An Empirical Analysis on the Use and Reporting of National Security Letters
Published:
We collect and analyze public information on National Security Letters.
This is a paper outside of my normal research area. We publish the first unified data set, consolidating data reported by the government (e.g., FISA and ASTR reports), companies (transparency reports), and published NSLs themselves. We analyze the collected data and draw conclusions about how the use of NSLs by government agencies developed over time.
Abstract
National Security Letters (NSLs) are similar to administrative subpoenas and can be issued directly by elements of the executive branch without requiring prior approval from a court or grand jury. Importantly, NSLs authorize the imposition of nondisclosure orders (aka “gag orders”) on the receiving party. Controversy about potential abuses of this authority has driven a range of legal and policy discussions. To address these concerns, both the public sector and the private sector have sought to document the usage of NSLs in aggregated form. However, each data source is limited in scope, time, and kind. In this paper, we consolidate the available data around NSLs and answer two questions: (1) what can the public effectively learn from the reported data and does this information suffice to assess the NSL usage? (2) how accessible is this data collection? We show that longitudinal trends in the usage of NSLs can be observed. For instance, we find a significant increase in NSL requests for non-US persons and that the policy reforms to decrease the mandated nondisclosure period appear to be effective. The observed trends suggest that the current transparency mechanisms are viable safeguards against the excessive use of NSLs. However, aggregating and normalizing the data requires manual reviewing, parsing, and validating. We even find inconsistencies within and across official data sources. Overall, the laborious data collection process hinders external and internal auditing efforts and demonstrates the need for a unified and more usable dataset for NSLs.
Data Set
Our data set and processing scripts are available on GitHub.