What is Stakeholder Identification?

Stakeholder Identification is how we connect the right people to the right AI systems in your inventory. Knowing what AI you have is only half the picture. You also need to know who owns it, who built it, who approved it, and who should be involved when decisions need to be made.

Every AI system in your organization has people connected to it. Someone built the model. Someone deployed it. Someone asked for it. Someone needs to review it. And someone is affected by its decisions.

When our platform flags a risk on an AI system, someone needs to own the fix. When a regulation requires documentation, someone needs to provide it. When a model needs to be retrained or retired, someone needs to make that call. If those questions do not have clear answers, things stall. Actions go unassigned. Risks sit there unaddressed.

Stakeholder Identification makes sure that does not happen. Every AI system in your inventory gets connected to the people who need to be involved.

The roles we track

In our platform, we identify and assign several stakeholder roles for each AI system. Here is what each one means:

System owner - The person or team ultimately accountable for this AI system. They make the big decisions - lifecycle changes, approvals, and ensuring the system meets governance standards
Technical owner - The data scientist, ML engineer, or team who actually built and maintains the system. They handle the technical side - running assessments, providing details, and implementing fixes when issues come up
Business owner - The person or team who requested the AI system and depends on its outputs. They define what success looks like and confirm the system is doing what it was built to do
Compliance and risk - Your governance, legal, or compliance team. They make sure the system meets regulatory requirements and internal policies
Reviewers and approvers - The people who need to sign off on governance activities like risk assessments, bias audits, or deployment approvals
Affected parties - The people or groups whose decisions, services, or experiences are influenced by what this AI system does. Understanding who is affected is essential for assessing risk and fairness

How we identify stakeholders

We do not rely on your teams to self report ownership. During our AI Discovery process, we capture metadata about who created, modified, or owns content in your connected sources. We use that information to suggest potential stakeholders for each AI system automatically.

Here is how it works:

1. During discovery, we capture ownership and contributor metadata from your connected sources

2. We use that data to suggest potential system owners, technical owners, and contributors for each Asset

3. Your governance team reviews these suggestions and confirms or adjusts the assignments

4. Stakeholder information gets stored on the Asset record, always visible and easy to update

5. When we trigger governance actions - assessments, reviews, follow ups - we route them to the right people based on their roles

You can always add, change, or remove stakeholder assignments as your teams evolve. The information stays current because it lives right on the Asset.

How this powers your governance workflows

Stakeholder Identification is not just a data field we fill in and forget about. It is wired into how our platform operates day to day.

Task routing - When an assessment needs to happen or a mitigation needs to be implemented, we know who to assign it to based on their role on that Asset
Notifications - Stakeholders get alerted when actions are needed on their AI systems. Escalation paths are clear when tasks are overdue
Review and signoff - Governance approvals get routed to the right approvers automatically. No manual chasing required
Accountability - We maintain a clear record of who did what and when, linking every governance action to a specific person. Your audit trail is built in

Why regulators care about this

Most regulatory frameworks - the EU AI Act, NIST AI RMF, ISO 42001 - require organizations to show clear ownership and accountability for their AI systems. It is not just good practice. It is a compliance requirement. Having stakeholders identified and linked to each Asset in your inventory means you are ready when regulators or auditors come asking. Learn more about regulatory alignment with our platform.

How we make it easier

Most tools treat stakeholder management as a manual task. You fill in a form, hope it stays current, and spend time chasing people when it does not. We take a different approach. We use evidence from the discovery process to suggest stakeholders automatically, reduce the manual work of keeping ownership up to date, and connect stakeholder data directly into our governance workflows. The result is that the right people are always involved at the right time, without your governance team having to play traffic controller. Learn more about how our platform works.