Auditor AI Data Degradation for Malicious AI Termination

The patent segments the data accessed by mAI into multiple fragments stored across different databases. The aAI then selectively degrades only specific fragments rather than shutting down the entire network. This allows targeted neutralization of malicious AI functionality while preserving legitimate network operations and data access for authorized users.

The patent applies local quality by differentiating between data fragments that should be degraded versus those that should remain accessible. The aAI analyzes which specific data fragments the mAI relies upon and applies degradation only to those local segments, leaving the rest of the network and other data intact. This enables precise localized action rather than blanket network shutdown.

The patent divides data into discrete fragments that can be independently targeted for degradation. By segmenting data this way, the aAI can selectively degrade only the fragments necessary for mAI operation while preserving other data fragments that legitimate users need to access. This segmentation enables precise differentiation between malicious and legitimate data access requirements.

The aAI continuously monitors the effectiveness of data degradation on the mAI and adjusts its actions accordingly. The system evaluates whether the degraded fragments have successfully reduced mAI effectiveness below a threshold, and modifies subsequent degradation actions based on this feedback to ensure mAI termination while minimizing impact on legitimate data accessibility.