Pressuring Big Tech to prioritize data privacy is key to prevent authoritarian data attacks---collapses democracy.

The United States has yet to show up to this information fight. U.S. cyber strategists prioritize defending physical infrastructure—routers, servers, and endpoint devices such as laptops and smartphones—but consistently underestimate the economic and political significance of the information carried on that infrastructure. The U.S. private sector, which rarely acts in the American national interest, is primarily responsible for protecting data and information platforms.

It can be tough to craft regulations and national security policies for data and technology that do not fall afoul of democratic and capitalist values. A national information strategy can sound, or indeed become, Orwellian without the right political leadership. But to thrive in the twenty-first century, democracies must now put information at the center of domestic, security, and foreign policy.

Marshaling Data

Russia’s interference in U.S. elections may be the most well-known case of a state using sensitive political and social information to attack another, but it is by no means the only example. In 2014, Chinese hackers stole the personal information of more than 22 million people connected to U.S. security clearance processes. That breach not only demonstrated the vulnerability of U.S. government systems to hostile actors but also provided China with a tremendous resource. China could use the stolen data to profile and target U.S. officials and their families. It could also use the “clean” and highly structured data to train the algorithms that power military-related artificial intelligence projects, sharpening the weapons in its cyberwar arsenal.

For similar reasons, China sponsors the theft of commercial data. Security officials believe that the data breach that rocked credit reporting giant Equifax in 2017—in which hackers stole the financial and personal information of approximately 147 million Americans— originated in China. That stolen U.S. information can make up for gaps in China’s own datasets. Although consumers provide plenty of data to Chinese e-commerce giants, China’s financial and health-care industries are threadbare compared to their U.S. counterparts. The Equifax operation provided the Chinese with detailed, organized information on nearly half the American population. Neat, easily searchable data, however acquired, is vital for companies to train artificial intelligence applications for both military and commercial use.

No advanced economy boasts a national artificial intelligence strategy more mercantilist than China’s. Beijing seeks to lead the world in this area by 2030. Xi’s doctrine of civil-military fusion yokes together the research efforts of private corporations, China’s military industrial base, and the intelligence agencies of the People’s Liberation Army. China subscribes to the zero-sum ethos of data competition; states want to acquire data from others, while limiting access to their own data. To that end, China, India, Russia, and other countries have introduced tough data localization laws to prevent companies from taking certain types of information across national borders.

In the private sector, access to data tends to create a virtuous cycle: more data lets companies build better applications, which makes them more profitable and allows them to harvest and monetize even more data. The dynamic explains why information giants, like Alibaba, Amazon, Facebook, Google, and Tencent are monopolies. But this commercial logic also prompts states to think in zero-sum terms.

The United States should prepare for the prospect of authoritarian regimes ramping up cyberattacks against the databases of its major companies. If China, for instance, wanted to boost its own national champion as the dominant player in a particular market, even a minor cyberattack against a rival firm could be very damaging. Stealthily disrupting or “poisoning” inputs through a data-integrity attack or by manipulating the quality-control algorithms that govern industrial processes and the delivery of products could result in inferior, potentially harmful outcomes. Before an affected company even detects this kind of attack, it could face numerous economic, legal, and reputational problems, losing consumers and becoming dislodged from supply chains.

Data-driven machine learning systems now assist decision-making in many areas of the private and public sectors. A data integrity attack does not even need to be successful to be catastrophic. The perception that an organization’s data is poisoned and its decision-making processes corrupted could reduce public trust in a company, in a government agency, and even in democratic process.