How does AI governance work for AI agents in SAP?

AI agents independently access business data, execute processes, and make operational decisions in SAP systems. This fundamentally changes the requirements for effective access controls. AI governance defines the rules, authorizations, and guidelines that ensure AI agents operate only within a strictly defined framework. This article explains how AI governance for AI agents works in practice within the SAP environment, what risks arise from a missing or incomplete authorization concept, and what measures companies should implement.

Key takeaways:

• AI governance determines which AI agents are permitted to access which SAP data, functions, and business processes, and which actions are allowed.

• Without a structured authorization framework – including for non-human identities – companies risk data leakage, compliance violations, and excessive autonomy on the part of their deployed AI agents.

• The principle of least privilege applies to AI agents just as it does to human users and should be applied to every agent profile.

• IBsolution helps companies build or expand their role and authorization structures specifically for the use of AI agents.

• Audit trails and traceable decision paths are essential components of robust, audit-proof AI governance for AI agents in the SAP environment.

Lay the foundation for your AI agent governance

What does AI governance mean?

AI governance refers to the set of guidelines, authorizations, and control mechanisms that govern the use of AI agents in SAP system landscapes. Unlike traditional chatbots, agent-based AI operates autonomously: The agents read master data, initiate ordering processes, or analyze financial data.

For SAP environments, this means that each agent requires its own authorization profile, which defines which transactions, data fields, and system functions it is permitted to access. Without such a profile, there is a risk that an agent will access data that falls outside the scope of its task. AI governance ensures that agents do only what they are supposed to do – and nothing more.

Why do AI agents need their own authorization concept?

In traditional SAP scenarios, a human logs into the system and works within the scope of their assigned role. An AI agent operates differently: It acts without direct human supervision and systematically accesses everything it needs to complete its task. The AI agent is capable of utilizing all available systems and data sources in a very short amount of time.

The German Federal Office for Information Security (BSI) points out that AI agents require the same authorizations as the employees whose tasks they are intended to perform. In cases of doubt, this also means that AI agents must be granted access to sensitive data and applications so that they can carry out their tasks. At the same time, the BSI publication explicitly emphasizes that this significantly increases the risk profile compared to conventional chatbots. Without a dedicated authorization concept, agents may have or be granted more rights than are necessary for their tasks. This phenomenon is classified as “excessive agency” and encompasses excessive functionality, excessive authorizations, and excessive autonomy. It can arise simply because the authorization model from which access rights are derived historically provides for too many authorizations because they have not been used in the company to date and were therefore not visible.

What risks arise in an SAP environment without AI governance?

A lack of governance for AI agents poses several risks simultaneously. At the data level, agents can access information that falls outside their scope of responsibility, such as HR data, financial metrics, or customer master records. At the process level, agents can trigger actions that are not intended, such as order approvals or changes to master data. At the compliance level, the absence of audit trails makes it impossible to trace who initiated an action.

These risks are exacerbated in hybrid system landscapes where on-premises systems and cloud solutions are operated in parallel. This is because each environment has its own access mechanisms and role models that must be taken into account in AI governance.

Three levels of effective AI governance

The recommendation for companies is to structure their AI governance across three levels: strategic, tactical, and operational.

Strategic level: Define the governance framework

The strategic level addresses the overarching question of in which areas of the company AI agents may be deployed at all and how they are embedded into the existing Identity Governance & Administration (IGA). This includes determining which agents are authorized, which data classes they are permitted to process, and who bears responsibility for their actions.

A governance framework forms the foundation for all subsequent technical and organizational measures. It should comply with regulatory requirements such as the GDPR and the EU AI Act to avoid any legal vulnerabilities.

Tactical level: Defining authorizations and roles

At the tactical level, specific authorization profiles are created for each AI agent. The principle of least privilege is central here: Each agent is granted only the rights it needs to perform its specific task. IBsolution supports SAP user companies in designing such authorization concepts that cover both on-premises systems and cloud environments.

Operational level: Setting up monitoring and audit trails

At the operational level, the focus is on ongoing monitoring and adherence to defined guidelines. Audit trails document which actions an AI agent has performed, what data it has access to, and what decisions it has made. Companies should pay close attention to this aspect, take this “audit function” seriously, and invest in the associated effort. In addition, they should set up automated monitoring routines that detect deviations from the defined authorization profile. This allows for early detection of whether an AI agent is operating outside its intended scope. Monitoring and auditing are the absolutely essential forms of proactive feedback for authorization management, which continuously addresses the application and the constantly changing volume of data.

How SAP AI Agent Hub supports AI governance

With SAP AI Agent Hub, SAP has created a central platform that enables companies to identify, inventory, and manage their AI agents. SAP AI Agent Hub provides a vendor-neutral overview of all agents in use, their dependencies, and their business context.

Key features include linking agents to their architecture and business context, ensuring analyzability through telemetry data, and enforcing governance and compliance rules at runtime. For companies using SAP Business AI, the SAP AI Agent Hub is an essential tool for maintaining an overview of the growing agent landscape and centrally enforcing AI governance.

What role does IGA play for AI agents?

Identity Governance & Administration forms the foundation of all AI governance in the SAP environment. A robust IGA ensures that every AI agent has a defined identity, is authenticated, and acts only within the scope of its role. In practice, this means that companies must manage non-human identities just as they do user accounts. This is because AI agents can only operate securely and at scale if their access rights are consistently maintained and regularly reviewed.

Conclusion: Establish AI governance now

Companies that use AI agents in an SAP environment or plan to do so should make establishing AI governance a high priority early on. Those who wait until after the rollout to monitor agents risk uncontrolled growth and allow authorization structures to develop that will require significant effort to correct later on.

IBsolution supports companies in the structured development of AI governance – from analyzing the existing authorization landscape to defining agent roles and implementing monitoring mechanisms. The foundation for this is a robust authorization concept that covers both current and future requirements.

FAQs about AI governance for AI agents in SAP

What is AI governance in the SAP environment?

AI governance in the SAP environment refers to the set of guidelines, authorizations, and control mechanisms that govern the use of AI agents. It defines which agents are permitted to access which data and functions. IBsolution supports companies in designing and implementing these governance structures.

Why do AI agents need their own authorizations?

AI agents operate autonomously and access SAP systems without direct human supervision. Without their own authorization profiles, they could potentially gain access to data and functions that fall outside the scope of their tasks.

Which SAP tools support AI governance?

The SAP Authorization Management Service (AMS), SAP Identity Access Governance (IAG), and the SAP AI Agent Hub are key tools for establishing AI governance. They manage authorizations, monitor agent activities, and enforce compliance rules.

What happens without AI governance?

Without governance, AI agents may be granted too many privileges. This can lead to unintended data access, compliance violations, and uncontrolled process changes. These risks can be avoided through structured authorization concepts and audit trails.

How do I start building an AI governance framework?

The first step is to make an inventory of the existing identities, roles, and authorizations in the SAP landscape. Building on this, you can define a governance framework that encompasses agent classes, authorization profiles, and monitoring mechanisms.