By Tim Leogrande, BSIT, MSCP, Ed.S.
Updated 06:19 PM EDT • Mon March 3, 2025
The newly formed Department of Government Efficiency (DOGE) has mandated that all U.S. federal employees submit weekly reports detailing their work accomplishments. Employees are required to provide five bullet points summarizing their tasks from the previous week and send these reports by 11:59 p.m. Eastern Time each Monday. The directive specifies that no links, attachments, or classified/sensitive information should be included; if all activities are sensitive, employees should state, “All of my activities are sensitive.”
The reported goal of this initiative is to increase accountability and efficiency within the federal workforce. However, it has faced resistance from several agencies. For instance, the Department of Health and Human Services (HHS) initially advised employees that responding was not mandatory but later mandated compliance, emphasizing the exclusion of sensitive information. Similarly, the Defense Department required civilian employees to comply after an initial pause, ensuring that national security topics were protected. Some agencies, such as the State Department and the FBI, have advised their staff not to respond due to concerns over sensitive information.
Critics argue that this requirement is burdensome and contradicts the goal of enhancing efficiency. Unions have expressed concerns about potential disciplinary actions linked to the directive, and legal challenges are ongoing regarding its implementation. Despite the controversy, the administration maintains that this measure is necessary to identify inefficiencies and improve government operations.
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In reality, this initiative is not merely a misguided bureaucratic efficiency tool, it is shaping up to be the most significant operational security (OpSec) breach in U.S. government history.
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This is not about justifying cybersecurity jobs or resisting workplace monitoring; it is about the systematic dismantling of the government’s security infrastructure through large-scale intelligence aggregation and artificial intelligence-driven exploitation.
What is being positioned as a simple weekly reporting mechanism actually introduces an unprecedented security risk. To wit, the act of requiring employees to CC: their supervisors is not about ensuring oversight of their emails, it is about using basic graph theory to construct a dynamic organizational tree of the entire federal government. This initiative allows those in control of the data to map classified operations, identify key personnel, analyze government workflows, and fine-tune AI models to answer intelligence-grade queries that no adversary has ever had access to before. What is happening right now is not just reckless; it is intentional. This is a strategic move to consolidate intelligence in a way that bypasses all existing security protocols, weakens internal governance, and allows for full-spectrum data exploitation.
Moreover, the requirement for government employees to submit weekly reports in a structured format is dangerous not because of the content of a single email, but because of what happens when millions of these reports are aggregated, analyzed, and modeled. This system enables the creation of a living map of government operations, revealing information that has always been deliberately compartmentalized to prevent large-scale exposure.
By compiling this data across agencies, those controlling the dataset can reconstruct internal reporting hierarchies, interdepartmental relationships, and hidden dependencies. They can see how different projects interact, which personnel work together, and where classified work is being done. Even employees who do not handle classified information can inadvertently expose critical intelligence simply by referencing their tasks, meetings, or collaborators.
Once this dataset is compiled, it allows for high-risk queries such as:
These are not hypothetical risks. They are real, active intelligence threats, and the architecture of this initiative appears to be deliberately designed to make these insights available to those in control of the data.
The most alarming aspect of this initiative is its potential to fine-tune large language models (LLMs) on government-wide intelligence. Once an AI model is trained on this dataset, it can be queried in ways that surpass traditional intelligence analysis methods. A well-trained AI system would be able to: