AI Shadow Assurance for real external risk
Fusionstek helps security teams see externally exposed AI surfaces, connector-backed provenance posture, and third-party dependency risk in one assurance fabric — without overclaiming beyond the evidence.
Three proof pillars
AI Shadow assurance is strongest when external observations, provenance posture, and dependency risk are kept distinct and explainable.
Externally observed AI exposure
Find attacker-visible AI surfaces such as AI endpoints, public model artifacts, vector database exposure, and AI key indicators. This is the external-first assurance lane.
Connector-backed provenance posture
Add metadata-only internal provenance signals for lineage, dataset integrity, pipeline provenance, and RAG source integrity when deeper assurance is needed. Missing connector data is treated as a coverage limitation, not a breach judgment.
One assurance fabric
Bring together external AI exposure, provenance posture, and third-party dependency risk into a board-safe summary with explicit coverage boundaries, review priorities, and action guidance.
AI Shadow Discovery & Provenance Assurance
See exposed AI endpoints, public model artifacts, vector database exposure, and AI key indicators. Add privacy-safe provenance posture and dependency risk for a clearer assurance picture.
Built for truth, not AI hype
Fusionstek separates externally observed AI exposure, connector-backed provenance posture, and dependency risk instead of collapsing them into one vague risk label. That means clearer evidence boundaries, more defensible reporting, and less false certainty.
Truth note: External AI exposure, provenance posture, and dependency risk are complementary lenses. Missing connector data is a coverage limitation, not a breach judgment.
Make AI risk visible without overclaiming
Unified visibility across external AI exposure, provenance gaps, and dependency risk.