The Digital Panopticon: How Nations Can Exploit Personal Data

by Benjamin Cook

If I was Ukraine or Taiwan I would outlaw the collection of all location data for commercial purposes, as well as the sale of all collected commercial data. You will soon understand why.

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Recent investigations have revealed significant security vulnerabilities due to the unregulated sale of mobile location data. A November 2024 report by WIRED uncovered that U.S. data brokers legally sold precise location information, enabling the tracking of U.S. military and intelligence personnel in Germany. This data exposed movements to sensitive sites, including nuclear storage facilities, posing serious national security risks.

Similarly, a December 2019 New York Times investigation demonstrated how easily accessible location data from smartphones could be used to identify individuals, including politicians, and track their daily activities. The study highlighted the potential for misuse of such data, emphasizing the need for stricter regulations to protect personal privacy.

Both articles underscore the urgent necessity for comprehensive oversight of location data sales to safeguard national security and individual privacy.

URGENT… yet 5 years have lapsed between articles.

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AI and Machine Learning (ML) are powerful tools for analyzing large datasets, such as location data, particularly for identifying patterns, deanonymizing individuals, and establishing "patterns of life." This is precisely the capability highlighted in the two similar articles published years apart, yet little progress has been made to address the vulnerabilities they expose.

Both the 2019 New York Times and 2024 WIRED articles demonstrate how unregulated sales of location data pose serious risks. The New York Times showed how easily available location data could be used to track individuals' daily routines and movements. Similarly, WIRED revealed that U.S. data brokers were selling precise location data, including the movements of U.S. military and intelligence personnel in Germany, potentially exposing critical national security sites like nuclear storage facilities.

Note: Interestingly, I used AI (ChatGPT 4o) to help me research and structure this piece. As well, rather than clutter an already long article with lots of definitions I invite you to use Google or ChatGPT to help you understand some of the concepts being discussed here.

How AI Could Be Applied to Such Data:

AI and ML are particularly well-suited for analyzing these large datasets, offering several practical applications:

1. Clustering and Anomaly Detection:

○     Algorithms can group frequent activities (e.g., home and work locations) and flag deviations or unusual patterns, such as trips to sensitive areas.

2. Temporal Pattern Analysis:

○     Time-series analysis with tools like RNNs or LSTMs can uncover daily routines, commute patterns, and behavior anomalies.

3. Geospatial Analysis:

○     AI models combined with GIS platforms can map movements and overlay them with known points of interest, helping to determine sensitive activities.

4. Social Network Mapping:

○     By correlating multiple individuals' data, AI can infer relationships and social networks, identifying key players within a dataset.

5. Predictive Modeling:

○     Machine learning models can predict future behaviors based on historical data, aiding in surveillance or risk assessment.

6. Multi-Dataset Integration:

○     AI excels at cross-referencing datasets (e.g., social media or purchase records), enriching location data to enhance deanonymization.

These capabilities can possibly also make AI a valuable tool for analyzing vulnerabilities and preventing misuse by adversaries.

Available Tools for Analyzing Data:

Several "off-the-shelf" AI platforms can perform many of these tasks, including:

●     DataRobot: Automates ML workflows for analyzing geospatial data.

●     Esri’s ArcGIS: Integrates AI for detailed spatial analysis, including clustering and predictive modeling.

●     Placer.ai: Focuses on consumer behavior and foot traffic analysis using location intelligence.

●     Mapbox MapGPT: Provides real-time insights into location data with conversational capabilities.

●     Geoalert: Specializes in analyzing satellite and aerial imagery to detect geospatial patterns.

Challenges and Ethical Considerations:

Despite the urgency, little regulatory action has been taken since these risks were highlighted. This is partly due to competing interests, such as the lucrative data broker industry and limited public awareness. The misuse of AI for deanonymization raises serious ethical concerns, including privacy violations and risks of misuse by authoritarian regimes or bad actors. Stricter regulations, transparency, and ethical AI use are essential to mitigate these threats.

Then there is the fact that the government we would turn to to regulate these things is in fact a consumer of the data. The US the government has found a “work around” to the constitutionally required warrant needed to surveil you. It might be illegal for a government agency to collect data on you without a warrant, but if they buy it from a third party that did the collecting, it’s legal.

Ultimately, AI tools are both a potential risk and possibly a powerful means of safeguarding against these big data threats. The question remains: will the sense of urgency translate into meaningful action, or will vulnerabilities persist until a significant incident forces change?

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Let’s assume our own nations are good stewards of our data. What about our adversaries?

If an adversary like Russia or China decided to utilize their vast resources and technological expertise to leverage location and behavioral data collected from apps, devices, and services, it could accomplish significant strategic objectives. Both nations, particularly China, already have access to vast datasets through platforms like TikTok, DJI apps, and Chinese-manufactured phones, providing them with a substantial head start. Here's an outline of what could be achieved, how it could be done, and the timeline for deployment:

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Potential Uses for Large-Scale Data Analysis

1. Surveillance and Control:

○     Domestic Monitoring: Analyze movement patterns of dissidents, protestors, and activists to preemptively disrupt activities.

○     International Espionage: Track diplomats, military personnel, and corporate executives globally to uncover sensitive operations or negotiations.

2. Military and Strategic Advantage:

○     Identify patterns of life for military personnel near sensitive installations, allowing for better targeting or espionage.

○     Monitor the supply chain and logistical movements of foreign militaries.

○     Use geospatial patterns to infer infrastructure vulnerabilities.

3. Economic and Corporate Espionage:

○     Track the movements of high-level executives and employees in industries like defense, tech, or energy.

○     Correlate location data with corporate activities, such as trade shows, R&D labs, and factory operations.

4. Psychological and Social Manipulation:

○     Combine location data with behavioral data (from apps like TikTok) to create profiles for targeted disinformation campaigns.

○     Use insights to manipulate social media algorithms to sow division or influence public opinion.

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Steps to Build and Deploy Such a System

1. Data Acquisition:

○     Collect data from apps, phones, IoT devices, and app stores, focusing on high-value targets like military bases, corporate campuses, and political gatherings.

○     Establish "legitimate" data broker companies to purchase data in Western markets or exploit existing legal loopholes.

2. Data Processing:

○     Preprocess raw data to clean and structure it, ensuring it can be analyzed efficiently.

○     Employ tools like Hadoop or Apache Spark for big data handling.

3. Model Development:

○     Train machine learning models to:

■     Deanonymize individuals based on movement patterns.

■     Establish "patterns of life."

■     Predict future behavior.

○     Use advanced AI frameworks (e.g., TensorFlow, PyTorch) with significant computational resources.

4. Integration and Deployment:

○     Integrate models into operational platforms that provide actionable intelligence for military or strategic use.

○     Deploy on cloud or edge computing systems to enable real-time analysis.

5. Continuous Improvement:

○     Feed new data into the system to refine and enhance predictive capabilities.

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Timeline for Deployment

Given the resources and data already available to a nation-state:

1. Phase 1: Data Aggregation and Preprocessing (3-6 months):

○     If datasets are already partially available (e.g., from TikTok, DJI), this step could move quickly, focusing on structuring and cleaning.

2. Phase 2: Model Training and Testing (6-12 months):

○     With significant computational resources (supercomputers or cloud infrastructures), this phase could be expedited.

3. Phase 3: Integration and Real-World Testing (6-9 months):

○     Deploy models in real-world scenarios, testing their effectiveness for specific tasks like surveillance or pattern prediction.

4. Phase 4: Full Operational Deployment (12-18 months):

○     Build user-friendly interfaces and systems for analysts, decision-makers, or operatives to act on AI-generated insights.

Total Time to Deployment: 18 months to 3 years, depending on the sophistication and scale of the project.

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Why This Is Feasible for China and Russia

●     China:

○     Already controls vast amounts of data through apps like TikTok and devices like DJI drones, along with a domestic ecosystem of data collection tools.

○     Centralized tech infrastructure and integration between private companies and government agencies (via laws like the National Intelligence Law) make data siphoning easier.

●     Russia:

○     While not as embedded in global tech ecosystems, Russia is skilled in cyber operations and could leverage partnerships with sympathetic corporations or use cyber-espionage to collect datasets.

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Key Concerns

1. Scale of Impact:

○     A nation-state could potentially monitor entire populations, not just individuals, influencing geopolitics and social stability.

2. Ethical and Legal Risks:

○     Exploiting this capability would likely violate international norms and laws but may go undetected due to the sophistication of operations.

3. Escalation:

○     The deployment of such systems could trigger a technological arms race in surveillance and data weaponization, with unpredictable consequences for global security.

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Final Thoughts

With the data and resources at their disposal, countries like China and Russia could deploy such a system in as little as 18 months, especially if leveraging existing infrastructure. This underscores the critical need for tighter regulations, data sovereignty protections, and robust international frameworks to counter misuse and safeguard privacy.

What’s the Solution?

The solution is simple. Yet, impossible. “Big Data” is real. What’s more, our regulating bodies are customers. The obvious solution is to ban collecting of personal data for commercial purposes. No opting in. Just ban it outright.  Or come up with a compartmentalization scheme that is impossible to deanonymize.  Use AI to do this.

Governments won’t like giving up their ability to surveil everyone. Sorry, I mean surveil just the criminals. The FBI is constantly pressuring big tech to give them backdoors to encryption on apps and phones. So, realistically it will take a politician being hacked, or attacked. Possibly another 9/11 style event. Or a major tech exec being compromised by an adversary. In the meantime, China, Russia, et al are going to be able to use AI to near-instantly compromise a large portion of our covert/sensitive military and intel personnel. Just so Christopher Wray can access a terrorist's phone in a small percentage of cases where other means fail. If Ukraine and Taiwan are smart, they will ban the collection of commercial data immediately.