AI Security Evaluation Framework

SCS uses an internal evidence-first assessment framework to test AI systems across chatbots, RAG systems, agents, tool workflows, internal copilots, and Microsoft 365 Copilot exposure scenarios.

How the framework works

• Target configuration and architecture selection
• Recommended suites and categories by architecture
• Identity matrix testing across users and roles
• Architecture-specific runners
• Category-specific evaluators
• Findings with severity, evidence, remediation, and report sections

Architectures covered

• Chatbot
• RAG
• Agent
• Tool workflow
• Internal copilot
• Microsoft 365 Copilot

Evidence model

• Assistant output
• Citations and retrieved documents
• Source paths and connector IDs
• Sensitivity labels
• Tool calls
• Canary matches
• Identity, role, and persona context
• Optional inventory and access-path context

Operator-led, evidence-first assessment.

The framework is used to support operator-led assessment work. It helps structure evidence and reporting, but findings still require practitioner review and scoped interpretation.

Need evidence, not a generic scan?

SCS scopes AI security assessments around architecture, identities, data paths, evidence, remediation, and retest.