AI Latency Elevated

Technically allow for the automatic recording of events ('logs') over the lifetime of the system to ensure traceability of functioning appropriate to the intended purpose; for Annex III(1)(a) systems, logs must include the period of use, reference database, input data leading to a match, and identification of natural persons involved in result verification.

Take appropriate technical and organisational measures to use the system in accordance with the instructions for use; assign human oversight to competent, trained, supported natural persons; ensure input data is relevant and sufficiently representative for the intended purpose (to the extent the deployer controls data); monitor operation; suspend use and inform provider/distributor/authorities where risk under Art 79(1) is identified or after a serious incident; keep automatically generated logs for ≥6 months; inform workers and representatives prior to workplace deployment; comply with GDPR DPIA obligations; for law-enforcement use, register in EU database; inform persons subject to decisions; cooperate with authorities.

Establish and document a post-market monitoring system proportionate to the nature of AI technologies and risks; actively and systematically collect, document and analyse data on performance throughout the lifetime of the high-risk system; evaluate continuous compliance with Section 2 requirements. Implement based on a post-market monitoring plan (template to be provided by the Commission).

Unbounded Consumption occurs when a Large Language Model (LLM) application allows users to conduct excessive and uncontrolled inferences, leading to risks such as denial of service (DoS), economic losses, model theft, and service degradation.

Adversaries may target machine learning systems with a flood of requests for the purpose of degrading or shutting down the service. Since many machine learning systems require significant amounts of specialized compute, they are often expensive bottlenecks that can become overloaded. Adversaries can intentionally craft inputs that require heavy amounts of useless compute from the machine learning system.

Adversaries may target different machine learning services to send useless queries or computationally expensive inputs to increase the cost of running services at the victim organization. Sponge examples are a particular type of adversarial data designed to maximize energy consumption and thus operating cost.

The functionality and behavior of the AI system and its components - as identified in the MAP function - are monitored when in production.

Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management.

The organization shall define and document the necessary elements for the ongoing operation of the AI system. At the minimum, this should include system and performance monitoring, repairs, updates and support.

The organization shall determine at which phases of the AI system life cycle, record keeping of event logs should be enabled, but at the minimum when the AI system is in use.

A single fault (hallucination, malicious input, corrupted tool, or poisoned memory) propagates across autonomous agents, compounding into system-wide harm.