AIdentity

Trust on the Internet has been steadily eroding and generative AI is now pouring petrol on the fire. It gives attackers tools to quickly create deep fakes so realistic they can even fool CFOs and cybersecurity professionals.

AI-based apps are starting to manifest as living agents - autonomous software entities that perceive their environment, make decisions, and take actions to achieve specific goals. These agents will become increasingly prevalent, offering both helpful assistance and, unfortunately, new avenues for criminal or abusive activities.

decentralized web

To navigate this landscape safely, both individuals and service providers can leverage the building blocks outlined above in conjunction with smart contracts and account abstraction. By using DIDs and VCs to confer trust to AI agents, we create a framework for secure and accountable AI interactions.

This approach offers several benefits:

  1. Verifiable interactions: Each agent’s actions can be tied to a specific, trusted identity.
  2. Granular permissions: Smart contracts can define precise rules for what an AI agent can and cannot do.
  3. Auditability: All interactions can be recorded using DWN-based tamper-resistant records.

Contextual Awareness

AI agents can also benefit from the decentralized web’s ability to store and share data in a contextual manner. By leveraging DWNs to store and retrieve data, agents have the means to persist information across steps when undertaking complex, multi-step workflows.

Additionally, agents have standards-based access to users’ personal information in order to make informed decisions without compromising privacy.

Reliable Data

The need for reliable, permissioned AI data sources is a contentious issue, with ongoing debates about data origin, rights, and usage.

The application of DWNs and DIDs at the data source level offers a solution:

  1. Provenance: DIDs can prove the origin of data, ensuring its authenticity.
  2. Rights management: Smart contracts associated with DIDs can encode usage rights and permissions.
  3. Verifiable value: The worth and reliability of data can be more accurately assessed when its source is known.
  4. Compliance: This approach helps in adhering to data protection regulations by providing clear trails of data usage and consent.
  5. Incentivization: A DID-based system could allow for fair compensation to data providers, encouraging the creation of high-quality datasets.

The building blocks of decentralized identity can be used to create a more transparent, ethical, and efficient ecosystem for AI data. This not only resolves many of the current data rights issues but also lays the groundwork for more advanced and trustworthy AI systems in the future.