How AI agents securely access critical data in SAP environments

AI agents are autonomous software systems that plan, make decisions, and use external tools on their own. They interact with APIs, databases, and business applications – often without direct human supervision. In SAP environments, such agents can, for example, process invoices, trigger purchase orders, or analyze sales reports.

The danger: Many companies deploy AI agents without adjusting security mechanisms accordingly. An agent designed to summarize customer feedback is often granted access to the entire CRM system – including contracts, financial data, and personal information. This discrepancy between the authorizations granted and the access required poses a significant security risk. A compromised agent can become a gateway for extensive access to sensitive business data.

Key takeaways:

• AI agents in SAP environments require clear access controls and authorization concepts to protect data.

• The least-privilege principle limits the autonomy of AI agents to the minimum necessary for their tasks.

• Robust governance concepts enable the secure integration of AI agents.

• Segregation of duties (SoD) must also apply in the future to autonomous agents operating within critical business processes.

• A central identity registry for non-human identities creates transparency and enables audit-proof documentation.

What risks do AI agents pose in SAP environments?

Expanded attack surface

AI agents connect to various systems within the SAP landscape: APIs, databases, cloud services, and even other agents. Each of these connections has its own vulnerabilities. Attackers can manipulate the agent itself or target the tool to which it is connected. Unlike system accounts, which perform their assigned functions, AI agents can act unpredictably. This unpredictability complicates traditional security measures.

Autonomous actions at high speed

Agentic automation means that AI agents act without receiving explicit instructions from a human. They can connect with other agents, allowing them to work together. Each of these agent actions presents an attack opportunity – and a vector for escalation if the attacker succeeds in compromising an agent.

Prompt injection and manipulation

In a prompt injection attack, the attacker feeds the AI agent malicious input that causes it to behave in unintended ways. The agent can be instructed to ignore security policies, disclose data, or misuse tools. Particularly dangerous: Indirect prompt injection hides the malicious prompt within a data source that the agent accesses.

How does the least privilege principle work for AI agents?

The principle of least privilege is a proven security approach: Each system or agent is granted only the authorizations that are absolutely necessary for its tasks. For AI agents, this means consistently restricting access to defined datasets, folders, and functions. The goal is to define a clearly defined scope of responsibilities for each agent and limit its authorizations accordingly. This ensures that even a faulty or malicious agent can only access the data it needs for its specific function.

Instead of granting an agent blanket system access, companies should implement the following controls:

• Data record-specific authorizations: The agent sees only the tables and fields it is supposed to process.

• Time-limited access rights: Authorizations apply only for the duration of a specific task.

• Context-based authorization: Access depends on role, time, and task context.

Make your role and authorization structure AI-ready

Why are traditional IAM concepts insufficient for AI agents?

Identity & Access Management (IAM) was designed for human identities. However, AI agents typically operate using service accounts or inherited user logins – without the restrictions that apply to human users. In many organizations, non-human identities – such as AI agents, bots, and scripts – already outnumber human accounts. They are growing exponentially due to the increasing use of AI services. Traditional authorization concepts and processes are no longer sufficient for these dynamic, context-sensitive identities.

An AI agent that executes transactions in SAP systems should only be allowed to perform actions for which the triggering user is authorized. The agent must act within the user’s context and adopt the user’s access rights. This means that if a regional manager is only allowed to view sales figures for his region, the agent must also respect this restriction. It must not be induced to disclose information from other regions.

How do companies establish an AI governance framework?

A robust AI governance framework for SAP environments consists of four core components:

• Central identity registry for agents
  Companies need a complete overview of all AI agents that access corporate data. The registry records each agent’s intended use, connected systems, authorizations, and the individuals responsible for it. Without such a registry, IT and security teams do not know how many agents are active in the company, what data they access, or which external services they are connected to.

• Lifecycle management for agent identities
  Like human employees, AI agents must go through a defined lifecycle: creation, adaptation and deactivation. Key attributes such as intended use, model type, and training data form the basis for secure authorization.

• Segregation of duties for autonomous systems
  Segregation of duties (SoD) must also be consistently applied to autonomous agents. An agent that searches a product catalog should not be the same one that places orders. An agent that can only read catalog data is unable to accidentally place orders. Such SoD rules must be integrated into the authorization concepts for SAP environments. This creates a dynamic control system that encompasses all types of identities.

• Comprehensive audit trails
  Every instance of data access, download, transfer, and sharing by an AI agent must be logged. These audit trails support compliance documentation in accordance with legal requirements such as the GDPR, the EU AI Act, and industry-specific regulations.

What does the EU AI Act require of companies?

The EU AI Act will apply to high-risk systems starting in August 2026 and requires documented AI governance, data traceability, and human oversight. Companies face fines of up to 7% of their global annual revenue for violations. For SAP user companies, this means they must be able to demonstrate how their AI agents handle personal and business-critical data. A complete audit trail is essential for this.

The GDPR requires a documented legal basis for every processing of personal data – including by automated agents. Companies in regulated industries, such as financial services or healthcare, must meet additional requirements.

How are companies preparing their SAP architecture for AI agents?

In SAP landscapes that have evolved over time, identities, users, and roles are often scattered across different systems. Single sign-on works in some cases, but not comprehensively. What’s missing is a centralized view of identities across all systems. For SAP Joule and other AI services to function across systems, they need context regarding users, their roles, and their authorizations.

Characteristics of an AI-enabled authorization structure:

• SAP systems use the same identity across all connected applications.

• AI agents correctly identify users across systems.

• Roles, authorizations, and contexts are defined consistently.

• Artificial intelligence operates securely, contextually, and scalably.

What technical measures protect against agent abuse?

Zero-trust architecture for AI agents

The zero-trust approach assumes that no device and no agent is trustworthy by default. Every access request must be authenticated and authorized. Multi-factor authentication (MFA) and continuous monitoring help defend against threats.

Input validation and prompt hardening

Input validation involves checking prompts against predefined rules before they are forwarded to the AI agent. This approach protects against prompt injection attacks. Prompt hardening provides the agent with strict, limited instructions that leave little room for misinterpretation.

Micro segmentation of agent environments

Dividing networks into individual segments limits the freedom of movement of AI agents. If an agent is allowed to execute code, it should do so in a sandbox environment. Strict runtime controls prevent a compromised agent from moving laterally across the network.

What does a practical governance model look like?

A well-functioning governance model for AI agents in SAP landscapes includes the following elements:

• Agent registry
  Mandatory security checks before an agent is allowed to access sensitive data. The registry documents the agent’s purpose, data sources, authorizations, and responsibilities.

• Granular access controls
  Each agent receives only the minimum set of authorizations required for its specific task. Time-limited tokens are used instead of permanent access credentials.

• Data loss protection
  Policies block the transfer of sensitive data to external AI services without authorization. The origin of the data is fully documented.

• Human supervisory control
  Human approval is required for high-risk agent actions such as financial transactions or external communication.

Conclusion: Using AI agents securely and within compliance in SAP environments

The adoption of AI agents in SAP environments is accelerating. At the same time, regulatory requirements are increasing due to the EU AI Act and the GDPR. Companies that establish data-driven agent governance now enable AI innovation while protecting sensitive information. The first step is transparency. Companies must know how many AI agents are active in their environment and what data they can access. The second step is control. This is ensured by clear authorization models, least-privilege principles, and flawless audit trails.

Frequently Asked Questions (FAQ)

What are non-human identities, and why are they relevant?

Non-human identities are digital identities of AI agents, bots, scripts, or modules that interact with systems autonomously. In many organizations, their number already exceeds the number of human identities. IBsolution helps companies integrate non-human identities into existing governance structures.

How does access control for AI agents differ from traditional IAM?

Traditional Identity & Access Management (IAM) was designed for human users. AI agents, however, act autonomously and can exhibit unpredictable behavior. Therefore, it is necessary to extend existing IAM concepts to include agent-specific controls such as context-based authorization and time-limited access rights.

What happens if an AI agent is compromised?

A compromised AI agent assumes all the authorizations assigned to it. Attackers can steal data, trigger transactions, or move laterally within the network. The least privilege principle and microsegmentation limit the damage caused by compromised agents.

What role does the least-privilege principle play with AI agents?

The least-privilege principle restricts each agent to the minimum authorizations it needs to perform its task. For security reasons, this principle should be an integral part of SAP authorization concepts.

How do I prepare my SAP landscape for SAP Joule?

For SAP Joule to work across systems, the AI requires consistent information about users, roles, and authorizations. This depends on an SAP system landscape in which identity providers, trust relationships, and access models are properly aligned.