The fame of blockchain technology became profound with the advent of cryptocurrency. Understanding its functionality became a top priority for several tech experts, primarily because of its ability to maintain tight security and decentralized records of transactions. Blockchain boosts data integrity due to its ability to make data immutable, hence solving the issue of data security with AI technologies. This post focuses on changes brought to AI data security by blockchain. Let’s start with an understanding of how blockchain works.
Understanding BlockchainÂ
According to IBM, blockchain is a shared, immutable ledger providing immediate, shared and transparent encrypted data exchange to multiple parties as they initiate and complete transactions. Blockchain is unique because of its characteristics, which include
- Saving data in blocks linked together via cryptography.
- Immutability, which prevents the altering of stored information.
Blockchain shares similarities with a local database but differs from it in its mode of operations. It uses programs called scripts for database activities like accessing, entering and saving data on multiple machines because it operates a decentralized system. The validity of stored information depends on all stored copies matching each other.
Changes Brought to AI Data Security
Secure Data Sharing
AI users have shown concern regarding the possibility of data loss or integrity damage during the sharing process. The dependence of AI on data from multiple sources calls for regular sharing and security. Blockchain’s encryption and access control provide an extra layer of security by ensuring data privacy, security and integrity while enforcing strict access control, thereby reducing data breaches.
Authenticity
Transparency has been an issue with AI for several decades. Blockchain solved this by providing some insight into the framework behind AI and its data-handling process. Combining blockchain and AI enhances data security, which promotes data integrity and user trust. Blockchain-based identity management systems strengthen the authentication process for accessing AI-related data and services. By providing a secure and decentralized way to manage digital identities, blockchain helps verify the authenticity of users and devices accessing AI systems. It solidifies AI application’s security.
Smart Contracts for Transactions
Smart contracts are self-executing contracts with predefined rules and conditions written into code. They can automate and enforce agreements between parties, facilitating secure transactions in AI applications. For instance, smart contracts can ensure transparent and trustworthy transactions without intermediaries in AI-based marketplaces or data exchanges.
Data Integrity and Immutability
Blockchain’s decentralized and distributed ledger system ensures data remains tamper-proof and immutable. For AI applications handling sensitive data, such as healthcare or financial information, blockchain helps maintain datasets’ integrity. Once data is recorded in a block and added to the chain, altering or manipulating it without consensus from the network becomes challenging.
Reduced Single point of failure
Blockchain operates a decentralized system, contrary to traditional centralized systems susceptible to single-points-of-failure and hacking attacks. This architecture mitigates single-point-of-failure risks by distributing data across multiple nodes, making it more resilient to cyber threats.
Access Control
Users have yearned for more control over their data for over a decade. Blockchain solved the puzzle. It empowers individuals to have greater control over their data by enabling them to securely share specific portions of their data while retaining ownership rights. This concept of data sovereignty aligns with privacy regulations and gives users more autonomy over their data usage in AI systems.
Conclusion
While blockchain offers promising solutions for securing AI data and transactions, it’s not a one-size-fits-all solution. Implementing blockchain in AI systems requires careful consideration of scalability, interoperability, governance, and regulatory compliance. Additionally, integrating blockchain and AI technologies is an evolving field that requires updated knowledge and development to address its challenges and optimize its potential benefits.
