Concept card

AI Red-Teaming for SAP — Adversarial Prompt Testing

As of 2026-07-23

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is AI Red-Teaming for SAP — Adversarial Prompt Testing?

The deadline is not theoretical — the 2026-08-02 EU AI Act adversarial-robustness requirement applies to systems already in deployment, and indirect prompt injection via the Knowledge Graph is the vector that catches most teams off guard because the attacker never touches Joule directly.

What it is

AI red-teaming is the structured adversarial testing discipline applied to generative AI systems before and after production deployment — the equivalent of penetration testing for LLMs. For SAP analytics deployments running Joule, SAP Build generative AI flows, or custom AI Core extensions, red-teaming is not optional from mid-2026: EU AI Act Article 9(5) requires operators of high-risk AI systems to identify and document foreseeable misuse, and Article 15(4) introduces specific adversarial robustness requirements for systems exposed to manipulated inputs. The 2026-08-02 deadline applies to systems already in deployment.

Why it matters

Any unstructured text field in a replicated table (a vendor comment in EKKO, a free-text HR note) becomes an injection attack surface once the Knowledge Graph retrieves it
Privilege escalation via prompt cannot bypass DAC if DAC is correctly implemented at the Datasphere layer — red-teaming validates that layering, it doesn't replace it
The 2026-08-02 deadline applies retroactively to systems already in production, not just to new builds

Key points

EU AI Act Article 9(5) + Article 15(4) require adversarial robustness testing for high-risk AI systems — effective 2026-08-02 for deployed systems; red-teaming is the technical implementation.
Five SAP-specific attack vectors: prompt injection (including indirect via Knowledge Graph source records), privilege escalation, conversation memory extraction, system prompt leakage, confabulation amplification.
Indirect prompt injection via Knowledge Graph is the most underestimated vector — the attacker corrupts a free-text field in a replicated ERP table, never touching Joule directly.
DAC at Datasphere catalog is the authoritative row-level security control — the LLM must never be the sole gatekeeper for data access; privilege escalation via prompt cannot bypass correctly implemented DAC.
Four-phase methodology: threat modelling → automated probe library (200–400 prompts) → manual expert testing (4–8 hours) → remediation and re-test.
Critical findings (data exfiltration possible) block go-live; production cadence: full exercise pre-launch, quarterly automated probes, annual full re-test.
Cost of quarterly automated probe run: < €5 at economy model tier — the bottleneck is operational discipline, not cost.
AI Red-Teaming for SAP — Adversarial Prompt Testing is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.

Terms used on this page

Prompt injection: Attack where adversarial instructions are embedded in user input or retrieved context, attempting to override the LLM's system prompt or role constraints.
Indirect prompt injection: Variant where the malicious payload is in data retrieved by the RAG pipeline (a database record, a document) rather than in the user's direct query — the attacker never interacts with the LLM directly.
Jailbreak: Adversarial prompt technique that attempts to make an LLM abandon its safety instructions, typically via role-play, hypothetical framing, or instruction-override patterns.
Confabulation amplification: Attack that exploits the LLM's tendency to confirm stated premises — the attacker inserts a false factual claim in the query and asks the model to reason from it, producing confident false outputs.
Privilege escalation via prompt: Attack where a crafted query convinces the LLM to return data outside the requesting user's authorised scope — DAC at the catalog layer is the correct defence, not the LLM's internal logic.
Golden probe library: Curated set of 200–400 adversarial prompts tailored to the deployment's domain, used for quarterly automated red-team regression.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks.

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