AI-Driven Decentralized Identity: Redefining Digital Identity Management

Our next post about the convergence of AI and blockchain technology focuses on the important issue of digital identity management. So far, we have covered the fundamentals of how AI models are built while exploring more expansive areas of AI risk mitigation protocols and how AI can enhance the interoperability of blockchains.

Throughout the series, we have established that data is the single most valuable and important resource on the planet. That is why it benefits us to further understand how our most sensitive data is managed. In order to do so, we need to also comprehend the most significant pain points in the current system, and how this convergence of AI and blockchain technology can change how we think about managing our digital identity.

Let’s begin by exploring the current state of identity management and the rise of centralized institutions (that are frequently exposed to hacks and leaks). We will then examine some of the novel blockchain solutions and privacy protocols that address these weaknesses before looking at how AI can further enhance the solutions.

The Importance of Digital Identification

In the digital age, we have become accustomed to freely divulging our most private information to third-party providers, whether we are disclosing our banking and credit card information to platforms and services or storing our social security numbers and medical records with workplace cybersecurity teams. Our digital Identities have proliferated across the internet, and while this may sound like we are decentralizing access, the reality is quite the opposite.

By disclosing sensitive information across multiple platforms, we have created a series of concentrated honey pots with single points of failure for malicious actors to exploit. Our most sensitive data has become only as secure as the most easily accessible platform that stores it.

The result of this concentration of our digital identities into a series of loosely connected single points of failure has been predictable. Extremely larger data breaches, hacks, and leaks across multiple industries are happening. The Marriot hotel chain had a breach in 2018 that leaked over 300 million customers’ names, addresses, passports, and dates of birth. In 2011, Sony was hacked with over 70 million of its game players’ IDs, payment information and passwords getting stolen. 2017 saw Equifax expose 147 million users, and Yahoo had the largest hack in history when it had over 3 billion accounts hacked in 2013-2014.  

Unfortunately, these examples represent only a few of the more recent attacks that have occurred. What is striking about them beyond the size of the breaches is the frequency and variety of industries that were impacted. 

With how valuable data is as an asset, this kind of continued onslaught of data breaches is a massive opportunity for bad actors to exploit, and as data becomes more valued in the era of AI, we can expect these large attacks to increase in frequency. For developers and everyone’s peace of mind, there needs to be a more secure alternative to protect our most sensitive data.

How Do These Hacks Occur?

In order to understand the extent of the problem, we first need to understand how breaches take place. It is not a straightforward answer, and while the third parties are single points of failure, there are numerous attack avenues that malicious actors can pursue. Most typically revolve around human engineering and exploiting the access from an employee to backdoor a way into a database.

While there are still exploitable weaknesses on the technical side of some of these centralized databases, the primary cause of most breaches is due to human error. Below is an example of how a socially engineered attack might occur.

Blockchain Innovation

Blockchain technology provides a unique set of qualities that makes it ideal for storing sensitive data such as digital identities. While the immutable nature of open distributed ledgers and block explorers may appear to be contradictory to the idea of privacy, blockchain innovations have proven incredibly effective in this goal.

In our earlier entry on AI-powered privacy solutions, we explored several novel privacy preserving tools and protocols like coinjoining, federated learning, and zero-knowledge protocols. Early iterations of these blockchain privacy-preserving protocols still relied on custodial intermediaries. Tumblers and mixers utilized a centralized wallet that would redistribute transactions with altered UTXOs. Early zero-knowledge protocols required the private data to be encrypted on the mainnet. Newer protocols are finding innovative ways to remove this custodial reliance through smart contracts, allowing users to remain in full control of their ID.

These solutions illustrate the importance of self-custody and demonstrate how smart contract engineering can allow users to maintain full control over their identity without disclosing exact details. Users utilizing blockchains for digital ID storage can effectively maintain control over their digital identity without disclosing the most sensitive aspects of data when confirming a proof of personhood transaction. 

This, in turn, reduces the overall threat of privacy breaches because each ID is then limited to the wallet that an individual holds rather than a concentrated honey pot. Human engineering attacks are reduced to the individual rather than the entire ecosystem.

‍Polygon’s Digital ID Model is an excellent example of how zero-knowledge protocols can be leveraged to confirm transactions without disclosing sensitive data while creating a new self-sovereignty-based identity framework. For more details on how these privacy protocols work, refer back to our previous blog post.

AI Enhancement

Building on top of these zero-knowledge protocols, AI can be used to introduce added layers of security through biometric and behavioral analysis. As we have seen in previous entries, AI is highly efficient at anomaly detection in risk management. Along with traditional digital identities, biometric fingerprint and retinal scan technology can be attached for further verification processes. This may become more of a requirement in the future as Generative AI deepfake attacks start to rise and physical verification is needed.

The same AI protocols used for anomaly detection in DeFi can also be leveraged for behavioral analysis in digital identification verification. Applying anomaly detection to digital identities can quickly highlight suspicious activity and be combined with biometric confirmation to create a more robust security protocol. This is especially important in healthcare and elections where creating verifiable proof of identity is required but revealing specifics of a person or an action that they have taken could put the person in a compromising position.

The Future of Digital ID Security and Management

We have tried to contextualize the significance of data in this new digital-first AI agentic world. What is apparent is the necessity for a more robust and secure form of digital identity management. Blockchain privacy-preserving protocols combined with AI technology are the key.