No owner
Who is responsible for which AI application? Without clear ownership, governance gaps arise that only become visible during incidents or audits.
The problem
Agent Discovery & Governance
Which AI agents are already accessing your systems?
Most organisations cannot today say exactly which AI solutions are already in use.
New tools emerge where they are needed: in business units, projects and teams. Before governance can take hold, transparency is needed first. Our discovery workshop helps you gain that overview.
The decisive point
AI agents must be treated as privileged identities.
An AI agent can today change SAP entries, close tickets, create users or trigger workflows. It thus effectively becomes a privileged identity. And every privileged identity needs governance: an accountable owner, clear access rights, defined lifecycle processes and traceable documentation.
Companies have learned over the years to manage human identities. AI agents now raise similar questions in a new context. Who is responsible? What authorisations are really necessary? How are changes documented? And when is an agent decommissioned? For many existing processes and systems, these requirements are not yet provided for.
Key takeaway: Agent governance is not a future topic. It already starts where autonomous systems receive access, execute actions and influence business processes.
Why act now
The adoption of AI is currently often happening faster than existing processes can be adapted. New assistants, agents and automations create enormous value, but also bring new responsibilities. For human identities, most organisations have established processes. It is clear who gets access, who approves it and how entitlements are reviewed. For AI agents, these structures are often still missing. This is exactly where the risk arises.
The problem
You cannot control what you cannot see.
AI agents are increasingly becoming part of productive business processes. They create content, process data, trigger workflows or access existing enterprise systems. In many organisations, these use cases emerge where they are needed, often faster than governance structures can keep up.
As a result, a new group of digital identities is growing for which clear responsibilities, processes or control mechanisms often do not yet exist.
No lifecycle
Many agents are introduced for a specific use case and then operated permanently. Authorisations, API access and credentials remain in place even when processes change or the original purpose is no longer relevant. Without defined lifecycle processes, regular review is often missing.
No audit trail
What decisions were made? Which systems were used? What actions were triggered? As long as everything works, these questions often remain unanswered. They become relevant during audits, security incidents or compliance reviews.
Our Agent Discovery approach
Transparency before control .
Before responsibilities, policies or governance can be built, it must first be clear what actually exists.
That is why we start with a structured inventory of your environment. The goal is to create transparency and derive concrete next steps from it.
What already exists?
Together we create an overview of existing AI applications, agents, automations and integrations. We consider both technical systems and organisational responsibilities to get a realistic picture of your current situation.
Workshops · architecture inventory · IAM analysis
Where is action needed?
Not every solution carries the same risk. We evaluate existing agents and automations with regard to their access, responsibilities and importance for critical processes. This makes it visible which topics should be prioritised and where governance gaps exist.
Result: risk assessment & prioritisation
What comes next?
Based on the results of our Agent Discovery, we develop a pragmatic roadmap for the next steps. You receive a clear assessment of the most important action areas as well as concrete recommendations for governance, processes and organisational responsibilities.
Result: written recommendation
The result of Agent Discovery
What you take away
A solid basis for the next steps:
Transparency over existing agents
All identified AI agents and autonomous systems with their access rights and responsibilities.
Risk assessment
Classification by criticality and privilege level. What should be considered in the short term and what can follow later?
Identified action areas
Where are there organisational or technical gaps? We also consider requirements from NIS2, DORA and other regulatory frameworks, as far as they are relevant for your organisation.
Prioritised roadmap
Concrete recommendations for next steps based on your existing infrastructure and the results of the discovery.
What comes next
Discovery is only the start. Governance comes next.
Perhaps it is three. Perhaps thirty. Most companies do not know exactly, and this is where the problem arises: first you need an answer to what already exists. Our Agent Discovery creates the foundation for finding that out.
Transparency is only the first step: once agents are identified, the next question arises. Who is responsible, what authorisations do they have and how are they managed in the long term? This is exactly where Non-Human Identity Management begins.
- 01OwnershipEvery non-human identity needs an accountable owner, escalation path and review cycle.
- 02Access scopeDiscovered agents should be checked against least-privilege principles and existing approval paths.
- 03LifecycleProvisioning, changes and decommissioning must become part of the same governance model as human identities.