Category: Artificial Intelligence (AI)

As AI Moves Fast and U.S. Policy Flounders, Will Organizations Look Abroad for Data Security?

data security

Last week’s firing of the head of the National Security Agency and U.S. Cyber Command, along with his deputies, is one more reason to conclude that the United States is not led by serious people. As the administration waves off the implications of Signalgate and then fires Four-Star General Timothy D. Haugh et al. on the reported basis that Laura Loomer told Trump they are “disloyal,” any common-sense observer will justifiably doubt whether national security is a priority for this administration. Concurrently, one wonders whether the administration’s security clumsiness, combined with its deepening relationship with U.S. Big Tech leaders, will foster anxieties over data security as organizations in every sector develop new AI models that will be tomorrow’s attack vectors.

While U.S. Big Tech praised Trump’s revocation of the Biden EO on AI as an end to regulation, the move could erode confidence for many organizations that need to develop AI in environments provided by domestic suppliers of confidential computing services. Although the U.S. remains a leader in cybersecurity, Americans are targeted by cyberattacks more than any other country, and rescinding the Biden EO did not reverse any regulation. On the contrary, exacerbating the U.S. history of laissez-fair cyber policy, Trump has been a direct beneficiary of data abuse and micro-targeting misinformation; and more than half of all citizens likely assume that our private data is not only insecure, but that the current administration would not scruple to exploit it for the most draconian purposes.

For my recent post about Section 230 reform, I spoke with Peter DeMeo, Chief Product Officer of Phoenix Technologies AG in Switzerland about agentic AI as both opportunity and threat. Not yet fully realized, the principle is that an AI agent can act autonomously to improve or maintain a given system. “But you want to keep the agents in a good place,” DeMeo says. For instance, he describes a Swiss hospital group where the IT infrastructure crashed overnight, but the staff found the agent had fixed the problem and kept operations running. This kind of positive result, however, should not mask the fact that AI agents are new attack vectors. DeMeo explains…

Imagine a foreign adversary infiltrating a hospital’s network through a sophisticated phishing attack, poisoning the AI agent’s data and turning it malicious. Unaware of the compromise, the IT team deploys these sleeper agents into a trusted execution environment—a secure enclave, where they can operate autonomously. From within this stronghold, the malicious agents launch a next-generation ransomware attack, encrypting critical system data. Surgeons and medical staff are locked out, unable to access patient histories, scans, and essential systems—crippling hospital operations and endangering lives.

Is the U.S. a Robust Data Security Environment?

America’s data security landscape comprises a patchwork of federal law, state law, and what might be fairly described as an honor system among many major providers of confidential computing services. U.S. policy (i.e., let Big Tech do what it wants) combined with “operational assurance” (i.e., trust the provider to do what it says) may not provide the kind of confidence various organizations demand as they develop and deploy agentic AI. And that was before DOGE’s questionable access to, and haphazard handling of, sensitive information—or before Trump fired the top cyber security official without cause.

Meanwhile, a key indicator to follow in this context will likely be the insurance industry. For instance, Chubb, a major provider of cyber insurance, released its first Navigating the Cyber Claims Landscape report early this year. The report shows, for instance, ransomware incidents increasing in the U.S. while they are declining outside the U.S, and it explicitly states that “A zero trust security model is essential to maintain controls.”

If organizations look outside the U.S. for confidential computing, Switzerland could emerge as a hub for the level of data security needed to confront the vulnerabilities inherent to agentic AI. For instance, Phoenix’s business model combines decades of confidential computing experience, compliance with Switzerland’s stringent data protection laws, and pricing tiers that make confidential computing accessible for small and mid-size organizations. Rather than “operational assurance,” as Chief Technical Officer Angel Nunez Mencias, explains, Phoenix provides “technical assurance,” meaning that only the customer holds the encrypted key to their own data. There is no “back door,” and it would not be possible to make a customer’s data available to a third party—not even with a warrant issued under the U.S. Cloud Act.

In compliance with the Swiss Federal Act on Data Protection (FADP), not only must the customer approve every change deployed, but statutory provisions include strict civil, and even criminal, liabilities for mishandling certain data—especially sensitive information about natural persons. Asked whether this approach to security might inadvertently provide opportunity for cybercriminals or terrorist organizations, Mencias notes, “Confidential computing is not a black box. Just as the customer must approve every change, we approve the software deployed in our environment.”

IT professionals at organizations in the U.S. and abroad will decide whether providers like Phoenix offer a more secure environment for advancements in agentic AI computing, but the value proposition DeMeo describes provoke questions that were difficult before the current U.S. administration began breaking things. Now that it shall be the policy of the United States to cede the field of excellence in a wide range of disciplines, it is fair to ask whether various organizations will look elsewhere for data security.

DC Circuit Affirms Human Authorship Required for Copyright

human

In a decision that is unsurprising but important, the DC Circuit Court of Appeals affirmed that “authors,” as defined in U.S. Copyright Act, are human beings and not machines that can autonomously generate works. I say unsurprising because nothing in history or statute should have led the court to any other conclusion, and indeed the opinion can be summed up thus: “…the text of multiple provisions of the statute indicates that authors must be humans, not machines.”

Dr. Thaler, a computer scientist, developed a generative AI (GAI) he calls Creativity Machine, which autonomously generated a visual work for which he applied for a claim of copyright with the U.S. Copyright Office. Thaler disclosed that the work was wholly created by the machine, and on the basis that copyright can only attach to works made by humans, the Office rejected the application. Thaler sued, arguing that the Office was asserting a policy not found in the statute or the constitutional foundation for copyright. He lost in the district court, and the appellate court has now affirmed that ruling. (See earlier posts.)

Specifically, the court cites several operative provisions of the Copyright Act that would be nonsensical if machines were “authors.” “Machines do not have property, traditional human lifespans, family members, domiciles, nationalities, mentes reae, or signatures,” the opinion states. This summary refers to the right to own any kind of property, duration of copyrights, inheritance of copyrights, jurisdictional enforcement of copyrights, incentive to create works, and the right and authority to transfer copyrights.

None of those rights or capabilities apply to non-humans, and non-humans do not have standing in court to adjudicate conflicts over such matters. Consequently, U.S. copyright law would unravel if machines were “authors,” which would, notably, moot Dr. Thaler’s claim that his GAI called Creativity Machine is legally the “author” of the visual work he sought to protect. “Numerous Copyright Act provisions both identify authors as human beings and define ‘machines’ as tools used by humans in the creative process rather than as creators themselves,” the opinion states. Imagine the opposite conclusion and Creativity Machine could be named as a plaintiff in an infringement suit. Chaos ensues, and not just for copyright.

As to Dr. Thaler’s theory that under the work made for hire (WMFH) doctrine, he could claim copyright in the work generated by the AI he owns, the court is clear that this misreads the principle. In plain terms, under WMFH, rights transferred to the hiring party must exist in the first place, but those rights can only be vested in a human being upon creation/fixation of a work. No human author means there are no rights to transfer to a hiring party.

Although the Thaler decision is not surprising, it is important because it reaffirms a core doctrine as both case law and policy evolve in response to GAI. By affirming the boundary that 100% machine-generated expression is not protected, this solidifies the framework in which courts to do what they often do in copyright cases—namely to separate protected expression from unprotected elements in a given work.

The more compelling and trickier question as to what is protected and not protected when an “author” uses a generative “machine” as a tool is now active in the District Court for the District of Colorado. As discussed in this post, artist Jason Allen presents a plausible argument that he used Midjourney as a tool to create and fix his mental conception of a visual work of expression. Arguably, Allen v. Perlmutter will be the first case to write early guidance for the use of GAI to create works that may be protected. As such, that outcome just might be surprising and important.

Photo by: Designer491

U.S. Copyright Law, Not Big Tech, Democratized Authorship

copyright law

Many copyright scholars refer to England’s Statute of Anne (1710) as the “first authors’ copyright law,” but I quarrel with that summary. In that year, and for many decades to follow, English “rights” for authors were too intertwined with the Crown’s authority to sanction publication of works for us to think of the Statute of Anne as affirming copyright rights as we understand them today. Although the administrative mechanisms of the Statute of Anne did inform the first U.S. Copyright Act of 1790, the “democratization” of authorship, which tech companies like to claim as incompatible with copyright law, was baked into American copyright as part of a novel Constitution expressing fundamental rights in a context to which no other nation on Earth could claim precedent.

Article I, Section 8, Paragraph 8—the progress clause—is a declaration of hope, to echo a sentiment of Elizabeth Wurtzel’s. While most of the roughly three-million Americans were farmers with little formal education, the progress clause (or IP clause) expressed an ambition that America would eventually produce its own literature, scholarship, and invention. But significant distinctions between the new U.S. and England (and other parts of Europe) established American copyright law as egalitarian and democratic.

First, the government was not granted authority by the Constitution to sanction or deny publication. Second, the speech, press, and establishment clause exerted considerable force upholding the author’s right to express himself. And finally, the European tradition of art and science patronage by the nobility could not become dominant in either the economic or political composition of the young nation. For better or worse, even with its imperfections, professional authorship in the U.S. would be subject to the democracy of the market, and the copyright rights vested in the individual author were, and remain, the sole basis for a fair-trade relationship with that market.

Enter Big Tech and their big bullshit word “democratization.” They love this term because, like so many in its bag of rhetorical tricks, it sounds progressive, egalitarian, and even anti-corporatist, which is funny coming from the most powerful oligarchs since Vanderbilt and Rockefeller. They even claim to have democratized democracy, and indeed, they may well have democratized it all the way to authoritarianism. So, when Big Tech says “democratization,” it is always a grift, but it is still worth understanding how the rhetorical meaning has shifted in reference to authorship and creative work.

Distribution, Derivatives, & Data

Until generative AI changed the dialog in the last few years, the claim that “democratization” was antithetical to copyright tended to focus on attacking distribution rights or the derivative works right. Distribution rights, according to Big Tech, were only administered by “rent-seeking gatekeepers,” thereby rationalizing mass piracy followed by the arrogation of distribution to streaming platforms as new intermediaries. The result was platforms “democratizing” far more revenue out of creators’ pockets than the allegedly outdated models.

The other rhetorical use of “democratization” tended to focus on the alleged injustice of the copyright right of the author to prepare (or authorize) derivative works. This battle was fought in public over the proposed cultural value of “remix,” a pet project of Lawrence Lessig, and which fostered a lot of assumptions and misstatements in the blogosphere about fair use doctrine. That battle was settled, at least as a matter of law, with the outcome in AWF v. Goldsmith, which rescued the derivative works right from being swallowed by overbroad application of fair use.

On a more subtle level, Silicon Valley advocates also argued that digital modes of production are inherently easier and cheaper (neither of which is necessarily true) and, thus, it was argued that digital tech both “democratizes” production and justifies rethinking legal protection of the works produced. Likewise, anti-copyright academics, on behalf of Big Tech, have argued that unprecedented data-driven market analysis lowers the risk of production, which, again, supposedly demands rethinking the purpose and application of copyright law.

These and other variations on the theme that tech “democratizes” now coalesce and mutate around the argument that GAI is important because it “enables everyone to be a creator.” Hence, “gatekeeper” intermediaries like record labels are no longer the only “barriers to progress” because now, the professional creator who spent years perfecting her craft is accused of elitism for trying to protect the exclusivity of her art. This argument is absurd on its face because, of course, typing a few prompts into Suno to produce a guitar riff ain’t gonna make you Mark Knopfler. And in this sense, GAI is to creativity as the “democratization of information” is to expertise. Indeed, everyone using Midjourney can be a visual artist the same way that everyone using Google search can be an epidemiologist.

It is notable that “democratization” is the same con game, whether the subject is creative work or information because the constitutional purpose of copyright law was established to “promote science.” This is not to say that the Framers intended to exclude fiction or poetry or fine art from copyright law, but by any interpretation of the word “science” from the period, it is fair to say that the Framers hoped later generations of Americans would be creative and intelligent. And, yes, it was imagined and hoped that creative and intellectual contributions might one day come from any citizen. That was “democratization” circa 1790 whereas Big Tech’s application of that word, a euphemism for exploitation, has not been wholly beneficial for either democracy or intelligence or creativity.