Security in agentic systems is not a perimeter—it is a mathematical constraint. We engineer fail-closed architectures that prevent the exponential scaling of opaque errors across healthcare logistics networks.
Our implementation of the DGAL Governance pillar focuses on four critical security layers derived from the NIST AI RMF standards.
Mathematical enforcement of "if-then" constraints compiled directly into middleware, ensuring policy cannot be bypassed by agentic reasoning.
Every decision artifact is serialized, cryptographically signed, and committed to Write-Once-Read-Many storage for immutable provenance.
Rigorous Testing, Evaluation, Verification, and Validation (TEVV) standards applied to every agentic reputation score and feedback loop.
Deterministic revocation of conditional autonomy. If an agent exhibits explanation instability, the system reverts to scaffolded human control.
We move healthcare supply chains beyond the "Black Box" of AI. Our security model proves to regulators exactly what variables were weighted, which policies were followed, and how the system structurally prevents fairness gerrymandering.