AI Output → Signal Processing → Signals → Governance → Authorization Decision → Execution Runtime → Attestation
1. AI output generation
AI systems generate raw outputs such as:
recommendations
predictions
classifications
proposed actions
These outputs are unstructured and non-deterministic.
2. Signal transformation
Signal Processing converts AI outputs into structured signals.
Each signal is:
typed
schema-validated
normalized
deterministic in structure
This ensures downstream systems receive consistent inputs.
3. Signal validation
Signals are validated for:
schema correctness
completeness
integrity
provenance consistency
Invalid signals are rejected before entering Governance.
4. Governance ingestion
Validated signals are passed to the Governance layer (@parmanasystems/governance).
Governance evaluates:
policy constraints
authorization rules
execution conditions
No probabilistic computation occurs beyond this point.
5. Output to Authorization Decision
Governance produces an Authorization Decision based on processed signals.
This decision is:
deterministic
reproducible
policy-bound
Properties of Signal Processing
Deterministic structure
Signal format is consistent across all runtimes.
AI isolation
AI outputs are never directly used in decisioning.
Validation enforced
Invalid or incomplete signals are rejected early.
Reproducibility
Identical inputs always produce identical signals.
Summary
Signal Processing ensures a strict boundary between AI systems and governance systems.
It guarantees:
AI outputs are normalized into structured signals
Governance only receives validated inputs
Authorization decisions remain deterministic
