Executive summary:
Every successful gray zone campaign exploits gaps in detection and attribution. Our adversaries deliberately design operations to avoid discovery, orchestrating complex campaigns that stay hidden while steadily eroding American interests. Without robust detection and attribution capabilities, the best deterrence doctrine and response options become irrelevant.
The U.S. needs detection capabilities that can rapidly identify coordinated campaigns while they're still emerging. This requires advanced systems that can correlate seemingly unrelated events across multiple domains into clear pictures of hostile action. More importantly, we need to attribute these campaigns to their sponsors quickly enough to enable meaningful responses.
Cognitive Hive AI offers a MOSA-compliant framework for detecting and attributing gray zone aggressions. To learn more, contact Talbot West for a consultation.
Detection forms the foundation of all gray zone defense. Without the ability to identify hostile campaigns while they're unfolding, even the most sophisticated deterrence frameworks and response options become meaningless. Our adversaries understand this reality, which is why they invest heavily in tactics designed to exploit detection gaps.
China's systematic technology acquisition campaigns offer a prime example. Rather than launching obvious cyberattacks, Chinese operators carefully distribute efforts across multiple attack vectors:
Each component appears innocent in isolation. The strategic damage only becomes clear when analysts correlate thousands of seemingly unrelated activities into a comprehensive picture of coordinated technology transfer. By the time such campaigns are detected, critical capabilities have often already been compromised.
Russia's influence operations show similar sophistication in detection avoidance. Instead of crude propaganda, Russian campaigns carefully layer multiple subtle tactics:
The true impact emerges from how these various components reinforce each other. Individual activities stay below detection thresholds while the cumulative effect steadily saps U.S. strength.
Even with rapid detection, an effective gray zone deterrence effort requires confident attribution. Adversaries have mastered the art of plausible deniability, and structure gray zone campaigns to obscure state involvement. This exploitation of attribution gaps has become a defining feature of modern conflict, allowing hostile actors to steadily advance strategic aims while avoiding responsibility for their actions.
For example, Russia's 2016 election interference campaign orchestrated influence operations through multiple cut-outs to maintain deniability. The Internet Research Agency, ostensibly a private company, deployed thousands of fake personas across social media platforms. When confronted with evidence, Russian officials pointed to the company's private status while continuing to fund its operations. Only through extensive digital forensics and intelligence operations could investigators definitively trace the campaign back to state sponsors.
As Dr. Gregory Bernard of the Naval Postgraduate School notes, we already have a model for stripping away such deniability in another domain:
If a nation allows nuclear materials to fall into the hands of non-state actors and they detonate a nuclear device, those materials will be traced back to their source through nuclear forensics and that nation will be held responsible. We need to let nations know that we will trace cyberattacks and other gray zone aggressions back to their source and attribute them to the nation from which they originated. (Dr. Gregory Bernard, 11/06/2024 video conference)
Attribution must extend across all domains of gray zone conflict. Just as nations understand they cannot escape responsibility for their nuclear materials, they must understand they cannot hide behind proxy forces, seemingly independent actors, or complicated attribution chains.
America's defense architecture excels at monitoring military movements and terrorist networks, yet consistently misses coordinated gray zone campaigns. The reason lies not in our intelligence gathering quality, but in how we process and correlate information across domains.
Our adversaries exploit gaps between military, intelligence, law enforcement, and civilian monitoring systems. A cyber intrusion targeting defense contractors generates alerts in one system, suspicious capital flows appear in another, academic recruitment programs draw attention from a third. Each signal stays isolated until the broader campaign achieves its objectives.
hree vulnerabilities enable this exploitation:
While the third issue is beyond the scope of this article, the first two can be addressed via adaptive, modular AI systems—which is where Talbot West excels. If you’d like to explore how Cognitive Hive AI can help with the attribution and detection of adversarial gray zone campaigns, contact us for a free consultation.
The sophisticated nature of modern gray zone campaigns demands fundamentally new detection capabilities. Consider China's technology acquisition efforts: when analysts finally pieced together their semiconductor campaign, they discovered thousands of seemingly unrelated activities spanning academic partnerships, corporate acquisitions, and cyber operations. By the time these connections became clear, critical technology had already been compromised.
Preventing such losses requires detection systems with the following capabilities and attributes:
Without these core capabilities, even the most sophisticated defensive strategies become irrelevant. Adversaries will continue exploiting detection gaps to advance their interests while maintaining deniability.
The capabilities needed for effective gray zone detection exceed human analytical capacity. No team of analysts, however skilled, can process the massive data volumes, correlate across multiple domains, adapt to emerging threats, and maintain attribution trails at the speed required to keep pace with our adversaries.
AI will not fix all of the organizational and policy-related challenges that hamper the U.S. in the gray zone (see our “Vulnerabilities and solutions” article in this series), but it offers immediate opportunities to close critical detection gaps.
AI systems can process millions of events per hour to spot subtle patterns that would take human teams weeks or months to identify. It can maintain continuous vigilance across every domain where adversaries operate. It can simultaneously monitor academic publications, corporate registries, social media patterns, and cyber indicators—and correlate subtle signals into clear pictures of coordinated action. More importantly, it can adapt to new tactics in real time while maintaining complete audit trails for attribution.
Unfortunately, monolithic AI products lack the explainability, configurability, and adaptability that gray zone detection requires.
These fundamental limitations mean the U.S. needs new AI architectures specifically designed for the unique challenges of gray zone operations.
Gray zone defense requires precise sets of capabilities tailored to specific threats and operational environments. No standalone AI product, however sophisticated, can anticipate and include every capability needed for complex gray zone operations.
Even if an AI product managed to include every capability that gray zone detection would ever require, the product would be prohibitively overbuilt and needlessly complex.
Monolithic AI products are trained on historical data and deployed as complete systems. Once deployed, their capabilities are essentially fixed. Updating them requires retraining and redeploying the entire model—a major undertaking that can take months.
When adversaries craft campaigns specifically to exploit detection gaps or develop entirely new tactics, these rigid systems become increasingly ineffective. The operational reality demands detection capabilities that can be modified or expanded without retooling the entire system from scratch.
Monolithic AI systems operate as "black boxes," making it impossible to understand how they reach conclusions. This opacity creates serious problems for attribution that must withstand diplomatic and legal scrutiny.
When imposing costs on adversaries for gray zone activities, defenders need clear evidence chains that demonstrate state responsibility. Without explainable AI, detection findings lack the credibility needed for international response.
Cognitive Hive AI (CHAI) brings configurability, explainability, and rapid adaptation to gray zone detection through specialized modules. Its audit trails document how patterns are identified and attributed.
When paired with open source intelligence (OSINT), CHAI enables detection capabilities that often are on par with classified sources. Most importantly, it removes the human analysis bottleneck that occurs when processing massive data volumes from multiple streams. "Traditional intelligence relies heavily on classified sources," notes Ethan Wilson, a SIGINT expert with 8 years of Navy experience. "We've found that properly analyzed open source data can match classified intelligence for detecting gray zone activities, while removing the bottleneck of the human in the loop."
OSINT encompasses a wide range of available data sources and types. These include commercial satellite imagery, corporate registries, maritime tracking data, academic publications, environmental monitoring, and more. While each source provides value, the real power comes from cross-correlating multiple streams to reveal patterns that remain hidden when analyzing sources in isolation.
Maritime security represents a prime opportunity for CHAI to create a common operating picture with OSINT data. The arena faces mounting threats from state actors who deliberately operate below the threshold of military conflict (or who, in some hotspots, openly attack vessels). "The future of maritime domain awareness isn't just about better sensors, it's about better analysis," Wilson explains. "When we apply AI to combine vessel tracking, environmental data, and communication patterns, we start to see the whole picture rather than just individual pieces."
CHAI modules process separate streams—satellite imagery, vessel movements, port records, weather data—while correlating findings to reveal coordinated activities. "Some of the most valuable maritime intelligence comes from correlating non-obvious indicators," notes Wilson. "A change in regional IP traffic patterns, combined with shifts in vessel behavior and social media activity, can reveal coordinated activities that might otherwise go unnoticed."
The CHAI + OSINT pairing applies to all gray zone detection arenas as well:
A future article examines CHAI and OSINT deployment specifically for gray zone detection, including technical architectures and implementation strategies. To learn more about strengthening your detection capabilities through CHAI implementation, contact us for a consultation.
System of systems architectures enable rapid integration of new detection capabilities without disrupting existing ones. When adversaries develop novel tactics, defenders can deploy targeted detection modules rather than rebuilding entire systems. This adaptability enhances gray zone operations defense where threats constantly evolve.
MOSA enables defense organizations to rapidly field and update capabilities as threats evolve. Rather than waiting years for monolithic systems to move through traditional acquisition cycles, MOSA allows teams to quickly integrate new components through standardized interfaces. When vendors develop innovative technologies or threats demand new responses, organizations can deploy targeted upgrades without overhauling entire systems. Additionally, MOSA's emphasis on documented interactions and defined interfaces makes systems easier to test, maintain, and modify over time while reducing vendor lock-in.
Cognitive Hive AI is a MOSA-compliant framework for artificial intelligence deployment.
Cross-border attribution requires coordinated analysis by multiple nations. When detection systems in different countries use compatible architectures and standard interfaces, they can rapidly share relevant indicators while protecting sensitive sources and methods. This technical compatibility strengthens international attribution efforts.
At Talbot West, we champion interoperability for artificial intelligence deployment so that collaboration—between allied nations, between agencies, or between the public and private sectors—is easy.
Rather than attempting to break encryption, CHAI correlates metadata and pattern analysis across multiple domains. This allows the detection of coordinated campaigns even when individual communications remain encrypted. The modular architecture enables rapid deployment of new analysis capabilities as encryption methods evolve.
Focus on pattern correlation rather than individual actions. While any specific activity might have legitimate explanations, sophisticated detection systems reveal coordinated campaigns through statistical analysis of timing, targeting, and effects across multiple domains. This pattern-based approach helps separate hostile campaigns from routine operations.
CHAI's architecture emphasizes explainability and rapid adaptation rather than just modularity. Each component maintains clear audit trails of its detection logic while enabling quick updates as threats evolve. This combination of transparency and adaptability proves essential for gray zone defense.
Adversaries will attempt to craft activities that avoid known detection patterns. Modular architectures enable rapid deployment of novel detection capabilities through specialized components. This allows defenders to quickly close gaps without revealing their full detection capabilities.
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