ICYMI
• AI in the Shadows – Your staff will find ways to access the most powerful AI tools, even if you ban them
Executive Summary
• Sensitive information, such as corporate strategy, can be deduced by outsiders prepared to collate and analyse fragments of publicly available information, such as job adverts.
• AI has made it far quicker and cheaper to collate and analyse data in this way.
• Conventional information-security controls do not fully address this risk.
• Scraping public data from the web is generally not treated as illegal.
• Firms can reduce exposure by red teaming their own data exhaust and risk-rating their outbound communications.
On the night of 1 August 1990, the CIA ordered a record twenty-one pizzas. Local Domino’s franchisee Frank Meeks told the Los Angeles Times a few months later: “The news media doesn’t always know when something big is going to happen because they’re in bed, but our deliverers are out there at 2 in the morning.” Iraq invaded Kuwait the following day.
Intelligence analysts call this mosaic theory: assembling individually innocuous facts into a material inference.
Historically, this has been a laborious and expensive task, and this has limited the risks to those being watched. Now, however, AI has industrialised mosaic analysis. Much of what a company emits, including job adverts, planning applications and executives’ Strava routes, can now be scraped and analysed.
The Mosaic in Action
A lot can be deduced from public data. Researchers at Oxford and armasuisse sampled corporate aircraft routes and M&A activity associated with 36 listed companies. In all seven acquisitions the study identified, the buyer’s aircraft had visited the target beforehand, on average 61 days earlier.
In another example, researchers found that investors could use satellite observations of retail car parks to predict corporate earnings, and formulate profitable trading strategies.
Routine releases can have outsized consequences. In late July 2017, developers mined prematurely released HomePod firmware, revealing the design of Apple’s next iPhone about six weeks before it was announced. And in 2018, a 20-year-old student showed that Strava’s aggregated exercise map drew the outlines of secret military bases. Six years later, Le Monde used public Strava profiles to trace 26 US Secret Service agents, deducing the hotel where President Biden was staying before his meeting with Xi Jinping.
The list of sources is long, including heat signatures captured through thermal satellite imagery, behavioural data such as web traffic, human writing such as compensation posts on Levels.fyi, and public records such as court filings.
The Industrialised Mosaic
Mosaic theory is not new. Satellite car-counting dates to about 2010, for example. What has changed is that much of the collection and analysis can now be automated at scale and low cost. A mid-cap engineering firm now faces hedge-fund-grade scraping and analysis.
Researchers at ETH Zurich reported in 2024 that, on a benchmark of 520 public Reddit profiles, an LLM achieved up to 85 per cent accuracy when inferring attributes such as location, income and sex. In that case, the models operated at about one-hundredth of the cost of human labellers.
The quality of mosaic signals is also improving. A single source such as pizza orders can be misleading. But with AI, snoopers can collate and analyse richer data from more sources, finding patterns which a human could not. As more of life moves online, both the number of signals and their granularity grow.
Use of such data is becoming commonplace. In Lowenstein Sandler’s annual survey of investment advisers at private fund managers, the proportion reporting use of alternative data (an input to mosaic analysis) rose from 62 per cent in 2023 to 90 per cent in 2025.
Conventional information-security techniques do not help here. The source material is public, commercially available or generated through routine activity, so controls designed to stop theft and leakage aren’t effective.
Reacting to the Mosaic
Executives are already reacting to this newly transparent environment. A 2023 Review of Financial Studies paper found that firms that expected their corporate disclosures to be read by AI altered their language and used more positive and excited vocal tones on earnings calls.
Companies are also addressing the corporate jet informational vapour trail. LVMH sold its corporate jet and shifted to charter planes after Bernard Arnault said tracking could reveal his movements. Nike placed two jets in the FAA’s flight-data filtering programme, making them harder to follow.
Another technique is to anonymise public signals. Apple has used foreign trademark filings, made through new subsidiaries, to make new product names harder to unearth. Meta used shell companies to disguise the nature of a Wisconsin data-centre project.
Information providers are also constraining large-scale aggregation. Strava restricts access to street-level detail and suppresses low-volume routes. LinkedIn uses technical systems to throttle or block automated scraping of public profiles.
Approaches to protecting secrets need to be holistic. For example, protecting the movements of a single executive now means considering social media, calendars, fitness apps, conference listings and flight trackers together, not separately.
Firms can also turn the tables on competitors. US courts make a distinction between scraping public web pages and hacking into restricted systems. Scraping public pages is generally not treated as illegal computer access, although contracts and other laws may still restrict collection or use.
The Mosaic Playbook
• Red-team your own exhaust. Have an LLM parse your job adverts, regulatory filings, public code, patents and other exhaust. An industry has been built around this capability so it can also be outsourced. Make sure to repeat periodically as patterns change.
• Clarify accountability. One of the reasons that mosaic analysis works is an asymmetry of approach. A snooper consolidates the available data about a company across all channels, whereas within the company accountability is diffuse, spanning communications, HR, investor relations, legal, security and more. The obvious choice to own the risk is the Chief Information Security Officer (CISO), but many are overloaded with AI-driven cyber threats.
• Treat routine publications as disclosures. Build a map that helps you understand which publications are connected to which secrets. Then score outbound content for risk before publication.
• Minimise sensitive signals. Around undisclosed initiatives such as M&A or business building, coordinate hiring, procurement, planning and travel so that little can be inferred from the sequence. Abstract the truth (Amazon code-named its twenty shortlisted headquarters cities after employees’ dogs). Protect the digital footprints of executives.
• Turn the tables. Use these techniques on competitors if for no other reason than to benchmark your own exposure against theirs. This is becoming a normal corporate function.
Monday Morning Actions for Executives
• Understand your exposure. Ask for a read of your own mosaic footprint.
• Vet outbound comms. Review job adverts, filings and announcements.
• Opt-out where possible. For example, ask data brokers to remove senior leaders’ details.
In June 2025, the Pentagon Pizza Report social media account reported a surge at pizzerias near the Pentagon before Israel’s strikes on Iran. The Pentagon disputed its significance, but the episode showed how easily public signals can now be collected and amplified. In the thirty-five years since the original event, mosaic theory has been transformed from an art form into an industry.
Questions for the Board
• Do we know what our public exhaust is revealing about us?
• Do we read our competitors’ exhaust as systematically as they read ours?
• Do we treat routine publications (e.g., job adverts) as disclosures?
Sources
• Slice of Life: Pizza Orders Soar in D.C.
• On the Capital Market Consequences of Big Data: Evidence from Outer Space
• HomePod’s Firmware and the Next iPhone
• A global heat map for joggers is exposing sensitive US military information
• Biden and Trump Put in Danger by Secret Service Agents: Watch the Second Episode of StravaLeaks
• Beyond Memorization: Violating Privacy via Inference with Large Language Models
• AI’s Integration Into Alternative Data Fuels New Opportunities and Challenges
• How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI
• World’s Second-Richest Man Bernard Arnault Sells Private Jet So Climate Activists Can’t Track Him
• We Reported on Nike’s Extensive Use of Private Jets. The Company Just Made It Harder to Track Them.
• Claiming Priority to Submarine Trademark Applications—A Curious Little Loophole
• At Least Four Wisconsin Communities Signed Secrecy Deals for Billion-Dollar Data Centers
• After Exposing Secret Military Bases, Strava Restricts Data Visibility
• hiQ Labs, Inc. v. LinkedIn Corp.
• Amazon Used Code Names of Employees’ Dogs to Keep HQ2 Finalists Secret
• Did Busy Pizza Shops Really Predict US Airstrikes on Iran?