The rapid evolution of the field of data governance: how organisations manage, process and secure information in a data-driven environment. More recently, synthetic data have been identified as a key class of privacy-enhancing technologies in a number of new studies (for example, the Royal Society’s extensive Synthetic Data Survey).
The growing array of innovative use-cases for increasingly complex artificial intelligence (AI) solutions in business has created an urgent need for a reliable strategy that harnesses this innovation while still protecting sensitive information. In tandem with cutting-edge research, organisations such as Elsewhen are applying AI-based methodologies to redefine enterprise digital landscapes.
The Promise of Synthetic Data
Synthetic data are artificially generated rather than obtained by direct measurement.
High-quality synthetic datasets can be tuned to minimise bias and enrich deep learning models, thereby improving AI performance. This method of generating adaptable, controlled data allows solutions to be tailored to specific business objectives.
The Royal Society Synthetic Data Survey: Key Findings
The Royal Society’s survey offers a detailed overview of these themes. Its findings demonstrate how synthetic data are already crucial for strengthening privacy protocols while also driving innovation in AI research and application. Key insights include:
- Not automatically private:
Naïve synthetic generation can leak real‐data information and is vulnerable to membership, attribute-inference and reconstruction attacks—rigorous privacy mechanisms (e.g. differential privacy) are essential. - Not a wholesale substitute for real data:
Privacy-guaranteed synthetic data necessarily distorts the original; final models should still be validated or fine-tuned on real data to avoid inference risks. - Outliers & minorities pose a dilemma:
Rare or low-probability events (e.g. “hidden” billionaires) and under-represented groups are hard to include without compromising privacy or signal, risking poor coverage and fairness. - Empirical privacy testing is limited:
Privacy must be proven on the generator (not just the dataset) via formal proofs or black-box leakage estimators; tests like nearest-neighbour distance can hint at issues but can also be misleading. - Opaque black-box models:
Over-parameterised generative models may produce realistic high-dimensional outputs, but their privacy and fidelity guarantees are hard to quantify uniformly across samples. - Beyond privacy:
Synthetic data offers promising tools for improving fairness, robustness and bias correction in ML systems, but practical frameworks and clear regulatory standards are still in development. - Custom-tailored trade-offs:
Utility, fidelity and privacy form a three-way trade-off—successful deployment requires choosing which statistical properties to preserve for a given use-case and applying appropriate privacy or fairness constraints.
These results not only illustrate the current state of the synthetic-data ecosystem but also reaffirm its pivotal role in shaping the next generation of AI-based, privacy-enhancing technologies.
Privacy-Enhancing Technologies (PETs) — A Paradigm Shift in Data Governance
Privacy-enhancing technologies encompass a broad set of tools and techniques designed to achieve data privacy without sacrificing utility. One of the foremost innovations within PETs is synthetic data: organisations conduct analytics and train AI models using synthetic, yet statistically representative data to preserve individual privacy.
Other PETs highlighted in the research include secure multiparty computation, differential privacy and federated learning — all essential for ensuring data can be used ethically and securely:
- Differential Privacy: Provides mathematical guarantees that the inclusion or exclusion of any single data point has a negligible effect on analytical outcomes.
- Federated Learning: A training approach that enables AI models to learn from decentralised devices while keeping data on local hardware rather than transferring it to a central server.
Combined with synthetic data, these techniques form a powerful framework for developing advanced AI systems that are intrinsically privacy-preserving.
Synthetic Data in the Agentic Enterprise
Synthetic data, while mitigating privacy and security challenges, will become even more impactful when integrated with sophisticated AI systems. Leading enterprise-tech providers such as Elsewhen are pioneering the “agentic enterprise” model, which leverages AI agents to automate operations and inform strategic decision-making.
This approach enables organisations to evolve their internal processes while navigating data-security and regulatory demands via a modular, carefully structured framework.
Future Directions: Uniting Synthetic Data, AI and Privacy
The future of synthetic data and PETs is clear: organisations will demand tools that foster rapid innovation without compromising privacy.
UK agencies like Elsewhen are laying the foundations for broader ecosystems in which AI agents collaborate — powered by synthetic data and PETs — to drive real-time innovation. These ecosystems redefine business operations, treating data as a strategic asset that is both powerful and secure.
The Royal Society’s Synthetic Data Survey provides a window into the present and future of privacy-enhancing technologies. Synthetic data are rapidly emerging as indispensable tools for protecting privacy and fuelling the innovation that underpins modern AI applications. Deploying PETs such as differential privacy and secure multiparty computation, alongside synthetic data within multi-agent frameworks, heralds a paradigm shift towards an agentic enterprise that is agile, secure and future-proof.
As we enter an era of strict data-privacy expectations, the convergence of advanced privacy-enhancing technologies and AI promises to unleash unprecedented levels of innovation and efficiency. By embedding synthetic-data frameworks within collaborative multi-agent ecosystems, organisations can drive sustainable growth, ensure compliance and secure a competitive edge in the digital age.
