Challenges to the monitoring of deployed AI systems: Center for AI Standards and Innovation

Published

March 6, 2026

Author(s)

Anita Rao, Andrew Keller, Neha Kalra, Ryan Steed, Kweku Kwegyir-Aggrey, Kevin Klyman, Diane Staheli, Amanda Bergman

Abstract

As artificial intelligence (AI) systems are increasingly integrated into commercial and government applications, there is a growing need to monitor these systems in real-world settings. Although pre-deployment evaluations are valuable for assessing AI system capabilities at multiple points prior to release, they are predominantly conducted in controlled testing environments. Post-deployment monitoring is crucial for (1) validating that AI systems operate reliably as expected in real-world scenarios, (2) tracking unforeseen outputs that occur due to, e.g., model non-determinism or dynamic input conditions, and (3) visibility into unexpected consequences of AI systems in deployment contexts. Stakeholders across the AI ecosystem agree on the need for post-deployment monitoring; however, monitoring best practices, validated methodologies, and common terminology are still nascent and scattered across the field. This report proposes monitoring categories and surfaces challenges to robust post-deployment AI system monitoring, rooted in practitioner workshops and a literature review. The identified gaps, barriers, and open questions highlight opportunities for further investigation and innovation. Notably, this report quotes practitioners' repeated calls for guidance on post-deployment AI system monitoring methods.

Citation

NIST Trustworthy and Responsible AI - 800-4

Report Number

800-4

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.AI.800-4

Local Download

Keywords

post-deployment monitoring, continuous monitoring, artificial intelligence

Citation

Rao, A. , Keller, A. , Kalra, N. , Steed, R. , Kwegyir-Aggrey, K. , Klyman, K. , Staheli, D. and Bergman, A. (2026), Challenges to the monitoring of deployed AI systems: Center for AI Standards and Innovation, NIST Trustworthy and Responsible AI, National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.AI.800-4, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=961461 (Accessed March 7, 2026)