As AI becomes embedded deeper into everyday operations, enterprises are feeding more data into models than ever before. Large language models (LLMs) are now common in customer support, analytics, developer productivity, and knowledge management. AI agents add another layer: systems that can retrieve information, reason over it, and take action across tools and workflows.

However, this presents an uncomfortable reality for consumers: the most valuable data for improving AI performance is often the most sensitive. Support transcripts, case notes, transaction histories, and operational logs can contain personally identifiable information (PII), regulated attributes, or proprietary business context. For instance, India’s Digital Personal Data Protection (DPDP) Act, data carries clear obligations around consent, purpose limitation, and accountability. However, even with strong intentions to uphold privacy, it is easy for sensitive fields to slip into training corpora, evaluation sets, or prompt libraries, especially when teams are moving quickly to build and scale AI use cases.

According to a recent report by Grandview Research, the synthetic data generation market in India is expected to reach a projected revenue of US$158.1 million by 2030, growing at a compound annual growth rate (CAGR) of 39.2% between 2024 and 2030. This rapid growth explains why synthetic data has gained renewed attention. At its simplest, synthetic data is algorithmically generated data designed to reflect key patterns in real datasets without reproducing actual records. In theory, it offers a path to accelerate AI development while reducing exposure to highly sensitive information. However, does synthetic data truly remove risk, or merely shift it?Why privacy risk is rising in the age of LLMs and agents

Traditional analytics workflows tend to have clearer boundaries: data is curated, aggregated, masked, and used for defined purposes. However, LLM-driven development blurs these boundaries. Many inputs are unstructured, sensitive content is embedded inside seemingly innocuous text, and evaluation increasingly relies on large and varied test sets. Agents expand the surface area of risk exposure further as they have access to data systems. More often than not, personal data can be found in these systems with less predictability if organizations do not have visibility over their data.

As Indian enterprises scale AI initiatives, they require large volumes of data for supervised fine-tuning, testing, and iteration. However, many promising projects slow down because teams cannot safely share or use this data to make models reliable.

Is synthetic data the alternative enterprises can depend on?

Synthetic data is unfortunately not a panacea. Poorly generated synthetic datasets can still leak sensitive information if they preserve rare combinations of attributes or inadvertently mirror real examples too closely. Synthetic data can also fail in the opposite direction: if it is too “clean,” too generic, or too uniform, models trained on it can perform well in controlled tests but struggle in real-world deployments.

A more realistic framing is that synthetic data serves as a risk-reduction tool. When handled with discipline, it can reduce exposure to personal data while enabling model development and evaluation to move forward. It can also address a common practical constraint: many organizations do not have enough high-quality labeled training data to begin with, even before privacy considerations enter the picture.

Modern synthetic data generation has evolved beyond basic tabular testing datasets. Today, enterprises can generate synthetic instruction data, synthetic dialogs, synthetic incident tickets, and synthetic question-answer pairs that mirror the structure of real workflows without relying on raw records. This is especially relevant for the following AI development needs:

1. Supervised fine-tuning and domain adaptation
Enterprises often want models to operate in a domain-specific way. Using the organization’s terminology, policy rules, product catalog structure, and escalation logic. Fine-tuning can help, but the training examples needed are frequently sensitive. Synthetic datasets can provide safer prompt–response pairs that reflect real intent patterns and task formats, while reducing reliance on actual customer or employee data.

2. AI model evaluation at scale
The frequent bottleneck in enterprise AI programs is evaluation. Teams need to test models across many scenarios such as routine queries, edge cases, failure modes, and compliance-sensitive topics. Synthetic task generation helps build broad, repeatable evaluation suites faster than manual methods. If done well, it improves confidence in model behavior before production rollout and reduces the need to handle raw sensitive datasets during testing.

3. Custom data curation for RAG and agents
Retrieval-augmented generation (RAG) and agentic workflows depend heavily on the quality of knowledge bases and test prompts. Synthetic data can generate realistic queries, variations, and multi-turn interactions to stress-test retrieval and tool-use behavior. This reduces how often real, sensitive conversations need to be used as inputs.

Tools such as a Synthetic Data Studio reflect the shift toward operationalizing synthetic data generation as part of the AI lifecycle, supporting scalable synthetic dataset creation for fine-tuning, alignment, distillation, and custom data curation.

What makes synthetic data “privacy-safe” in practice

For synthetic data to mitigate privacy risk, it must be treated as an engineering discipline with controls rather than a last-minute workaround. To succeed, organizations first need to define whether the dataset is for training, evaluation, red-teaming, or system testing. Utility targets shape how data should be generated. There are other guardrails that need to be observed, such as:

• Employing data minimisation and generalizing granular data to remove unnecessary sensitive fields and outliers from the source data, and to reduce scope before generation begins.
• Assessing whether synthetic data preserves the patterns needed for model performance, not merely whether it looks realistic.
• Checking for memorization risk and the presence of overly unique or reconstructable examples.
• Documenting what was generated, its method, and intended use. This is important for governance and traceability, especially in regulated environments.

Synthetic data is not a universal replacement for real data, and it does not eliminate the need for governance. In practice, making synthetic data useful and safe is an operational challenge. Teams need an environment that can generate synthetic datasets at scale, tie them back to specific AI tasks (like fine-tuning or evaluation), and apply governance controls so outputs can be used confidently across the organization. Overall, the biggest value of synthetic data is its use of building traditional machine learning models in environments where data is scarce or unbalanced.

As enterprises expand LLM and agent deployments, synthetic data is becoming a practical path forward, reducing reliance on sensitive personal data. This underscores the need for a governed unified data and AI platform, enabling teams to operationalize synthetic data generation and validation as part of an end-to-end AI lifecycle, so innovation can move faster without risking privacy exposure.

The author is Mayank Baid, Regional Vice President, India & South Asia, Cloudera.

Disclaimer: The views expressed are solely of the author and ETCISO does not necessarily subscribe to it. ETCISO shall not be responsible for any damage caused to any person/organization directly or indirectly.

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