Abstract
Enterprises have responded to generative AI risk in a predictable way: layer more safety controls. Filters, classifiers, refusals, policy gates, and human-in-the-loop checks are added incrementally with the assumption that more protection always improves outcomes. In practice, beyond a certain threshold, these controls begin to interact in nonlinear ways that degrade responsiveness, reduce answer specificity, and increase user workarounds. This paper defines that tipping point as the Safety Saturation Problem (SSP). The proposed framework introduces a Safety Saturation Index (SSI) that measures when cumulative guardrail pressure begins to suppress legitimate task performance. Rather than evaluating each safety mechanism in isolation, the approach models the combined behavioral footprint of stacked controls across real workflows. The architecture is model-agnostic and deployable across enterprise copilots, support automation, and internal knowledge assistants. Field-style evaluations show strong correlation between elevated SSI and user reports that AI systems are “over-filtered,” “slow,” or “unnecessarily restrictive.” As organizations continue hardening AI deployments, the ability to detect safety saturation early will be essential to maintaining both compliance and operational value.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The Safety Saturation Problem in Enterprise AI: When Guardrails Begin to Erode Utility", Technical Disclosure Commons, (February 26, 2026)
https://www.tdcommons.org/dpubs_series/9403