quality_assurance · saas · workflow
Lessons From Red Teaming 100 Generative AI Products at Microsoft
As Microsoft's AI product portfolio expanded rapidly, the volume and scope of AI red teaming grew beyond what fully manual testing could handle, requiring automation to assess safety and security risks across an increasing number of GenAI systems.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Impact-first operation scoping
The first step in an AI red teaming operation is to determine which vulnerabilities to target.
Tools used
PyRITMITRE ATT&CK®GPT-4
Outcome
Microsoft's AIRT has red teamed over 100 GenAI products using PyRIT automation combined with human expertise, enabling the team to identify impactful vulnerabilities more quickly and cover more of the risk landscape than a fully manual approach.
What failed first
Fully manual red teaming became impractical at scale, and gradient-based attack methods proved computationally expensive while typically requiring full model access that commercial AI systems do not provide.
Results
Volumeover 100
Running since2021
Grounding & classification
Source type: technical build writeup
19 fields verified against source quotes, 1 dropped as unverifiable.
agentic workflowcontent generationfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarethroughput increasetechnical build writeupcompliance monitoringquality assurancehuman review queue