Run-Time Assurance for AI/ RTA-AI

Definition

Run-Time Assurance for AI is safety architecture that monitors AI outputs and switches to a verified fallback when behavior leaves bounds. In defense applications, it lets learning systems operate near certified envelopes without trusting them blindly. The hard part is bad monitors, unsafe fallback transitions, and unclear bounds, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a KhanBMS safety wrapper for autonomy that can affect motion or mission state, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

safety architecture

Operational value

Lets learning systems operate near certified envelopes without trusting them blindly

Primary risk

Bad monitors, unsafe fallback transitions, and unclear bounds

KhanBMS role

A KhanBMS safety wrapper for autonomy that can affect motion or mission state

Related terms

• Run-Time Assurance (RTA)
  Safety architecture that monitors and overrides untrusted autonomy at run time.
• Simplex Architecture
  Run-time assurance pattern with a verified backup controller and decision monitor.
• Fault-Tolerant Inference
  AI inference designed to keep functioning despite node loss, degraded sensors, hardware faults, or link outages.
• Human-on-the-Loop AI Supervision (HOTL-AI)
  Supervisory control model where humans monitor autonomous systems and can intervene or abort.