1. Connected Systems Analysis
Statistical sampling of AI-assisted documents across connected platforms. Sampling methodology: stratified random, 90-day lookback, proportional allocation per system. Confidence: 95%, margin ±5% for populations exceeding 10,000 documents.
2. Shadow AI Detection
Detection of ungoverned AI tool usage via identity provider authentication logs, MDM signals, and attestation ledger refusal events. Sources vary by connected system availability.
3. AI Agent Inventory (STRLZE-BoM-v1)
Enumeration of all AI tools, models, agents, and data flows active in the assessment period. Formatted as an AI Bill of Materials using Stratalize BoM schema v1.
4. Input Verification
Classification accuracy assessment, sensitivity tier compliance verification, and training data exposure quantification for AI inputs.
5. Output Verification
Claim extraction and grounding verification for AI-generated outputs. Cross-vendor consistency detection across multiple AI systems. ZK-proven authorization coverage percentage.
6. Permissions and Access
User access review including ghost user detection, over-provisioning identification, and sensitivity tier distribution.
7. Dormant AI Capabilities
Identification of available AI features with low or no adoption across connected platforms.
8. AI Stack Optimization
Recommendations for maximizing current stack utilization, agent optimization, and capability gap identification.
9. Framework Awareness
Light-touch mapping to relevant governance domains: model risk governance, data classification policy, AI inventory, access control, incident management. Not a compliance certification.
10. Risk Register and Remediation Roadmap
Prioritized action items with severity classification and resolution tracking.