The author, Lasse Andresen, CEO of IndyKite, proposes the use of modern Identity and Access Management (IAM) to capture critical information about data. He believes that modern IAM, which connects disparate data silos and treats identity data as a growth enabler, is underutilized in today's tech stack. By adopting a graph-driven, modern identity approach, companies can leverage a unified identity fabric, complete with context, data provenance, and risk attributes for each entity. This approach can support digital trust standards and provide the necessary transparency and evidence of data veracity.
Key takeaways:
- The success of AI models is highly dependent on the quality, availability, and validity of the input data.
- Data provenance, which provides transparency into how data was collected and its journey through an organization, is crucial for enhancing the trustworthiness of AI applications.
- Modern Identity and Access Management (IAM) can be leveraged to provide data veracity, treating each application, digital entity, and data point as an 'identity' and capturing information to support digital trust standards.
- Starting with a flexible data model and creating a unified data layer enriched with provenance metadata and relationships can help drive intelligent access decisions, enhance application logic, and power AI applications.