The Rise of AI-Generated Synthetic Data: A Strategic Lever for Scaling AI in Regulated Industries

Over 80% of enterprise AI initiatives fail to progress beyond pilot or limited deployment. In regulated industries, the most persistent constraint is neither algorithms nor infrastructure, but somewhat restricted access to usable data due to privacy, security, and regulatory controls. As financial services, healthcare, life sciences, and public-sector organisations accelerate AI investment, the gap between model ambition and data permissibility has become a structural barrier to scaling. 

AI-generated synthetic data has now crossed a critical inflection point. What began as a tactical solution for testing and anonymisation is increasingly being deployed as a strategic enabler of compliant AI at scale. Leading enterprises are using synthetic data to unlock otherwise inaccessible datasets, compress model development and validation cycles, and meet heightened expectations around auditability, lineage and governance. 

For consulting leaders advising regulated clients, synthetic data is no longer a specialist capability or experimental add-on. It is a foundational lever that reshapes how AI programmes are designed, governed and industrialised. The strategic question is no longer whether synthetic data can be used, but how it can be operationalised in a way that is regulator-ready, enterprise-grade and defensible under scrutiny. 

Why regulated industries are turning to synthetic data now 

Regulated sectors face a convergence of pressures that make traditional data strategies increasingly untenable. Data volumes are growing rapidly, regulatory oversight is intensifying, and AI use cases are expanding from descriptive analytics into high-impact decision support and automation. 

In financial services, access to transaction-level and customer behavioural data is tightly constrained by privacy and banking regulations. In healthcare and the life sciences, patient confidentiality, consent management, and cross-border data transfer restrictions severely limit the reuse of clinical and real-world datasets. Public-sector and defence environments face additional national security and data sovereignty constraints. 

Synthetic data directly addresses these challenges by decoupling model development from raw production data while preserving statistical fidelity and structural relationships. This enables organisations to expand training, testing and validation activity without proportionally increasing compliance exposure. Importantly, this shift aligns with regulator expectations around minimisation and risk-based data use, rather than attempting to bypass them. 

From experimentation to measurable business impact 

As synthetic data adoption matures, its value is increasingly articulated in terms of operational and financial benefits rather than theoretical advantages. Enterprises are deploying synthetic data across three high-impact domains. 

First, test data provisioning. Generating high-quality synthetic datasets dramatically reduces the time and cost required to create compliant test and development environments. Large enterprises report reductions of several months in environment setup timelines, with corresponding savings in engineering effort and programme delay costs. 

Second, model training and validation. Synthetic data enables broader coverage of edge cases, rare events and stress scenarios that are often underrepresented in real datasets. This is particularly valuable in risk modelling, fraud detection, and clinical decision support, where model robustness is critical, and failure modes carry regulatory consequences. 

Third, data sharing and collaboration. Synthetic datasets enable internal teams, external partners, and vendors to collaborate without exposing sensitive information. This has become especially relevant as organisations integrate third-party AI tools and cloud-based analytics platforms into regulated workflows. 

Enterprise platforms and vendor consolidation

 

The strategic importance of synthetic data is reflected in the accelerating integration of platforms and vendor consolidation. Databricks has embedded synthetic data generation and evaluation capabilities into its Data Intelligence Platform, positioning synthetic data as a standard component of enterprise ML workflows rather than a standalone tool. 

Mostly AI, headquartered in Vienna, has emerged as a dedicated enterprise synthetic data provider, offering high-fidelity generation, privacy risk quantification and deployment options compatible with Kubernetes and OpenShift environments. Its focus on governance, scalability and regulatory alignment has driven adoption among large financial institutions and public-sector organisations.

Major platform players have also made decisive moves. SAS expanded its synthetic data capabilities through the acquisition of assets from Hazy, integrating synthetic data into its analytics and AI portfolio for regulated industries. Nvidia has invested heavily in synthetic data generation for simulation and model training, particularly in perception, robotics and autonomous systems, signalling that synthetic data is becoming core infrastructure rather than auxiliary tooling. 

These moves underscore a clear market signal. Synthetic data is being embedded where enterprise buyers expect durability, support and compliance assurance. 

Regulatory and governance implications 

Despite its advantages, synthetic data does not eliminate regulatory responsibility. Regulators are increasingly viewing synthetic datasets through a risk-based lens, focusing on the potential for re-identification, bias propagation, and undocumented model behaviour. 

The European Union AI Act, which entered into force in 2024 with phased applicability, formalises expectations around data governance, transparency and risk management for high-risk AI systems. Synthetic data used in training or validation does not exempt organisations from these obligations. Instead, it introduces new requirements regarding documentation, provenance, and quality assurance. 

Leading organisations are responding by treating synthetic data generation as a governed process. This includes maintaining versioned records of generation parameters, conducting quantitative privacy risk assessments, and ensuring traceability from synthetic dataset to model outcome. These controls are increasingly essential for audit readiness and regulatory engagement. 

Operating model for industrialising synthetic data 

Organisations that scale synthetic data successfully adopt an operating model that mirrors other enterprise-critical capabilities. 

At the policy layer, leadership defines permitted use cases, minimum quality thresholds and approval checkpoints aligned with regulatory risk profiles. At the platform layer, synthetic data generation, validation and lineage capture are standardised and integrated into existing data and MLOps stacks. At the assurance layer, independent validation, audit artefacts and re-certification processes ensure ongoing compliance as models evolve. 

Critically, synthetic data is embedded into CI/CD pipelines for AI models, enabling repeatable and auditable releases. This reduces manual intervention, accelerates deployment cycles and provides regulators with clear evidence trails. 

Conclusion: A strategic imperative for regulated AI scale

 

For regulated industries, synthetic data has moved decisively beyond experimentation. It is now a strategic capability that enables AI scale while strengthening, rather than weakening, governance and compliance. 

Organisations that invest early in enterprise-grade synthetic data platforms, robust governance models and regulator-aligned operating processes gain a durable advantage. They shorten the time to value, reduce compliance friction, and expand the range of AI use cases they can pursue with confidence. 

For executive teams, the priority is clear. Synthetic data should be evaluated not as a data science tactic, but as a core element of the enterprise AI strategy, with ownership, investment and accountability at the highest levels of the organisation.