where enterprises can confidently push the boundaries of what’s possible while maintaining the highest data protection standards and ethical AI development. As we move forward, synthetic data will increasingly be recognized not just as a tool for compliance, but as a driver of AI agility, fairness, and innovation – ensuring privacy is at the heart of technological progress.