Failure Pattern
AI workloads depend on precise identity, high integrity, and strict control over data flows. Legacy networks rely on metadata, shared trust zones, and identity assumptions that fail at AI scale.
What We See in the Field
AI models exchange sensitive data across large compute clusters. A compromised workload blends into traffic patterns. Identity drift contaminates training or inference pipelines.
Underlying Causes
No ground truth workload identity
Complex dependency chains
Metadata-based routing
Models trusting insecure pipelines
Weak segmentation across GPU clusters
Legacy networks rely on metadata, shared trust zones, and identity assumptions that fail at AI scale.
Trust-Native Resolution
DTL binds cryptographic identity to every AI workload and data flow. Only verified TrustKeys can access training data, model weights, and inference pipelines, ensuring data integrity and preventing poisoning.
Broken Trust Assumption
This failure pattern has played out repeatedly in real security incidents—not because of missing tools, but because of how trust is assigned.
In breaches such as SolarWinds, Capital One, Okta, and MOVEit, attackers did not bypass security controls. They operated through them, using valid identities, trusted credentials, signed code, and encrypted sessions. Security systems accepted these signals as proof of legitimacy, allowing malicious behavior to proceed.
The common thread across these incidents is structural: identity was assumed based on trust signals, not proven at the moment of execution.