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Evaluate an SAP AI feature as a complete workflow, not as a chatbot. Before a pilot can pass, you should be able to show which user identity is carried to every retrieval and tool call, what data leaves each system and region, how prompts and memory are retained, how the feature behaves with stale or hostile context, who approves consequential actions, and how every result is audited and reversed. An AI interface does not grant rights that the user lacks in SAP. Treat read, recommendation, drafting and transaction execution as separate risk levels, then approve only the smallest scope that meets measured controls.
What should an SAP AI evaluation prove?
Your evaluation should produce evidence that the feature stays within a declared business, technical and regulatory boundary under normal and adverse conditions. Write the boundary before testing: business outcome, SAP and non-SAP systems, data classes, jurisdictions, user populations, tools or APIs, and whether the feature retrieves, summarizes, recommends, drafts or commits a transaction.
Classify the decision impact at the same time. Employment, financial, health, customer, identity and other sensitive contexts need stronger oversight than a low-impact search assistant. SAP describes risk classification for AI use cases and expects additional controls for high-risk cases.
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| Feature behavior | Typical example | Minimum evaluation emphasis |
|---|---|---|
| Retrieve or summarize | Answer a question from an authorized purchase-order set | Permission filtering, source quality, leakage and retention |
| Recommend | Suggest a supplier or payment exception | Evidence, bias or error analysis, explanation and human review |
| Draft | Prepare an employee or customer message | Recipient scope, sensitive-data handling, approval and recordkeeping |
| Execute | Post, release, change master data or trigger a payment | Separate execution permission, confirmation, segregation of duties, rollback and complete audit trail |
Do not let a successful answer in a sandbox stand in for workflow evidence. The same feature can be low risk when it summarizes public policy and high risk when it proposes a personnel action or writes to financial records.
#1 Best Overall
How do I map identity and least privilege before testing?
Verify authentication, role checks, tenant boundaries, principal propagation and tool-level permissions before the first retrieval. The control must be enforced by the systems performing the retrieval or write, not merely described in a prompt.
- Identify the human principal. Record how the user signs in, which SAP tenant and business roles apply, and whether a service account is involved.
- Trace propagation. Follow that identity from the user interface to the orchestration layer, retrieval service, SAP APIs, HANA sources and every external connector. Confirm that the original principal, not a broad technical account, is used for authorization decisions.
- Test a denied case. Give a test user access to one record and explicitly deny a similar record. Ask for both. The system should return only the permitted record and should not reveal that the denied record exists through snippets, counts, citations, error text or generated summaries.
- Test cross-tenant and cross-company boundaries. Use users and records from separate tenants, legal entities, plants or personnel areas. Look for accidental joins in retrieval indexes, caches, conversation memory and logs.
- Check each tool independently. A user may be allowed to read an invoice but not release it. Verify read, create, change, approve and execute permissions for every API operation.
SAP Learning states that every agent action carries the user’s identity and access permissions, so an agent can act only on what that user is entitled to do. Treat that as a design requirement to verify in your tenant, not as a reason to skip negative testing.
Separate capabilities instead of relying on one role
Create distinct permissions for retrieval, recommendation, drafting and execution. A useful pattern is a read-only agent that can prepare a proposed change, followed by a controlled approval service that performs the write only after an authorized person confirms the exact object, values and target system.
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What data enters prompts, memory, models and logs?
Draw a data-flow diagram for one representative interaction. Include the user message, system instructions, retrieved documents, embeddings, conversation memory, tool arguments, model provider, region, subprocessors, telemetry, audit records, backups and deletion path. Record data classification and retention for each hop.
Minimize before you redact
- Remove personal data, credentials, secrets, session tokens and confidential content that the task does not require.
- Use field-level masking or tokenization for identifiers, amounts or health details where exact values are unnecessary.
- Keep retrieval indexes and embeddings inside approved tenants and regions; confirm whether deletion removes both source records and derived vectors.
- Set memory to off or short-lived for workflows that do not need continuity. Define who can inspect conversation history.
- Review prompts, tool payloads and error messages because sensitive data often leaks through diagnostics rather than the visible answer.
SAP Help Portal guidance says, “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.” If an approved design must process sensitive content, document the lawful basis, protection, retention and access controls before enabling it.
Rank #2
Ask where the model and subprocessors operate
Document provider, model and version, processing region, cross-border transfers, subprocessors and contractual terms. A feature that is acceptable in one jurisdiction may require a different deployment or additional safeguards elsewhere. Recheck these facts whenever a provider, model, connector or region changes.
How should I test grounding, accuracy and prompt-injection resistance?
Build a test set from real workflow patterns, with permission-sensitive and deliberately hostile cases. Evaluate the complete path from retrieval to final business result rather than grading a model response in isolation.
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- Missing context: The required record is absent. The assistant should say it cannot verify the answer instead of inventing one.
- Conflicting context: Two records disagree. The output should identify the conflict and defer to the authoritative source or a human.
- Stale context: An old policy or superseded master-data value is retrieved. Check freshness indicators and refusal or escalation behavior.
- Malicious context: A document contains instructions such as “ignore previous rules” or asks the agent to disclose secrets. The instruction must remain untrusted content.
- Unauthorized context: A relevant-looking document is outside the user’s scope. It must not influence the answer or appear in evidence.
- Tool abuse: A prompt attempts to invoke a write operation, change an amount or bypass an approval. The agent should refuse or route to the approval gate.
Measure more than factual accuracy
| Measure | What to record | Failure response |
|---|---|---|
| Grounding | Whether claims are supported by permitted, current sources | Require citations, confidence limits or human review |
| Refusal | Behavior when data, authority or evidence is missing | Block completion and provide a safe escalation path |
| Leakage | Unauthorized fields, records, prompts, memory or secrets in output | Fix retrieval filters, masking and output controls before retest |
| Prompt-injection resistance | Whether untrusted text can alter policy or tool use | Isolate instructions, validate tools and reject unsafe calls |
| Harmful action rate | Incorrect or unauthorized writes, messages or approvals | Reduce scope, add confirmation and require rollback evidence |
SAP architecture guidance describes grounding with HANA Vector Engine and knowledge-graph context, as well as prompt-injection detection and output filtering. Those components help, but they do not replace tenant-specific adversarial tests.
Where must a human approve an AI-assisted SAP action?
Require accountable human approval for payments, postings, employee decisions, customer communications, master-data changes, access changes and other irreversible, regulated or reputation-sensitive actions. The reviewer must see the proposed action, evidence, affected object, identity and authorization scope before approving.
Design an explicit approval gate
- Have the AI produce a proposal or draft, not an immediate write.
- Display source records, assumptions, uncertainty and any policy conflicts.
- Require a named approver with the appropriate SAP role and segregation-of-duties status.
- Capture the approver’s decision, timestamp, comments and the exact payload approved.
- Execute only that approved payload; reject changes made after approval and request a new review.
- Provide cancellation or compensating-transaction procedures for errors.
For low-impact read-only assistance, automated responses may be reasonable after testing. As decision impact rises, shorten the autonomy window, narrow tool permissions and increase review frequency.
What must an audit trail contain?
An auditor should be able to reconstruct who asked, what the agent saw, what it did and what business result followed. Capture, subject to approved privacy controls:
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- Prompt or request metadata, policy and workflow version
- Retrieved source identifiers, versions, timestamps and permission decisions
- Model, agent, prompt-template and connector versions
- Every tool call, arguments, response, approval and exception
- Final output, final business transaction identifier and rollback or correction
- Retention, deletion, access-review and export events
Protect logs from unauthorized readers and tampering. Verify that redaction does not destroy the linkage needed for an investigation. Confirm that deletion requests remove applicable prompts, memory, telemetry, indexes and backups, not just the user-facing conversation.
Know the documented SAP retention behavior
SAP AI Launchpad application logs are documented as persisted in ELK Logstash for seven days, while audit-log data is retained for 30 days before deletion, according to current SAP Help Portal security documentation accessed in 2026. Treat these as documented service behaviors for that product and configuration; confirm your tenant’s settings and export requirements before relying on them for a longer legal or internal retention period.
The SAP AI Core Security Guide was published on May 20, 2026. Use the current guidance for your deployed version, and record any configuration that changes default logging, retention or deletion.
How do privacy and AI regulations divide responsibility?
Map the use case to GDPR, the EU AI Act, sector rules, contracts and internal policy. Identify the controller or business owner, lawful basis, purpose limitation, data-subject rights process, records of processing, transfer mechanism and incident route.
Rank #4
SAP provides platform features and attestations, but customers remain responsible for case-specific data-protection and privacy obligations, including consent and deletion decisions, in applications built with AI services. Assign an owner who can approve the purpose, data classes, retention and user notice; do not assume a platform certification answers those questions for your process.
Create a compliance evidence pack
- Use-case description and risk classification
- Data-flow and subprocessor inventory
- Role, permission and segregation-of-duties design
- Test cases, results, thresholds and unresolved exceptions
- Human-approval and incident-response procedures
- Retention, deletion, access-review and regional evidence
- Change-control triggers and next review date
How do I run a bounded SAP AI pilot and make a go/no-go decision?
Start with a narrow workflow, synthetic or minimized data and read-only access. Define acceptance thresholds before anyone sees results, then expand only when evidence meets them.
- Set scope. Name the process, systems, roles, data, region, model, connectors and prohibited actions.
- Define thresholds. Specify maximum unauthorized-disclosure and harmful-action rates, required grounding coverage, refusal behavior, response latency and audit completeness.
- Prepare controls. Configure least privilege, tenant isolation, masking, retention, approval gates, monitoring and a kill switch.
- Run adverse tests. Include denied users, stale and conflicting records, prompt injection, malformed tool arguments and service outages.
- Observe operations. Assign owners for alerts, access reviews, incident export, model changes and user feedback.
- Review changes. Trigger reassessment when the model, prompt, connector, role, data source, region or regulation changes.
- Decide. Approve, restrict, remediate or stop. Record the decision, evidence, exceptions, compensating controls and re-review date.
Use a stop condition, not optimism
Stop the pilot if an unauthorized record is retrieved, a write bypasses approval, logs cannot link an action to a principal, deletion cannot be demonstrated, or a prompt-injection test changes tool behavior. Restore the previous manual process or read-only mode while the defect is investigated.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should two SAP AI features be compared?
Score each candidate against the same workflow and evidence requirements. A polished demo should not outrank a less flashy feature with stronger authorization, audit and rollback controls.
| Comparison axis | Questions to answer |
|---|---|
| Business value and impact | What outcome improves, and what is the consequence of an error? |
| Authority | Is the feature read-only, recommendatory, drafting or able to execute? |
| Identity | Does principal propagation enforce the user’s SAP roles at every tool? |
| Data and residency | What sensitive data, regions, providers and subprocessors are involved? |
| Grounding | Can users inspect permitted, current evidence for each material claim? |
| Security testing | How does it handle injection, leakage, stale data and denied access? |
| Human control | Are high-impact actions gated, reviewable and reversible? |
| Auditability | Are identity, sources, versions, calls, approvals and outcomes retained? |
| Operations | Who monitors it, changes it, exports incidents and operates the kill switch? |
| Compliance effort | What notices, contracts, deletion processes and evidence must your organization maintain? |
Choose the feature that satisfies the non-negotiable controls first. Optimize convenience and coverage only after authorization, privacy, human oversight and auditability pass.
What is the practical pre-production checklist?
- Workflow, decision impact, data classes, jurisdictions and prohibited actions are documented.
- Authentication, principal propagation, role checks, tenant boundaries and tool permissions passed positive and negative tests.
- Prompts, retrieval, embeddings, memory, telemetry, providers, regions and subprocessors are inventoried.
- Personal data, credentials and confidential information are removed or protected under an approved design.
- Grounding, source freshness, refusal, injection, leakage and harmful-action thresholds are met.
- Material actions require named human approval with segregation-of-duties checks.
- Logs link user, agent, sources, versions, calls, approvals and final transaction, with tested retention and deletion.
- Privacy, regulatory, contractual and internal-policy owners have signed off.
- Monitoring, rollback, kill switch, incident export and change-control triggers are operational.
- A bounded pilot has a written go/no-go decision and re-review date.
Frequently Asked Questions
Can an SAP AI assistant see data hidden from the user?
It should not, when authorization is enforced at retrieval and tool layers with the user’s propagated identity. Test denied records explicitly, including indirect leakage through counts, snippets, citations, caches and conversation memory; a natural-language instruction alone is not an access control.
Should prompts be stored for troubleshooting?
Only under a documented retention and access design that removes or protects personal, credential and confidential content. Prefer metadata, redacted samples and short retention, and verify that deletion covers logs, memory, indexes and backups where applicable.
What evidence will an internal auditor usually request?
Expect the approved use-case scope, role and data-flow diagrams, adversarial test results, model and connector versions, approval records, retention settings, incident procedures and samples showing that a business transaction links back to a user and authorization decision.
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When should an SAP AI pilot be re-approved?
Re-review it after a model, prompt template, connector, role design, data source, processing region or applicable regulation changes. Also trigger review after an incident, a failed monitoring threshold or a material change in the workflow’s decision impact.
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