Protection for Me, But None for Thee: The Gilded Age of AI Governance in the United States

A New Federal Rule Reveals What the Government Knows, What It Demands for Itself, and What It Refuses to Provide for Everyone Else

In 1882, Mark Twain co-authored a novel called The Gilded Age. The title became shorthand for an era defined by spectacular wealth concentration, political corruption, unchecked corporate power, and a government that served the interests of industrialists while ordinary Americans bore the costs of rapid economic transformation. The railroads, steel mills, and oil refineries of that era generated enormous value, but the workers who built and operated them had no safety protections, no labor rights, no environmental safeguards, and no meaningful legal recourse when things went wrong. The government was not ignorant of the conditions. It simply chose not to act, because the people profiting from the status quo were the same people funding political campaigns and shaping policy.

We are in a new Gilded Age, and this time the industry is artificial intelligence. The parallels are not subtle. A small number of companies have accumulated extraordinary wealth and power by extracting value from resources they did not create and do not own. The people whose data, intellectual property, and creative work built the technology have received nothing in return. Workers are being displaced. Communities are absorbing environmental costs. And the government, rather than stepping in to establish the rules that would balance these interests, is looking out for itself while leaving everyone else exposed.

On June 17, 2026, the General Services Administration published a proposed rule in the Federal Register that makes this dynamic impossible to ignore. The rule, GSAR clause 552.239-7001, establishes comprehensive requirements for safeguarding government data processed by Large Language Model AI systems. It is meticulous, sophisticated, and demanding. It addresses data ownership, intellectual property rights, bias and truthfulness standards, foreign influence protections, supply chain accountability, and incident reporting. It represents exactly the kind of serious, detailed AI governance that the country needs.

And it applies only to the federal government’s own contractors. The rest of us get nothing.

What the Government Is Demanding for Itself

The proposed clause runs eight pages in the Federal Register. Comments are due August 3, 2026, and GSA has scheduled a public listening session for July 14 at George Washington Law School. The rule would apply across GSA’s government-wide contracts, including the Federal Supply Schedule, GWACs, and OASIS+, which are the primary vehicles through which federal agencies purchase technology services.

At its core, the clause does several important things. It establishes clear government ownership of all data processed by LLMs under federal contracts. “Government Data” is broadly defined to include all inputs (prompts, queries, documents, system prompts, knowledge bases, user account information) and all outputs (responses, analyses, metadata, synthetic data, derivative data). The government retains full ownership of all of it. Contractors get a limited, revocable license to use government data solely for performing the contract, and nothing more.

The clause flatly prohibits contractors from using government data to train, fine-tune, or improve their models for any purpose. They cannot use it for advertising, marketing, sales, business strategy, or to benefit other customers. They cannot sell or license it. Upon contract completion or termination, all government data must be permanently deleted from all systems, and the contractor must certify that deletion in writing.

The rule creates supply chain accountability through four defined roles mapped to the NIST AI Risk Management Framework: LLM Developers, System Operators, System Integrators, and Service Providers. Each role has specific flowdown requirements, meaning obligations cascade through every company involved in delivering an LLM service to the government. Foreign ownership and adversary government restrictions prohibit core components from being developed or operated by entities under the direction, influence, or control of adversary foreign governments.

And then there is this provision, which deserves to be quoted in its entirety because of what it reveals about what the government knows and is choosing not to require of AI companies in the private sector:

“The LLM must be truthful in responding to user prompts seeking factual information or analysis. The LLM must prioritize historical accuracy, scientific inquiry, and objectivity and must acknowledge uncertainty where reliable information is incomplete or contradictory.”

Read that again. The government is requiring that AI systems used on its contracts be truthful, historically accurate, scientifically grounded, and objective. It is requiring that these systems acknowledge uncertainty rather than present fabricated or skewed information as fact. It is prohibiting contractors from intentionally embedding “partisan or ideological judgments” through training data selection, fine-tuning, RAG references, system prompts, or other configuration methods. The government reserves the right to run its own benchmarks at any time to test for bias and truthfulness, and contractors must provide the tools and interfaces to make that testing possible.

The government understands that LLMs can be manipulated to produce biased, misleading, or outright false outputs. It understands that the companies building these systems have commercial, political, and ideological incentives to shape what their models say and amplify. It understands that without explicit requirements for truthfulness and objectivity, AI companies will optimize for engagement, revenue, and competitive advantage, not accuracy. And it has decided that these protections are important enough to mandate for its own use of AI.

But apparently not for yours.

Who Decides What Is True?

There is another dimension to the truthfulness provision that deserves scrutiny, because it cuts in a direction that should concern everyone regardless of political orientation.

The clause requires LLMs to be “truthful” and to “prioritize historical accuracy, scientific inquiry, and objectivity.” On paper, that sounds like exactly what we should want from any AI system. But the clause also gives the government the exclusive right to define what those terms mean in practice. The government reserves the right to run its own benchmarks to test for bias, truthfulness, and “unsolicited ideological content.” Contractors must provide the tools and interfaces for that testing. And critically, the clause states that the government is “under no obligation to disclose or provide access to the underlying data, methodologies, or systems” used in those assessments, with a narrow exception only when the results are used as the basis for an adverse contract action.

In other words, the government gets to decide what counts as historically accurate, what qualifies as scientifically sound, and what constitutes an “ideological judgment,” and it does not have to show its work.

Without clear, publicly available definitions of what these standards involve, without transparency into the benchmarks and evaluation criteria, and without independent oversight of the testing process, this provision could function as a censorship mechanism. Whatever administration is in power gets to determine the version of truth that government AI systems are required to produce. If a particular administration decides that certain climate data is not “scientifically sound,” or that a particular historical narrative does not meet its standard of “accuracy,” or that a factual analysis of a policy constitutes an “ideological judgment,” the contractor has to comply or face contract termination and decommissioning cost liability.

This is not a hypothetical concern. We have already seen federal agencies remove scientific data from government websites, redefine terms related to diversity and civil rights, and pressure researchers to alter findings that conflict with administration priorities. A provision that gives the government unilateral authority to define truthfulness for AI systems, without public visibility into the guidelines or the ability to challenge the standards being applied, is a provision that can be weaponized by any administration to shape what government AI tells federal employees and, through government-funded programs and services, the public.

The principle of requiring AI systems to be truthful is sound. The problem is that the clause gives the government the power to define truth without any requirement for transparency, public input, or independent review. That is not a safeguard against bias. It is a framework for controlling information.

A well-designed truthfulness provision would include publicly available evaluation criteria, independent review of benchmarks, a process for challenging government determinations, and protections against politically motivated definitions of “accuracy” or “bias.” The GSA rule includes none of these. The irony is hard to miss: a provision designed to prevent AI companies from embedding ideological judgments could just as easily enable the government to embed its own.

A Two-Tier System: Sound Familiar?

During the original Gilded Age, the federal government maintained one set of standards for itself and another for the public. Federal employees had protections that private sector workers did not. Government contracts included specifications and quality requirements that the open market did not demand. The government understood the risks of unregulated industry because it saw the consequences in the products it purchased and the infrastructure it funded. But it did not extend those standards to protect workers, consumers, or the environment until decades of public pressure, labor organizing, investigative journalism, and eventually catastrophic economic collapse forced its hand.

The GSA’s proposed rule creates exactly this kind of two-tier system for artificial intelligence. When the government is the customer, AI companies must protect data, delete it upon request, certify that deletion, refrain from using it for training, maintain transparency about their supply chains, disclose foreign influence, test for bias, ensure truthfulness, and report security incidents within 72 hours. When a private citizen, a small business owner, or a Fortune 500 company is the customer, AI companies can do essentially whatever their terms of service allow, which is usually whatever serves the company’s interests.

The people who drafted this rule clearly understand the risks with precision and sophistication. They understand that LLMs can be used to extract and repurpose data without consent. They understand the commercial incentives to use customer data for training and competitive advantage. They understand that bias can be embedded through training data, system prompts, and configuration choices. They understand that without explicit prohibitions, AI companies will act in their own financial interest. The rule itself is proof that these are not theoretical concerns. They are known, documented, and addressable risks that the government has identified and chosen to mitigate for itself.

If the U.S. government can draft clear rules about data ownership, data deletion, truthfulness, bias, and prohibited uses of data by AI companies, it can require the same protections for American consumers and businesses. The fact that it has not done so is a policy decision, not a technical limitation.

The Great Appropriation

The original Gilded Age was built on extraction. Railroad magnates took land through government grants and eminent domain. Steel and mining companies extracted natural resources and externalized the environmental costs onto communities. Oil companies built monopolies by acquiring competitors and controlling distribution. In every case, the value created by these industries was real, but the distribution of that value was radically unequal, and the people who bore the costs of extraction received little or nothing in return.

The AI industry has executed the same playbook, but at a scale and speed that would have astonished even the most ambitious robber barons. Every major LLM was trained on data scraped from the open internet, ingesting copyrighted books, articles, photographs, music, code, academic papers, personal blogs, and creative works without permission, without payment, and without recourse for the creators. The companies that built these models did not negotiate licensing agreements. They did not pay royalties. They simply took the work of hundreds of millions of people and used it to build the most valuable companies on earth.

The result is one of the most extraordinary transfers of value in economic history. Elon Musk became the first trillionaire in significant part because of AI’s integration into Tesla’s valuation and his stakes in xAI and other ventures built on the same foundation of scraped data and appropriated work. Sam Altman’s OpenAI went from a nonprofit research lab to a company valued at over $300 billion. The people who actually created the underlying data, the writers, photographers, coders, musicians, researchers, and small business owners, received nothing. Many of them are now watching AI systems trained on their own work compete with them for the same jobs and clients.

The government has stood in the wings and watched. Instead of establishing clear intellectual property protections that force AI companies to innovate within legal boundaries, the government has left enforcement to individual creators who must bring complex, expensive litigation against some of the wealthiest corporations in history. The New York Times, with substantial legal resources, has been litigating against OpenAI since December 2023. Most creators and small businesses cannot afford that fight. They are simply absorbing the loss.

Meanwhile, the GSA rule demonstrates that the government knows exactly how to draw these lines when it wants to. Paragraph (e) of the proposed clause spells out intellectual property rights with remarkable clarity: the government retains ownership of all data; the contractor gets a limited license; prohibited uses are specifically enumerated; custom developments belong to the government; and upon termination, everything gets deleted and certified. If the government can write those rules for its own data, it can write them for everyone’s data. The choice not to do so is a choice to protect AI industry profits at the expense of the people whose work built the industry.

Who Bears the Costs: Then and Now

The original Gilded Age had its company towns, its child laborers, its rivers catching fire, and its workers dying in unventilated mines and unregulated factories. The AI Gilded Age has its own version of externalized costs, and they are piling up fast.

The data centers that power AI systems consume extraordinary amounts of electricity and water. Communities near these complexes are experiencing strain on their water supplies, higher energy costs, and increased pollution from the fossil fuel plants that still power most of this infrastructure in the United States. Unlike Europe, where there is meaningful pressure to build sustainable AI infrastructure, the U.S. has imposed no comparable requirements. The environmental burden of AI development falls on local communities, ratepayers, and taxpayers. The financial benefits flow to a small number of companies and their shareholders.

At the same time, the AI industry is driving significant job displacement across multiple sectors. Creative professionals, customer service workers, paralegals, junior developers, copywriters, translators, and many others are seeing their roles automated or significantly diminished. The companies deploying this technology are capturing the productivity gains as profit. The workers being displaced are absorbing the transition costs on their own, often without meaningful retraining support or economic safety nets.

And the information environment itself is being degraded. AI-generated content is flooding the internet, polluting search results, and making it harder to distinguish reliable information from fabricated material. The same algorithms that the government is now requiring to be truthful and unbiased when serving federal agencies are, in the commercial sphere, being tuned for engagement, ad revenue, and platform growth. Only the “right” voices get amplified. The “wrong” ones get suppressed. Small and mid-sized businesses that cannot afford to pay for visibility are being drowned out by AI-optimized content from larger competitors. Individual creators are watching their original work get outranked by AI-generated summaries of their own material.

The government knows this is happening. The truthfulness and bias provisions in the GSA rule prove it. A government that requires LLMs to “prioritize historical accuracy, scientific inquiry, and objectivity” and to refrain from embedding “partisan or ideological judgments” is a government that understands exactly how these systems are being used to manipulate information in the private sector. And yet it has chosen to protect only itself from that manipulation. In fact, it could plan to use that ability to manipulate information to its own self-perpetuating advantage.

Where Gilded Ages End

The original Gilded Age did not end with a graceful transition to balanced governance. It ended with the Great Depression. Decades of unchecked corporate consolidation, speculative excess, labor exploitation, and regulatory failure produced an economic catastrophe that destroyed millions of lives and took a generation to recover from. The reforms that followed, the New Deal, the Securities Act, the National Labor Relations Act, Social Security, the Fair Labor Standards Act, did not arrive because industrialists had a change of heart. They arrived because the consequences of inaction became impossible to ignore and politically impossible to sustain.

We are not yet at that point with AI, but the trajectory is familiar. Wealth is concentrating at an accelerating rate. The gap between those who own AI infrastructure and those whose labor, data, and creative work feed it is widening. Public trust in institutions, in information, and in the fairness of the economic system is eroding. The government is “managing” the situation through piecemeal administrative action rather than comprehensive legislation, buying time while the structural imbalances grow.

The GSA rule is a case study in this dynamic. It is thoughtful, competent, and narrowly self-interested. It protects the government while the conditions that will eventually demand a broader reckoning continue to build unchecked.

Not Even Pro-Business

The administration has positioned its resistance to AI legislation as pro-innovation and pro-growth. But regulation by procurement is the opposite of a business-friendly approach. It creates a two-tier compliance environment where AI companies selling to the federal government face one set of detailed, binding obligations, while those same companies operating in the private sector face a patchwork of state laws that the administration is simultaneously trying to preempt. That is not predictability. That is a maze.

The administration has used executive orders and DOJ task forces to challenge state AI laws it considers overly burdensome. Colorado’s AI Act, which requires “reasonable care” to prevent algorithmic discrimination, was singled out as problematic. But the GSA clause imposes requirements that are far more demanding than any state law on the books: comprehensive data ownership protections, bias testing rights for the government, foreign influence disclosures, supply chain flowdown requirements, 72-hour incident reporting, mandatory model change notifications, and decommissioning cost liability for noncompliance. If these requirements are appropriate for federal contractors, the argument that state-level requirements for basic transparency and fairness are unreasonably burdensome collapses.

Actual legislation, the kind that Congress passes and the President signs, would give every company in the AI ecosystem a single, clear set of rules. It would create predictability. It would level the playing field. It would protect consumers and businesses alike. That is the pro-business position. What we have instead is the worst of both worlds: a government that protects itself through procurement while pressuring states not to protect their citizens, leaving the private sector navigating uncertainty while the AI industry operates with minimal accountability outside the federal contracting context.

Where This Fits in the Broader Landscape

The Shadow AI Policy

Just last week, Axios reported that export controls, voluntary testing frameworks, and procurement guidelines have become the building blocks of a “shadow AI policy” in the United States. I wrote about this dynamic and noted specifically that GSA was developing this rule. Now it has arrived, confirming the pattern: without comprehensive federal legislation, agencies are using procurement authority to impose the standards they believe are necessary. The result is protections for the government’s own vendors and no comparable framework for the rest of the economy.

The Anthropic Situation

The foreign ownership provisions in this clause connect directly to the Anthropic supply chain risk designation. The Pentagon designated Anthropic a national security supply chain risk after the company refused a military contract and publicly advocated for AI safety measures. Anthropic sued, arguing retaliation and abuse of discretion. The GSA rule approaches the same underlying concern, protecting government data from foreign access, through procurement rather than national security designations. It is more measured and legally defensible, but the same foreign influence risks that justify these protections for government data exist in the commercial sphere, where there are no comparable safeguards.

The Self-Regulation Question

In my analysis of public statements by the CEOs of Anthropic and OpenAI, I examined whether their calls for responsible AI development represent genuine commitment or strategic positioning to forestall government oversight. The GSA rule answers that question from the government’s perspective: the government does not trust the AI industry to self-regulate when it comes to government data and IP. It is imposing binding contractual obligations with real consequences, including decommissioning cost liability and contract termination. If the government itself does not believe voluntary self-regulation is sufficient for its own AI use, there is no reason the public should accept it as adequate for everyone else.

A Government By and For Whom?

The Constitution establishes a government of the people, by the people, and for the people. The AI governance landscape in the United States in 2026 does not reflect that principle. What we have is a government that understands the risks of AI with considerable sophistication, has demonstrated the technical and legal capacity to craft meaningful safeguards, and has instead chosen to deploy those safeguards exclusively for its own protection, while the people it is supposed to serve absorb the full impact of unregulated AI development.

Their creative work and intellectual property has been ingested by AI systems without permission or compensation. Their personal data is being processed by LLMs under terms of service that offer none of the protections the government demands for its own data. Their jobs are being automated. Their communities are bearing the environmental costs. Their water and air quality is being degraded. Their information environment is being manipulated by systems that the government has acknowledged need truthfulness and objectivity requirements, but only when the government is the one reading the output. And when ordinary people seek recourse, they find a legal system that requires them to litigate against companies with effectively unlimited legal budgets, because the government has not established the regulatory framework that would make such litigation unnecessary.

Every protection in the GSA clause is also an implicit admission. Every prohibition on using government data for training is an admission that AI companies would otherwise do exactly that, and in the private sector, they already are. Every requirement for data deletion certification is an admission that without such a requirement, data persists indefinitely, and for consumers, it does. Every truthfulness provision is an admission that without enforceable standards, LLMs produce manipulated, biased, and unreliable outputs, and that commercial AI systems are doing so right now, at scale, with real consequences for real people.

The government drafted protections for itself that it knows the public also needs. That is not an oversight. It is a choice. And like the original Gilded Age, the question is not whether that choice will eventually be corrected. The question is how much damage accumulates before it is.

The GSA’s proposed rule is competent, detailed, and addresses real risks. If finalized, it will create meaningful protections for government data processed by LLMs. That is a good thing, as far as it goes. But it also stands as a record of what the government knows, what it can do, and what it is choosing not to do for the people it serves.

The original Gilded Age produced the Progressive Era, the New Deal, and a generation of reforms that built the modern regulatory state. Those reforms did not arrive because industrialists volunteered for them or because the market corrected itself. They arrived because the public demanded them and because the costs of inaction became too catastrophic to sustain. We are building toward that same inflection point with AI. The only question is whether we arrive there through foresight or through crisis.

Key Dates and Links

Comment Deadline: August 3, 2026

Listening Session: July 14, 2026, 11 a.m. to 2 p.m. ET, George Washington Law School, Room Lerner 201

Registration Closes: July 3, 2026

Submit Comments: Search “Notice-MVAC-2026-01” at regulations.gov

Full Text: Federal Register Document 2026-12205

Sources

The Proposed GSA Rule

General Services Administration, “General Services Acquisition Regulation; Acquisition of Information and Communication Technology; Notice of Listening Sessions and Request for Comments,” 91 FR 36559 (June 17, 2026) https://www.federalregister.gov/documents/2026/06/17/2026-12205/general-services-acquisition-regulation-acquisition-of-information-and-communication-technology

GSA Interact, “Advanced Notice for MAS Refresh 31 and Upcoming Mass Modification” (First draft of clause, January 12, 2026) https://buy.gsa.gov/interact/community/6/activity-feed/post/4d70761f-60f8-4eb0-8119-052ec4c7c9b3/Advanced_Notice_for_MAS_Refresh_31_and_Upcoming_Mass_Modification

Executive Orders and Federal AI Policy

White House, “Ensuring a National Policy Framework for Artificial Intelligence” (December 11, 2025) https://www.whitehouse.gov/presidential-actions/2025/12/ensuring-a-national-policy-framework-for-artificial-intelligence/

Executive Order 14110, “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence” (October 30, 2023) https://www.federalregister.gov/executive-order/14110

OMB Memorandum M-25-22, “Driving Efficient Acquisition of Artificial Intelligence in Government” https://www.whitehouse.gov/omb/information-regulatory-affairs/memoranda/

OMB Memorandum M-25-21 (AI governance guidance for federal agencies) https://www.whitehouse.gov/omb/information-regulatory-affairs/memoranda/

AI Industry Framework: NIST

NIST AI Risk Management Framework (AI RMF 1.0), Appendix A https://www.nist.gov/artificial-intelligence/ai-risk-management-framework

AI Industry Wealth Concentration

“SpaceX IPO Makes Elon Musk World’s First Trillionaire,” Bloomberg (June 13, 2026) https://www.bloomberg.com/features/2026-spacex-ipo-elon-musk-trillionaire/

“Elon Musk Poised to Make History as World’s First Trillionaire,” Al Jazeera (June 12, 2026) https://www.aljazeera.com/economy/2026/6/12/elon-musk-poised-to-make-history-as-worlds-first-trillionaire

“Elon Musk Becomes the World’s First Trillionaire,” Visual Capitalist (June 2026) https://www.visualcapitalist.com/elon-musk-becomes-worlds-first-trillionaire/

“OpenAI’s $122B Raise at $852B Valuation,” Tech Insider (April 2026) https://tech-insider.org/openai-122-billion-funding-round-852-billion-valuation-2026/

“OpenAI IPO: $850B Valuation, $25B Revenue,” Tech Insider (June 2026) https://tech-insider.org/openai-ipo-850-billion-valuation-2026/

“The Trillion-Dollar IPO Test: SpaceX and OpenAI Face Public Markets,” Investing.com (May 2026) https://www.investing.com/analysis/the-trilliondollar-ipo-test-spacex-and-openai-face-public-markets-200680688

Government Removal of Scientific Data

“Disappearing Data: Trump Administration Removing Climate Information from Government Websites,” National Security Archive, George Washington University (February 6, 2025) https://nsarchive.gwu.edu/briefing-book/climate-change-transparency-project-foia/2025-02-06/disappearing-data-trump

“Disappearing Data, Part II: Distorted Science and Deregulation,” National Security Archive, George Washington University (September 30, 2025) https://nsarchive.gwu.edu/briefing-book/climate-change-transparency-project/2025-09-30/disappearing-data-part-ii-distorted

“National Climate Assessments Removed from Federal Websites,” Columbia Law School, Sabin Center for Climate Change Law (June 30, 2025) https://climate.law.columbia.edu/content/national-climate-assessments-removed-federal-websites

“Trump Administration Removes EPA Scientific Integrity Policy from Agency Website,” Columbia Law School, Sabin Center for Climate Change Law (August 21, 2025) https://climate.law.columbia.edu/content/trump-administration-removes-epa-scientific-integrity-policy-agency-website

“Why the Federal Government Is Making Climate Data Disappear,” Government Executive (July 15, 2025) https://www.govexec.com/management/2025/07/why-federal-government-making-climate-data-disappear/406715/

“All the Climate Info That Disappeared Under Trump. And How It’s Being Saved,” E&E News by Politico (January 23, 2026) https://www.eenews.net/articles/all-the-climate-info-that-disappeared-under-trump-and-how-its-being-saved/

“As Trump Administration Purges Climate Data and Web Pages, Research Groups Scramble to Save Information,” Inside Climate News (February 5, 2025) https://insideclimatenews.org/news/04022025/todays-climate-trump-climate-data-purge-archive/

AI Environmental Impact

“Rising Emissions, Depleting Water and Vanishing Land: AI Is Threatening Natural Resources for Billions,” United Nations University Institute for Water, Environment and Health (June 3, 2026) https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints

“AI Could Use as Much Water as 1.3 Billion People by 2030, U.N. Report Warns,” Time (June 3, 2026) https://time.com/article/2026/06/03/ai-global-water-resources-un-report/

“Data Centers Could Consume 9.3 Trillion Liters of Water by 2030,” Earth.org (June 2026) https://earth.org/9-3-trillion-liters-of-water-un-report-exposes-unfathomable-footprint-of-data-centers-as-ai-booms/

“Data Drain: The Land and Water Impacts of the AI Boom,” Lincoln Institute of Land Policy (February 2026) https://www.lincolninst.edu/publications/land-lines-magazine/articles/land-water-impacts-data-centers/

“The Carbon and Water Footprints of Data Centers and What This Could Mean for Artificial Intelligence,” ScienceDirect (December 2025) https://www.sciencedirect.com/science/article/pii/S2666389925002788

AI and Intellectual Property Litigation

The New York Times Company v. Microsoft Corporation et al., Case No. 1:23-cv-11195 (S.D.N.Y., filed December 27, 2023)

State AI Laws and Federal Preemption

Colorado AI Act, SB 24-205 (effective June 30, 2026)

California Transparency in Frontier AI Act, SB 53 (effective January 1, 2026)

Texas Responsible AI Governance Act (effective January 1, 2026)

The Gilded Age: Historical Reference

Mark Twain and Charles Dudley Warner, The Gilded Age: A Tale of Today (1873)

Federal Procurement and Contracting

48 CFR Parts 539 and 552 (General Services Administration Acquisition Regulation)

FedRAMP Authorization Program

https://www.fedramp.gov/

CISA Incident Reporting Form https://myservices.cisa.gov/irf

Regulatory Docket

Comments may be submitted at regulations.gov by searching for “Notice-MVAC-2026-01”

https://www.regulations.gov