Artificial intelligence (AI) and machine learning are powerful technologies that are transforming industries, driving innovation and efficiency. However, they rely on data (often sensitive data) to learn and perform tasks effectively. This reliance raises significant data security and privacy concerns, and how best to leverage the power of AI and machine learning while safeguarding sensitive information. Enter tokenisation, a tool that can enhance data security in AI and machine learning applications without compromising efficiency.
AI and machine learning models rely on large datasets. The more relevant data they have, the better these models perform. This often includes personally identifiable information (PII), financial data, medical records, and other sensitive information. Directly using this data in training and inference exposes it to potential risks, such as data breaches, unauthorised access, and misuse. Traditional anonymisation techniques like pseudonymisation or aggregation may not be sufficient, especially with the increasing sophistication of data analysis techniques that can often re-identify supposedly anonymised data.
Enter tokenisation. It replaces sensitive data with non-sensitive substitutes called tokens. These tokens retain the format and length of the original data but have no intrinsic value or meaning outside of a secure tokenisation system. The actual sensitive data is stored securely in a separate, controlled environment, often a secure vault. This allows AI models to work with the tokens, effectively learning and making predictions without ever directly accessing the real sensitive data.
The tokenisation process typically involves data identification (identifying the sensitive data elements that need protection), tokenisation (replacing the sensitive data with corresponding tokens using a tokenization algorithm), secure storage (storing the original sensitive data securely, along with the mapping between tokens and the actual data) and detokenisation (retrieving the original sensitive data by replacing the tokens with their corresponding values when needed, usually only within a highly controlled and authorized environment). There are several tokenisation methods. With Format-Preserving Tokenisation, tokens maintain the format of the original data (e.g., a credit card number token still looks like a credit card number). This is crucial for many AI/ML applications that rely on data format. Cryptographic Tokenisation uses encryption techniques to generate tokens. Lastly, Random Tokenisation replaces sensitive data with randomly generated tokens.
The European Union (EU) has made accommodations for tokenisation in AI. The EU's General Data Protection Regulation (GDPR) requires that personal data be processed securely, including when it is tokenised. Tokenisation can help anonymise or pseudonymise data, which aligns with GDPR principles. Tokenisation may also play a role in the bloc’s proposed AI Act by ensuring that AI systems comply with the Act's requirements, particularly in high-risk applications like healthcare or finance, where data security and privacy are critical.
As AI continues to evolve and rely on increasingly complex datasets, tokenisation will play an even more critical role in ensuring data security and privacy. Coupled with robust key management and secure infrastructure, tokenisation can pave the way for building trustworthy and responsible AI and machine learning systems.
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