AI creates a new audit problem
AI-assisted workflows can generate recommendations, scores, classifications, summaries, and actions. After the fact, it may be hard to reconstruct what the model presented, what confidence or context existed, and what a human approved.
What SignalFang can preserve
SignalFang can preserve the decision boundary around an AI-assisted workflow without becoming the model itself.
Model context
Recommendation category, confidence, version reference, or output hash.
Human decision
Approval, rejection, escalation, override, or reason code.
System state
Source system, timestamp, environment status, or degraded-mode flag.
Integrity proof
Tamper-evident receipt for later verification.
Human accountability without payload exposure
The receipt can reference hashes and metadata instead of storing protected model input, output, or mission/business payload. That keeps the proof layer focused and minimizes unnecessary data exposure.
The review question
The goal is to make it easier to answer: what did the AI-assisted system recommend, what did the human approve, and does the evidence still verify?
Quick answers
Is SignalFang an AI model?
No. SignalFang is not an AI model. It is a proof layer for decision evidence around workflows that may include AI.
Can it store model outputs?
The preferred pattern is to store hashes, references, and decision metadata rather than copying sensitive model payloads into SignalFang.
Why does AI need human authorization proof?
Because high-consequence workflows often require traceability between automated recommendations and human-approved actions.
Need proof around a workflow?
SignalFang is exploring pilot workflows where signed decision receipts, payload-safe evidence, and local verification matter.