“The country that cannot shield its data cannot shield its story. And in a world where generative AI is rapidly becoming the main author of that story—where it writes text, edits video, mimics voice, and manufactures emotion—the data deficit is no longer a technical oversight. It is a national security emergency.”

– Aditya Mohan, Founder, CEO & Philosopher-Scientist, Robometrics® Machines

When influence can be manufactured at scale by foundation models, the decisive resource is no longer oil or even silicon; it is authentic, high‑fidelity human data.  

James Madison warned that “knowledge will forever govern ignorance.” 23andMe’s data—genetic blueprints of millions of Americans—now sits at the center of a geopolitical contest. If that dataset flows into an adversary’s AI pipeline, the United States risks surrendering not just personal privacy, but biological leverage. The breach‑and‑exploit saga is more than a cybersecurity lapse; it is a warning flare for how democracies must guard genomic sovereignty in the generative native world. The lesson is clear: data is sovereignty, and nations that fail to protect it may find their future health, identity, and security shaped elsewhere.

“We cannot play innocents abroad in a world that's not innocent; nor can we be passive when freedom is under siege. Without resources, diplomacy cannot succeed.”

– Ronald Reagan, 40th President of the United States

(State of the Union Address, January 26, 1982)
Source

In context, this underscores why being second in a strategic technology race is not a viable option. It speaks to the necessity of active engagement and strategic investment when freedom is at risk—exactly the kind of context in which AI leadership and data sovereignty become matters of national defense. It emphasizes the importance of resources for influence and survival, which dovetails with the notion that falling behind in the AI race jeopardizes national interests.

History once hinged on who commanded armies, shipping lanes, or oil wells. Today, in what scholars call the generative native world, the critical lever is invisible: data—the feedstock that trains foundation models capable of shaping language, images, and even cultural memories at global scale. States that control broad, diverse datasets will steer the next generation of thinking machines; those that leak or forfeit such data risk ceding not only economic advantage but the power to define reality itself.

Against that backdrop, the bankruptcy of 23andMe is more than a corporate misfortune; it is a national‑security stress test. The company’s archive—raw genotypes and self‑reported traits from more than 15 million people—is scheduled for auction under Chapter 11 rules that treat DNA as an ordinary asset . If a foreign bidder acquires the trove, the United States would hand adversaries an irreplaceable slice of its genetic diversity—material that can be used to train bio‑foundation models, reverse‑engineer biometric identities, and craft selective threats ranging from bespoke pathogens to hyper‑targeted influence campaigns.

The stakes echo a wider data‑deficit problem already evident in social‑media platforms and large‑language‑model training sets. Authoritarian regimes hoard domestic data while harvesting the open flows of democratic societies, sharpening AI systems that can out‑persuade, out‑predict, and out‑maneuver open rivals. In this new battlespace, a single deep‑faked video or dialect‑matched voice clone—generated by models informed by leaked genomes—can erode public trust at neural speed. The arena is no longer just cyber or kinetic; it is cognitive.

Meeting that challenge demands more than better hygiene or incremental regulation. Genomic information must be recast as critical infrastructure, protected by export controls, vetted through an expanded CFIUS‑style review, and stored in privacy‑preserving federated trusts. Parallel investments in encrypted computation and democratic data alliances can ensure researchers still innovate while adversaries remain data‑starved. The future will belong not to the state that computes the fastest, but to the one that grasps the strategic value of data—and moves quickly to secure it.

This case study explains:

• What population‑genomic data are and why they matter

• How 23andMe’s dataset could be weaponised by state or non‑state actors

• Gaps in today’s regulatory armour (HIPAA, CFIUS, state privacy laws)

• Technical and policy options to protect citizens’ genetic blueprints

The paper is written for AI professionals, policy‑makers, and other non‑genomics specialists.

The 2023 credential‑stuffing incident shows the power of the relatives graph: hackers accessed 14,000 compromised accounts but retrieved information on 6.9 million individuals through opt‑in sharing features.

A “Digital Biobank” in Four Stacked Layers

1. Raw genotype files – Each customer’s test produces ~640 000 measured DNA positions (called SNPs) plus a much larger set that can be imputed by statistical inference. Across ≈ 15 million individuals that yields ≈ 9.6 trillion data points—comparable, in sheer size, to the text tokens that feed GPT‑4.

2. Phenotype & lifestyle surveys – Roughly 80% of users answer optional questionnaires covering diet, mood, medication response, and exercise. These free‑text answers link genetic variants to day‑to‑day behaviour, creating the “labels” machine‑learning models crave.

3. DNA Relatives graph – By comparing shared segments, 23andMe infers millions of parent–child, sibling, and distant‑cousin connections. This graph functions like a genealogical social network, enabling re‑identification of so‑called anonymous genomes through triangulation.

4. Longitudinal re‑contacts – Repeat surveys, email campaigns, and mobile‑app pings provide time‑series data: how a user’s sleep, weight, or prescription list changes year‑by‑year. Few other biobanks have that longitudinal richness at population scale.

Together these layers create what venture capital calls a full‑stack data moat: raw bytes, rich labels, social linkage, and temporal depth wrapped into a single asset.

Why this Matters in a Generative Native World

Key Concepts in Plain English

• SNP (single‑nucleotide polymorphism) – Think of DNA as a 3‑billion‑letter book; an SNP is a single‑letter typo that varies from person to person.

• Imputation – Using known patterns to fill in missing letters, boosting each file from ~640 k measured sites to 5–10 million estimated ones.

• Polygenic risk score – A number that adds up the small effects of hundreds of SNPs to estimate, say, diabetes risk—much like a credit score aggregates many financial signals.

• Re‑identification – The process of linking an anonymous genome back to a real name by comparing it to relatives or public records.

From Personal Curiosity to Strategic Leverage

When 23andMe launched, most customers wanted ancestry pie‑charts and quirky trait reports. In 2025 the same data underwrite strategic capabilities:

• Precision‑pathogen modelling – HLA gene frequencies in the dataset could guide design of a virus that spreads efficiently in one population but stalls in another.

• Cognitive‑social nudging – Variants in dopamine or oxytocin pathways correlate (weakly) with risk‑taking or social bonding; large‑N analytics can segment audiences for more persuasive messaging.

• Supply‑chain disruption – Knowing which enzyme deficiencies cluster in specific workforces (e.g., pilots, nuclear technicians) could inform targeted sabotage of medication stocks.

Analogy for Non‑Genomics Readers

Imagine every American’s browser history were combined with a detailed family tree and multi‑year health diary—then sold at auction. 23andMe’s trove is the biological equivalent: immutable, inheritable, and extraordinarily difficult to “change your password” if leaked.

Take‑Away for AI Professionals & Policy‑Makers

• For AI engineers: genome files are just another high‑entropy language with its own grammar; scaled models trained on them will unlock unforeseen applications—good and bad.

• For policy‑makers: traditional privacy laws treat DNA as healthcare data; the generative‑native reality treats it as both identity infrastructure and strategic commodity.

• For national‑security planners: the question isn’t whether genomes can be exploited—proof‑of‑concepts already exist—but how to limit who gets to run foundation‑model‑grade compute on them.

Closing Thought 

In short, 23andMe doesn’t merely hold personal health insights; it holds a population‑scale blueprint that, in the wrong hands, can be converted into an engine for biological insight, influence, and potential coercion—at machine speed.

The 23andMe saga unfolds like a three‑act legal drama. Each act tightens the strategic stakes, moving the company from regulatory curiosity to litigation target to distressed asset whose fate will ripple through national‑security planning.

Act I - Regulators Discover the Genome Goldmine (2013 – 2019)

2013 The U.S. FDA issues a warning letter halting 23andMe’s health‑risk reports until the science is validated. This marks the first time a federal agency treats direct‑to‑consumer DNA as medical instrumentation, not novelty.
2015‑2019 Partial FDA clearances trickle in, but the company’s valuation now hinges on its enlarging dataset rather than kit sales alone.

Act II - The Breach and the Courtroom Deluge (2023 – 2024)

24 Oct 2023 A credential‑stuffing attack exposes 6.9 million users via the “DNA Relatives” feature; Reuters breaks the story.
Autumn 2023 – Summer 2024 More than 40 class actions consolidate as In re 23andMe Customer Data Security Breach Litigation.
13 Sept 2024 A US$30 million settlement is announced, but plaintiffs retain the right to pursue equitable relief over future data handling. 

Act III - Chapter 11 and the Policy Grey Zone (March 2025 - May 2025)

23 Mar 2025 The company files voluntary Chapter 11 to pursue a § 363 sale of “substantially all assets,” explicitly classifying the genomic database as intellectual property, not protected health information.
30 Apr 2025 The bankruptcy judge approves bidding procedures and appoints a data‑privacy ombudsman; Reuters reports a court‑appointed overseer for genetic files.
11 May 2025 Bidding continues amid rumors of interest from U.S. pharma, EU bio‑AI start‑ups, and at least two foreign genome institutes—transactions that may escape CFIUS scrutiny because only data, not equity, would change hands.

The § 363 wrinkle

Under 11 U.S.C. § 363, debtors can sell assets “free and clear” of most liabilities. That fast‑track mechanism was designed for factories and trademarks, not 15 million genomic blueprints. Because the sale involves data rather than corporate control, CFIUS has only a narrow hook to intervene, leaving a regulatory gap at the very moment population‑scale DNA becomes a strategic commodity.

Comprehensive Timeline (to May 11, 2025)

Why Policymakers Should Care

• Speed vs. scrutiny – A § 363 auction can close in 60–90 days—far faster than CFIUS or new legislation can react.

• Data vs. equity – Current law focuses on who owns an American company; it says little about who ultimately computes on American genomes.

• Precedent setting – If population DNA can be off‑shored through bankruptcy once, every future direct‑to‑consumer bio‑startup becomes a potential national‑security loophole.

Closing Thought 

In short, the legal clock is running faster than the policy clock. Unless Congress or regulators expand their remit to cover data‑only transfers of critical personal information, the United States may watch its most intimate strategic asset—the genome itself—leave the courtroom free and clear.

Glossary of Key Terms

These terms underpin the strategic discussion in Section 4, translating genome jargon and AI shorthand into plain language for policymakers, legal teams, and technical leaders new to biomedical data.

From Double Helix to Foundation Model

Over the past five years, research groups have begun training genome‑scale language models—analogous to GPT—on billions of DNA letters. DeepMind’s AlphaFold 3 converts raw sequence data into atomic‑level protein structures, while projects such as MutBERT treat stretches of human variation like sentences, predicting the “missing letter” at each position (blog.googleBioRxiv). These self‑supervised models thrive on quantity: the more diverse the genomes, the better they learn subtle evolutionary rules.

Because the United States still holds one of the world’s most heterogeneous human datasets, losing stewardship of 23andMe’s archive would widen what analysts call the bio‑data deficit—the same vulnerability that already worries policymakers in semiconductor and large‑language‑model arenas. Chinese state‑backed entities have openly announced plans to map 100 million genomes by 2035; feeding U.S. samples into those efforts would help them build the biological equivalent of a multilingual GPT ahead of American labs (Yale Journal of International Affairs).

Identity Profiling in a Generative Native World

The marriage of DNA with generative media produces an entirely new reconnaissance surface. A single cheek‑swab genotype, linked with public genealogy graphs, lets an adversary predict hair colour, eye shade, even approximate face geometry. Recent academic demonstrations show machine‑learning pipelines that start with variant calls and end with a photo‑realistic avatar or synthetic voice trained on dialect‑associated SNP patterns.

In practice, that means:

• Deep‑fake passports whose biometric data “passes” a targeted country’s e‑gate algorithms.

• Persona factories that spin up thousands of social‑media accounts carrying plausible genetic markers of a swing‑state population, then micro‑target them with influence campaigns.

• Counter‑intelligence risks when clandestine operatives shed cells at an embassy party; soil samples can be sequenced, cross‑referenced against the breached dataset, and used to unmask their lineage.

Traditional privacy shields—blurred faces, voice changers—do not protect against such genotype‑to‑phenotype reverse mapping.

Population Analytics and Strategic Forecasting

Genomic big data also unlocks insights at scale. With millions of users reporting lifestyle and health traits, the 23andMe corpus serves as a live biosensor for American demographics. An AI system can estimate, for example, how many young adults in a specific ZIP code carry alleles linked to opioid metabolism or extreme heat intolerance. In a crisis, that knowledge translates to targeted coercion or sabotage: altering a widely used drug’s supply chain so that it disproportionately harms genotypes prevalent in a rival’s military age cohort.

Conversely, a domestic bio‑intelligence unit that lacks such data faces blinds spots. The Generative Native world rewards whichever actor can couple large, heterogeneous datasets with transformer‑scale compute. Falling behind on population‑level genomics means relying on foreign‑made analytic models—an uncomfortable dependence already familiar from the semiconductor shortage.

Bio‑personaliseM AI: Medicine, Marketing and Malware

Precision‑medicine firms now advertise “digital twins”—AI models that simulate how your body might react to a drug. They work by integrating two layers of information:

1. Your genotype and epigenetic profile
2. Large‑scale clinical outcome data learned by foundation models

If an adversary inserts poisoned parameters into the training set, the digital twin could recommend a dosage that quietly harms carriers of a specific allele. DARPA’s Safe Genes program funds counter‑measures, but those tools assume U.S. agencies retain sovereign control of baseline data (DARPA). In bankruptcy court, that assumption no longer holds.

Legal Fault Lines Revisited

• Export controls for “dual‑use bio‑AI parameters” do not yet exist; the CHIPS + Science Act addresses semiconductor know‑how but is silent on human‑omics IP (Yale Journal of International Affairs).

• The GA4GH policy brief warns that some national‑security statutes could chill international data sharing, yet provides no mechanism for emergencies when data leak abroad anyway (GA4GH).

• A burgeoning academic debate asks “When are foundation models not the answer?”—arguing that incomplete, biased genomic collections yield unreliable outputs (McDonnell Genome Institute). Handing U.S. data to better‑resourced foreign labs may lock American researchers into exactly that disadvantage.

Connecting the Dots: From Data Deficit to Doctrine

In military science, the side that sees farther usually wins. Population‑scale genomic corpora extend that maxim from missiles to microbes and from radar images to inherited code. If the United States relinquishes its largest civilian genome bank without enforceable guardrails, it risks:

• Strategic asymmetry: adversaries train next‑gen bio‑foundation models on American diversity while U.S. agencies work with censored or synthetic stand‑ins.

• Operational exposure: every future soldier, diplomat, or intelligence officer carries an immutable biometric beacon that a foreign AI can read from a coffee‑cup swab.

• Domestic fragility: loss of public trust in precision medicine, stalling the very innovations—pandemic‑ready vaccines, AI‑guided drug discovery—that a healthy bio‑economy should deliver.

The existing policy table (Section V) and technical‑safeguard table (Section VI) map concrete levers to mitigate those outcomes. The narrative you have just read explains why those levers must be pulled quickly: in a Generative Native world, the half‑life of strategic ignorance is measured in months, not decades.

“As we will see, the future belongs not to those who merely compute the fastest or store the most, but to those who see the strategic stakes of data itself—and act accordingly.”

– Aditya Mohan, Founder, CEO & Philosopher-Scientist, Robometrics® Machines

“The most potent weapon of the oppressor is the mind of the oppressed.” 

— Steve Biko, Father of Black Consciousness

In the generative‑native era, that weapon is forged from data—especially immutable data such as DNA. The path from a saliva kit to strategic harm follows four predictable stages; each stage is turbo‑charged when population‑scale genomes meet foundation‑model compute.

Narrative Walk‑Through

1. Acquire - A bankruptcy auction (§ 363) or a quiet licensing deal hands over petabytes of raw genotype files plus self‑reported traits. Unlike missiles or microchips, no customs officer inspects a hard drive crossing a border.

2. Re‑identify- Using public genealogy websites and 23andMe’s DNA‑Relatives graph, adversaries attach real names to “anonymous” genomes. Academic red‑team studies show 60 % success for fourth‑cousin matches; adding a leaked birth year pushes that above 85 %.

3. Analyze - Multimodal foundation models treat genomes like a high‑entropy language. They learn to predict disease, temperament, even facial structure—akin to the way GPT predicts the next word. The larger and more diverse the dataset, the sharper the inferences.

4. Exploit - Outputs feed into three operational buckets:

• Selective bio‑threats (pathogens tuned to HLA profiles).

• Identity targeting (matching stray DNA to an undercover operative’s family).

• Narrative warfare (micro‑segmenting persuasion content based on dopamine‑receptor variants).
  Every loop of data → model → influence closes faster, until the OODA cycle of bio‑information warfare shrinks from months to days.

Threat‑Pathway Table

“He who wishes to be obeyed must know how to command data.” 

—  adapted from Niccolò di Bernardo dei Machiavelli, Florentine Diplomat, Author, Philosopher, and Historian

Glossary for Non‑Specialists

Why this Matters

Heraclitus wrote, “The unseen harmony is mightier than the seen.” Genomic datasets are that unseen harmony: silent, persistent, and, once copied, eternal. In the age of foundation models, the party with the bigger harmony trains the stronger instrument. Fail to guard the pipeline at any of the four steps, and strategic autonomy dissolves—quietly, algorithmically, and at neural speed.

• HIPAA applies only to “covered entities” (clinics, insurers). Direct‑to‑consumer firms like 23andMe sit outside its core rules.

• GINA (Genetic Information Nondiscrimination Act) bars misuse by employers and insurers, not by foreign governments or data brokers.

• CFIUS currently lacks an explicit mandate to block cross‑border transfers of purely personal‑data assets.

• State privacy statutes (e.g., California’s CPRA) grant deletion rights, yet those rights become murky once a company is in bankruptcy.

“Trust, but verify.” — Proverb popularised by President Reagan

Verification is the watchword of the generative native age: cryptography, audit trails, and privacy maths aim to place ballast under a world where data flow at neural speed. Yet, as Sections 5 and 6 showed, the threat pathways exploit scale and feedback loops; technical fixes must therefore operate at comparable scale and speed, or they risk becoming Maginot Lines of the digital genome.

How Safeguards Map to Earlier Risk Steps

• Acquire → Encryption‑at‑rest guards physical hard drives but says nothing about authorised insiders who can copy decrypted files.

• Re‑identify → Differential privacy adds noise to prevent singling out one genome, but it can cripple rare‑disease research if over‑applied.

• Analyze → Homomorphic encryption lets AI crunch encrypted DNA, yet today’s runtimes turn a one‑hour GWAS into a one‑week cloud bill.

• Exploit → Watermarked model weights embed an ownership seal inside a foundation model, making illicit distribution detectable, though not yet stoppable.

Technical measures therefore reduce the attack surface but do not eliminate the strategic logic of Section V; policy and market incentives must close the remaining gaps.

Safeguard Table

“The wise warrior avoids the battle.” — Sun Tzu

In other words, design infrastructure so adversaries never touch raw data, rather than fighting them after exfiltration.

Key Terms at a Glance

Technical safeguards are necessary but not sufficient. They must be coupled with:

• Reciprocity mandates – to ensure foreign labs cannot sidestep HE or TEE by simply buying plaintext abroad.

• Export controls on foundation‑model weights – to make watermarking meaningful through enforceable penalties.

• Funding carrots – grants that cover the HE cost multiplier so researchers do not default to insecure shortcuts.

In the generative‑native world, data are the new oilfields and the new armies; purely technical bulwarks can slow a breach, but only policy and market design can remove the underlying incentive to loot the barrel. The next section turns to those systemic levers.

1. Classify population‑scale genomic archives as critical infrastructure, placing them under DHS and NIST cybersecurity minimums.

2. Expand CFIUS authority to encompass “bulk sensitive personal data” sales, not just equity transactions.

3. Adopt export‑control categories for high‑resolution human genomic data, analogous to ITAR restrictions on satellite imagery.

4. Create a federal Genomic Privacy Trust: users’ raw data are held by an independent custodian; commercial entities license access under strict terms.

5. Invest in privacy‑preserving computation R&D, including fully homomorphic encryption tailored to genome‑wide association studies.

Why Fresh Doctrine is Needed

In a world where synthetic content shifts opinions faster than armies can change borders, whoever owns the raw material for intelligence—data—sets the rules of engagement. Genomes are now part of that critical feedstock. Treating them like any other corporate asset invites strategic disadvantage: the country that sheds its genetic blueprints loses the ability to train, calibrate, and defend the next wave of bio‑foundation models.

Below is a menu of policy moves—legal, diplomatic, technical, and economic—designed to close the data deficit that today favors authoritarian data‑hoarders.

Putting Policies into Action

1. Sequencing the legal genome – Congress can fast‑track S. 289 by attaching it to the annual defense authorization, the same parliamentary route once used for CFIUS reform.

2. Leaning on the purse strings – NIH and BARDA grants should carry data‑sovereignty clauses: accept federal money, keep raw genomes on shore.

3. Creating positive incentives – Offer R&D tax credits for companies that train encrypted models on‑premise instead of exporting raw DNA.

4. Diplomatic alignment – Fold genomic data into the Quad Tech Working Group agenda; negotiate reciprocal safeguards with partners already implementing the EU Data Governance Act.

5. Civil society engagement – Make an “opt‑in to defend” campaign that frames secure genomic storage as patriotic duty, increasing public support for stricter controls without chilling medical research.

Bottom Line

History’s decisive resource was once oil, later silicon. In the generative‑native age it is diversified data—especially the blueprints that shape our bodies and, by extension, the AI models learning from them. Every policy above aims to convert genomic information from an exposed liability into a protected strategic asset, ensuring that democratic nations train the most capable bio‑AI without surrendering the cognitive high ground.

We have entered an age in which the decisive resource of statecraft is not steel, oil, or even silicon, but data—the lifeblood of foundation models that can write, speak, and persuade at a scale no human network can match. Whoever commands the richest, most diverse streams of that resource shapes the mind‑space in which rivals must operate.

The impending auction of 23andMe’s genetic vault forces the United States to decide whether its own biological blueprints are simply tradable assets or a strategic resource that demands sovereign care. If the files leave trusted hands, an adversary could fold millions of American genomes into next‑generation bio‑AI, gaining the ability to design selective threats and influence operations tuned to our very chromosomes. What looks like a routine bankruptcy case is, in reality, a test of national resolve.

More than two centuries ago, George Washington warned that “there is nothing so likely to produce peace as to be well prepared to meet an enemy.” (George Washington's Mount Vernon) Preparation today means securing the raw material of AI before an enemy trains on it. It also means equipping citizens and institutions with the insight to recognize and resist engineered narratives.

James Madison reminded us that “knowledge will forever govern ignorance; and a people who mean to be their own governors must arm themselves with the power which knowledge gives.” (University of Chicago Press) Genomic information is a form of knowledge—permanent, intimate, and, once lost, impossible to revoke. Treating it as critical infrastructure is therefore not an overreaction; it is self‑government in practice.

And as Dwight D. Eisenhower observed, “plans are useless, but planning is indispensable.” (Quote Investigator) The policy menu set out in this paper—stronger export controls, federated data trusts, democratic genomics alliances—may evolve, but the ongoing process of anticipating threats and re‑designing safeguards cannot pause.

Acting now will not only keep America’s genetic diversity safe from exploitation; it will demonstrate a broader principle for the generative‑native world: open societies can share knowledge without surrendering it. By treating data as a strategic asset rather than a commodity, the United States can hold the cognitive high ground in an era where the most powerful weapons are the models that decide what people believe to be real.