This guest post is by Sunil Soares, founder and CEO of YDC – AI Governance. Previously, he founded and led Information Asset, a data management firm. Sunil brings a deeply-researched perspective to AI governance—authoring 13 books that have shaped how enterprises approach data and AI at scale.
The YDC team developed an AI Governance prototype in Atlan. We reused the existing operating model with assets and added custom attributes and relations.
AI Use Cases
As discussed in an earlier blog, a digital twin may be a virtual replica of a particular patient that reflects the unique genetic makeup of the patient or a simulated three-dimensional model that exhibits the characteristics of a patient’s heart. Digital twins may be utilized to accelerate clinical trials and reduce costs in the life sciences industry. The YDC team implemented an overview of the Digital Twins for Clinical Trials AI Use Case in Atlan.
AI Risk Assessments
We conducted an AI Risk Assessment for the use case with Atlan. Digital twins have the potential to introduce bias risks based on the algorithms and the underlying data sets. We documented the bias risk assessment and a mapping to the associated regulations in Atlan.
We also documented the privacy risks in Atlan.
We documented other dimensions of AI risk including Reliability, Accountability, Explainability and Security in Atlan. For the sake of brevity, I have not included those screenshots here.
This use case would likely be classified as High Risk based on the Medical Device category of Article 6 of the EU AI Act.
AI Risk Assessment Workflows
We configured an AI Risk Assessment workflow in Atlan to route the AI Risk Assessment to the appropriate parties for approval.
The screenshot below shows the AI Risk Assessment in Approved status based on approvals from the Operational Risk Management Committee (ORMC) and the AI Governance Council.
Shadow AI Governance to Ingest Metadata from ServiceNow CMDB and YDC_AIGOV Agents on Hugging Face to Highlight COTS Apps with Embedded AI
In an earlier blog, I discussed Shadow AI Governance and the YDC_AIGOV agents. As part of the current exercise, we ingested metadata around the Commercial-off-the-Shelf (COTS) apps into Atlan. This information includes metadata such as Application Name, Privacy Policy URL, Data Specifically Excluded from AI Training, Embedded AI and Opt-Out Option.
The screenshot below shows Atlan before running the integration with the YDC_AIGOV agents. The catalog only contains one AI Use Case (Digital Twins for Clinical trials) and one application (Google Product Services).
After running the integration with Atlan API, Atlan contains a broader list of applications including Actimize Xceed including metadata in the right panel.
Conditional Logic with Atlan API to Auto-Create AI Use Case and AI Risk Assessment Objects
We implemented conditional logic in the Atlan API to auto-create AI use cases only for applications with embedded AI. In this case, we created an AI use case object in Atlan for Actimize Xceed because Embedded AI = “Yes.”
We also implemented conditional logic in the Atlan API to auto-create AI Risk Assessment objects where Data Specifically Excluded for AI Training = “No.” Obviously, this logic is configurable.
This is a basic AI Governance configuration in Atlan with more to come!
This post was originally published on Your Data Connect. Read the original article here.
