They turn the UI into part of the execution path, which means governance has to cover suggested actions, not just menu access. If an assistant can surface a workflow or trigger a task, teams need logging, approvals, and backend checks that apply before the action completes. Otherwise the identity control model lags the user experience.
Why This Matters for Security Teams
AI-assisted actions in Fiori change the control point from “can the user open the app?” to “can the system safely complete the action the assistant just proposed?” That shift matters because the UI is no longer a passive presentation layer. It becomes part of the execution path, which means access governance has to account for suggestions, prompts, approvals, and backend calls. Traditional access reviews are often too coarse for this model.
Current guidance from the NIST Cybersecurity Framework 2.0 and the OWASP Non-Human Identity Top 10 points toward stronger runtime controls, but there is no universal standard for AI-assisted enterprise UI flows yet. The practical issue is that a user may be authorised for a transaction while the assistant is not, or the assistant may surface a high-risk path the user would not normally navigate. NHIMG’s Top 10 NHI Issues shows how governance gaps often arise when identities are treated as static records rather than active execution actors.
In practice, many security teams encounter excess privilege only after an assistant has already accelerated a workflow that should have stayed blocked.
How It Works in Practice
Fiori governance needs to distinguish between display permission, recommendation permission, and execution permission. A user can be entitled to see a tile or receive a suggested action, but that does not automatically mean the assistant should be able to trigger the downstream SAP transaction or API call. The safer pattern is to treat the assistant as a separate workload identity with its own policy envelope, then evaluate each action at runtime before any state change occurs.
That means the control stack usually includes three layers:
- UI entitlements that decide what the person can view or request.
- Intent-aware policy that decides whether the assistant may surface or prepare the action.
- Backend checks that re-authorise the actual task, especially for posting, release, approval, or payment steps.
In this model, logging has to capture the full chain: who initiated the request, what the assistant recommended, what context it used, what was approved, and which backend service executed the action. This aligns with the direction of the NIST Cybersecurity Framework 2.0, which emphasises identity, access, and response as continuous functions rather than one-time grants. It also fits the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs, where short-lived credentials, rotation, and lifecycle controls are central to reducing exposure.
For SAP environments, current best practice is to keep the assistant out of direct privilege inheritance where possible and instead use policy-as-code, approval gates, and backend segregation of duties checks. If the assistant needs to call services, those credentials should be ephemeral and scoped to a single task, not reused across sessions. The governance target is not just least privilege, but least authority at the moment of action. These controls tend to break down when legacy Fiori extensions call shared service accounts because the assistant and the human user become indistinguishable in audit and enforcement.
Common Variations and Edge Cases
Tighter approval and runtime checks often increase friction, so organisations have to balance speed against control. That tradeoff is especially visible in Fiori deployments where some AI-assisted actions are low risk, such as drafting or searching, while others can create immediate financial or operational impact.
There is no universal standard for this yet, but current guidance suggests applying stricter treatment when the assistant can initiate changes in ERP, HR, procurement, or finance workflows. Read-only suggestion features usually need lighter controls than features that can submit, approve, or post records. The same is true when the assistant spans multiple systems, because one recommendation can cascade into several backend actions.
Edge cases often appear when organisations rely on role-based access alone. A role may be valid for the user, but the assistant may still be over-empowered if it inherits the role without task context. The 52 NHI Breaches Analysis reinforces a common pattern: compromise becomes easier when credentials or execution paths are broad, reusable, and poorly attributed. For that reason, access governance for AI-assisted Fiori should be reviewed alongside approval design, secret handling, and service-to-service trust boundaries, not as a separate UI concern.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A-03 | AI-assisted actions need runtime authorization before execution. |
| CSA MAESTRO | M1 | Separates assistant intent from human entitlements and execution rights. |
| NIST AI RMF | AI RMF supports governance for AI-driven decisions inside business workflows. |
Apply AI RMF governance to define ownership, oversight, and escalation for assistant actions.
