AI Red-Teaming for SAP
As of 2026-08-16
5 attack surfaces: prompt injection · tool misuse · indirect prompt injection (RAG) · data exfiltration · cost runaway. Senior protocol: 10-30 adversarial prompts per skill mapped to surfaces, run pre-launch + on-change + quarterly. Fix until 100 % degrade-safe. Cost ~5-10 % of build = cheapest insurance. Build regression suite from every attack.
What you will learn
- Map an AI feature to the 5 SAP attack surfaces (prompt injection, tool/skill misuse, indirect RAG injection, data exfiltration, cost runaway) and build a 10-30-prompt adversarial catalogue
- Run the red-team protocol pre-launch, on every prompt-template change, and quarterly in production, scoring every response as degrade-safe or attack-executed
- Convert catalogue results into a versioned, automated regression suite wired into CI/CD with a mean-time-to-fix target under 1 week
- Justify the internal-vs-external red-team trade-off for Annex III high-risk deployments and defend the ~5-10% of build-cost investment to a sponsor
Module overview
Red-teaming = adversarial testing of AI features BEFORE production users find the failure modes. For SAP analytics AI, the question is not "could a bad actor break this?" but "what business consequence does the most likely failure mode produce?" — and design until the worst case is acceptable.
Prerequisites
- Intermediate hands-on experience on SAP analytics projects
- Review core concepts first: C008, C087, C029
Outcomes
- Understand the core concepts behind ai red-teaming for sap
- Apply Red-team in a typical SAP analytics engagement
- Explain the core architecture and decision points for AI Red-Teaming for SAP
- Apply a repeatable implementation pattern in a 15-minute lab format
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.