Failure Pattern
Data pipelines accept input from upstream workloads without verifying identity. Attackers inject malicious data that corrupts analytics and AI systems.
What We See in the Field
A compromised producer publishes manipulated or poisoned data. Downstream systems trust the producer because metadata matches expectations.
Underlying Causes
Pipeline trust assumptions
Overprivileged ingestion jobs
Certificate inheritance
Metadata spoofing
No trusted session validation
Trust-Native Network Resolution
DTL enforces identity on every data producer. Pipelines accept data only from workloads presenting valid TrustKeys.
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.