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Evaluate an SAP AI feature as part of the full workflow—not as a model demo. Before a pilot, document what the feature can read and change, verify that identity and permissions are enforced before retrieval or action, test its handling of sensitive and unreliable data, require human approval for material decisions, and prove that activity can be audited and controlled. SAP provides relevant security and governance features, but customers remain responsible for their particular privacy, consent, deletion, and compliance obligations.
What should an SAP AI evaluation prove?
An evaluation should show that the feature delivers a defined business benefit while staying within an approved boundary: the right people, data, systems, actions, locations, and retention rules. A convincing answer to a prompt in a demonstration is not enough. Risk can arise in the documents retrieved, the identity used by a tool, the agent’s ability to write to SAP, or the logs retained after a request.
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- Business outcome: What task should become faster, clearer, or less error-prone? How will a pilot determine whether it helped?
- Systems and interfaces: Which SAP systems, connected services, documents, APIs, and external tools are involved?
- Data: Which personal, financial, employee, health, customer, credential, or confidential data could appear in prompts, retrieved content, memory, or logs?
- People and jurisdictions: Which users and affected individuals are involved, and where are they and their data located?
- Decision and action: Does the feature retrieve, summarize, recommend, draft, or execute? Can it cause a payment, posting, record change, or external communication?
Classify the use case before testing. SAP’s governance materials describe risk classification and stronger oversight and controls for high-risk cases. The classification depends on the actual use and its context; the fact that a feature is described as an assistant does not determine its risk category.
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How do I check that AI respects SAP permissions?
Confirm authorization at every point where data is retrieved or an action is taken. An AI interface should not become a shortcut around existing SAP role checks, tenant boundaries, or tool permissions.
Map the identity chain from the person who starts the request through the AI feature and each connector or tool. Identify which identity each component uses, how user identity is propagated, and where authorization is checked. SAP’s workflow example puts identity and authorization checks before data distribution, and SAP Learning says that each agent action carries the user’s identity and access permissions. Treat those design statements as requirements to validate in your own configuration and workflow.
- List the principals. Record the end user, agent, service identities, connectors, and downstream tools that participate. Note when a service identity acts on a user’s behalf.
- Trace each permission check. For every retrieval and write operation, identify the system that checks authorization and the identity it checks. A permission check only at sign-in does not demonstrate that later tools enforce the right scope.
- Test users with different access. Use accounts with distinct roles and permissions. Ask for the same sensitive record from each, including through summaries and follow-up questions, and test whether a tool can retrieve or change a record the user cannot access.
- Test tenant and connector boundaries. Include cases where similar records exist in different business units or tenants, and verify that context from one boundary cannot appear in another.
- Test write permissions separately. A role that permits viewing or drafting should not silently gain the ability to post, approve, or change master data through an agent tool.
Record the expected result and actual result for each case. If the system cannot show which identity was used or where a permission decision occurred, treat that as a gap to resolve before expanding the pilot. A successful demonstration with an administrator account does not establish least-privilege behavior for ordinary users.
What data paths, prompts, and logs need review?
Inventory data throughout the feature’s lifecycle, not only what a user types. Review prompts and messages, retrieved documents, embeddings, memory, model providers, regions, subprocessors, telemetry, and application and audit logs. For each location, determine what is sent or stored, who can access it, how long it remains, and how deletion works.
SAP Help Portal warns, “Don’t store personal data in prompts when using the generative AI hub.” SAP developer guidance similarly says, “Do not include personal data, credentials, or confidential information in your messages.” Treat these as practical guardrails: minimize sensitive content, and do not assume that a prompt is temporary or private unless the specific service and configuration establish that.
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- Remove fields that the task does not need, especially credentials and unnecessary personal or confidential details.
- Check whether retrieved content or summaries can carry sensitive information into prompts, memory, traces, or outputs.
- Identify model providers, processing regions, subprocessors, and applicable service terms for every part of the flow.
- Determine whether logs capture prompt text, outputs, retrieved source identifiers, tool calls, or personal data, and restrict log access accordingly.
- Verify retention and deletion behavior for each data store instead of assuming that deleting a chat deletes its related logs or audit records.
One specific retention fact should not be generalized to every SAP AI service: SAP’s current security documentation, accessed in 2026, says SAP AI Launchpad application logs are persisted in ELK Logstash for 7 days, while audit-log data is retained for 30 days before deletion. Confirm that those periods apply to the service and setup in scope, and check whether your own application, connected systems, or policies require different controls.
How should I test grounding, accuracy, and prompt-injection resistance?
Test whether answers are supported by the information the user is entitled to see, and whether the feature behaves safely when that information is weak or hostile. SAP’s architecture guidance describes grounding with HANA Vector Engine and knowledge-graph context, as well as prompt-injection detection and output filtering. Those mechanisms are useful design controls to assess; their presence is not proof that a particular workflow is safe.
Build a test set from representative, permission-sensitive cases. Include ordinary requests, but also deliberately difficult conditions:
- Missing context: The relevant record or document is unavailable. Does the feature say it cannot establish an answer, or invent one?
- Conflicting context: Two accessible sources disagree. Does the output surface the conflict and identify its evidence, or present one answer as certain?
- Stale context: A record is outdated or superseded. Can the feature distinguish current information from old material?
- Unauthorized context: A useful-looking source exists but is outside the user’s access. Does it remain unavailable to retrieval and output?
- Malicious context: A document or message includes instructions aimed at changing the agent’s behavior. Does the feature treat that text as untrusted content rather than as authority to reveal data or take action?
- Unsafe request: A user asks for a sensitive disclosure or an action outside the feature’s approved scope. Does it refuse or route the request for appropriate review?
Assess each test against observable criteria: factual accuracy, source or evidence quality, correct handling of uncertainty, refusal behavior, data leakage, prompt-injection resistance, and harmful action rates. Keep a record of the input conditions, expected behavior, actual output, and any tool calls. A polished answer is not evidence of a correct answer; where the task matters, reviewers need to be able to inspect supporting evidence.
Which AI actions need human approval?
Separate permission to read from permission to recommend, draft, and execute. Put a named, accountable human approval gate in front of actions with material, irreversible, or regulated consequences, rather than expecting the model to decide when oversight is warranted.
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Define the permitted action level for each workflow and ensure the controls match it:
| Feature behavior | What the pilot should establish |
|---|---|
| Retrieve or summarize | Retrieved information is limited to the user’s authorized scope, and the answer can be checked against evidence. |
| Recommend | The recommendation is presented for review rather than treated as a completed business decision. |
| Draft | The draft is clearly distinguishable from an approved or sent communication, posting, or record change. |
| Execute | The tool has only the required authority, applicable approvals occur before execution, and the action and result are traceable. |
For payments, postings, employee decisions, customer communications, master-data changes, and other high-impact actions, define who reviews the proposal, what evidence they see, and how they approve or reject it. SAP’s governance materials describe human oversight for high-risk decisions and controls before execution. If a workflow has no reliable way to hold an action for review, reduce its authority or keep it out of the pilot.
How do I make an SAP AI workflow auditable?
An auditor or incident responder should be able to connect a business outcome to the user, the agent, the permissions in effect, the information used, the tools called, and any approval. Decide what evidence is necessary before launch, then verify that it is actually captured and can be retrieved by authorized reviewers.
For each workflow, check whether the audit trail or operational records can establish:
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- the user and agent identity, plus the authorization scope applied to each relevant action;
- request or prompt metadata, retrieved source identifiers, and the model and agent versions involved;
- which tools were called, what they did, and whether an action was proposed, approved, rejected, or completed;
- the output, exceptions, final business result, and any human review;
- who can access records, how long they are retained, how they can be exported for an incident, and how deletion is handled.
SAP documentation describes audit trails, verification records, telemetry, and explicit log-retention and deletion behavior. Confirm the exact records and retention periods for the services in your design rather than relying on a general product description. Logging also needs its own access and data-minimization review: an audit trail can help prove what happened, but it should not expose sensitive information to people who do not need it.
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How do I assess compliance responsibility?
Map the workflow to the obligations that apply to its actual use: privacy law such as GDPR where relevant, the EU AI Act where applicable, industry rules, contracts, and internal policy. Identify the responsible business and technical owners, the people affected, the purpose of processing, and the decisions that require documented oversight.
SAP’s product capabilities and attestations can inform that review, but they do not decide every customer’s legal obligations. SAP’s guidance states that customers remain responsible for case-specific data-protection and privacy obligations, including consent and deletion decisions. SAP AI Core’s Security Guide was published on 2026-05-20; use the applicable current service documentation and your organization’s legal and compliance review to assess the particular deployment.
Maintain an explicit responsibility map. It should name who approves the use case, who owns data access and retention, who reviews high-impact outputs, who monitors incidents, and who can suspend the feature. If a provider or subprocessor handles data, establish which contractual and regional terms apply to that processing rather than assuming the application’s location answers the question.
How should I run a bounded pilot and make a go/no-go decision?
Limit the initial pilot to a defined workflow, audience, data scope, and action level. Set acceptance criteria before testing so that favorable examples cannot substitute for evidence that the feature remains within its boundary under adverse conditions.
- Set measurable thresholds. Define acceptable behavior for evidence quality, refusals, permission checks, leakage, tool actions, approvals, and unresolved errors. Choose thresholds appropriate to the decision impact; do not invent a universal accuracy target for every workflow.
- Use controlled test identities and data. Include different permission profiles and representative difficult cases. Avoid unnecessary real personal or confidential information in test prompts.
- Assign operational owners. Name the people responsible for monitoring, approval queues, log review, incident response, and policy exceptions.
- Prepare containment. Establish how to stop the agent or disable its tools, revoke or reduce permissions, roll back a change, and handle any action already completed.
- Review change triggers. Require reassessment when the model, prompt, agent, connector, user roles, data path, region, or applicable regulation changes.
- Document the decision. Approve only when test evidence meets the thresholds and the owners accept the remaining risk. Otherwise, narrow the feature’s scope, remove write access, add an approval gate, or do not proceed.
Set a review date even if the pilot passes. A passing result applies to the tested configuration and scope, not automatically to future versions, additional users, or newly connected systems.
How do I compare two SAP AI features fairly?
Compare features against the same workflow and risk boundary, not just their answer quality or interface. Score each dimension using evidence from the intended configuration; mark an item as unknown when it has not been verified.
| Evaluation dimension | Evidence to compare |
|---|---|
| Business value and impact | Target outcome, decision consequences, and how benefit will be measured. |
| Authority | Whether the feature reads, recommends, drafts, or executes; the controls around write actions. |
| Identity and least privilege | User identity propagation, role checks, tenant boundaries, and tool-level authorization. |
| Data handling | Sensitivity, residency, prompt and memory paths, providers, subprocessors, and retention. |
| Output behavior | Grounding, evidence quality, uncertainty handling, refusal behavior, and resistance to malicious context. |
| Human control | Approval gates, accountable reviewers, and separation between drafts and executed actions. |
| Audit and operations | Identity-linked records, tool-call evidence, retention, incident export, monitoring, and rollback. |
| Compliance and effort | Regulatory classification, contractual terms, control ownership, and the work required to operate the feature safely. |
A feature that produces better answers in a demo may still be a worse fit if it requires broader permissions, exposes more data, or cannot produce adequate audit evidence. Prefer the option whose controls can be demonstrated in the actual workflow and maintained by named owners.
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Does SAP AI Core make my application GDPR-compliant automatically?
No. SAP’s AI Core Security Guide describes service security, but SAP says customers remain responsible for their own case-specific privacy obligations, including consent and deletion decisions. Applicability depends on your data, purpose, configuration, and jurisdiction.
Should I use production data to test an SAP AI pilot?
Use the least sensitive data that can test the workflow realistically, and avoid placing personal data, credentials, or confidential information in messages unless an approved design explicitly permits and protects it. Test role differences with controlled accounts and data wherever possible.
What should I do if an AI agent takes an unauthorized action?
Use the containment procedure defined for the pilot: stop or disable the agent or tool, restrict its permissions, preserve relevant records, and route the incident to the designated security and business owners. Review the identity, authorization check, tool call, and approval evidence before restoring access.
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