Artificial Intelligence heavily relies on real data which is regulated extensively by governments globally. Strict regulations around data collection, use, storage, and sharing make it impossible for fast-growing organizations to train their AI systems effectively, correctly, and on time. Furthermore, data breaches due to data misuse and improper data protection processes have exponentially increased. A report by IBM states that the cost of data breaches in 2023 was 4.45 million dollars globally, a 2.25% increase from 2022.
Lawsuits are already being brought against GenAI companies with concerns that the data being used to train machine learning algorithms is either infringing on copyrights, scraping, or misuse of data without consent.
As a result, organizations are now looking at alternative data acquisition and usage methods to protect themselves from legal action without limiting any advancements in the development of artificial intelligence. A leading solution to these challenges is Synthetic Data because of its use cases in both data privacy protection and. AI/ML development
Synthetic data refers to artificial data generated by algorithms that mimic the statistical properties of real-world data without containing any personally identifiable information. As organizations, startups and businesses across the globe invest in innovative AI/ML technologies, the demand for access and use of data increases. Therefore data protection and regulatory agencies globally are now implementing strict data privacy laws.
The Personal Data Protection Act (PDPA) in Singapore, the GDPR in Europe, and other data privacy and protection bodies that govern the use of personal data have become active in protecting data. Since Synthetic data is artificially generated to mimic real data it has no Personally Identifiable Indicators (PII) which offers a solution to leverage increasing amounts of data while adhering to data privacy and protection guidelines, allowing organizations to collect, share and use data freely.
One of the biggest challenges in AI development is the availability of quality data. In regions like Southeast Asia, where data collection can be fragmented, synthetic data provides an efficient solution. AI models can be trained to generate additional data from a limited dataset, bypassing the limitations of data scarcity. Therefore organizations can customize data depending on their Machine Learning requirements.
Training AI models require vast datasets, which can be expensive and time-consuming to collect and process. Synthetic data reduces this burden by offering a cost-effective alternative. It enables organizations, especially startups and SMEs in regions like Singapore, to develop and refine AI models without the need for extensive data collection campaigns. Synthetic Data offers intelligent data rebalancing, which includes eliminating biases and correcting imbalances. By removing biases, your business can employ fair and transparent AI models. Additionally, correcting imbalances enhances the performance of AI models which is why synthetic data is becoming the first choice of data scientists and privacy teams.
AI systems must be rigorously tested in diverse scenarios, which can be challenging with limited real-world data. Synthetic data creates safe, controlled environments for testing AI models, ensuring they are robust and reliable before deployment. This is particularly relevant in sensitive fields like healthcare and finance.
For Instance in the banking industry typically a transaction dataset has 98-99% non-fraudulent transactions and 0.1-2% fraudulent transactions. Due to this huge imbalance in the data set the ML models to detect fraudulent transactions have a very high rate of false positives. Synthetic Data can be used to balance out the dataset by generating additional data for rare scenarios improving accuracy and decreasing costs.
Synthetic data not only protects privacy but also spurs innovation. In academic and research institutions across Singapore and the region, synthetic data is becoming a key tool. It allows researchers to explore new AI applications without the constraints of data privacy concerns.
This is particularly true in scenarios like third-party software testing or collaborative product development with other companies and research institutions. To mitigate these challenges, replacing sensitive data with synthetic data can streamline the process. This approach can cut down the time and costs associated with risk assessments by up to 70%.
