Executive summary:
State-sponsored cyber intrusions and cyberattacks against the U.S. are sophisticated and difficult to detect. These adversarial gray zone tactics often go unnoticed for months because current detection and attribution technologies are outdated. Fast detection requires the fusing and analysis of many disparate data sources to tease out patterns that go undetected by human analysis.
Cognitive Hive AI (CHAI) applies a system-of-systems approach to APT detection. Its modular architecture deploys specialized AI components that analyze patterns across network telemetry, infrastructure changes, malware behavior, open-source intelligence, and more. Each component maintains auditable decision trails, enabling clear attribution of state-sponsored campaigns.
To discuss a CHAI implementation for your organization, contact Talbot West today.
Advanced persistent threats (APTs) are cyberattackers who are sponsored by a nation who is an adversary of the United States.
To illustrate: In October 2024, a Chinese state hacker group known as Salt Typhoon infiltrated major U.S. telecommunications providers, including AT&T, Verizon, and T-Mobile, to access sensitive communications and wiretap data. That same month, Chinese intelligence compromised phones belonging to senior staff in the Trump-Vance and Harris-Walz presidential campaigns. Meanwhile, Russian operatives gained access to critical infrastructure.
APT operations rarely occur in isolation. State actors typically deploy cyber intrusions as one component of broader gray zone warfare campaigns—coordinated actions that stay below the threshold of conventional military conflict while advancing strategic objectives. For example, when China targets defense contractors, cyber operations often complement human intelligence collection, academic partnerships, and joint ventures. Each attack vector appears legitimate in isolation. The strategic impact emerges only by correlating activity across multiple domains.
These campaigns are sophisticated, persistent, and increasingly difficult to attribute. The financial toll is massive; Chinese intellectual property theft is estimated to cost U.S. organizations hundreds of billions annually.
It’s time for the U.S. to develop better gray zone detection and attribution capabilities. This article explores how to do that in the cybersecurity realm.
APT campaigns often go undetected for months or even years. In 2024, FireEye reported average dwell times—the period between initial compromise and detection—of 204 days in Asia-Pacific, 177 days in EMEA, and 71 days in the Americas. These extended periods allow attackers to thoroughly map networks, exfiltrate data, and establish persistent access for future operations.
Several factors make APT detection uniquely challenging. First, the sheer volume of potential security events overwhelms human analysis. A typical enterprise generates millions of daily log entries across networks, endpoints, and applications. APTs deliberately structure their activities to blend into this noise, spreading operations across extended timeframes to avoid sudden spikes in activity.
Second, sophisticated actors increasingly use legitimate tools and processes to conduct malicious activities, a tactic known as “living off the land.” For example, when Russian operators compromise a network, they often deploy standard administrative tools such as PowerShell and WMI rather than custom malware. Living off the land makes distinguishing malicious from legitimate activity extremely difficult because the tools and commands look identical to normal system administration.
Third, state actors employ counter-forensics to hide their tracks. They carefully modify or delete log files, use memory-resident malware that leaves no traces on disk, and route traffic through compromised systems to obscure their origin. Some APTs even deploy tools to actively monitor an organization's security systems, adjusting their techniques to avoid detection.
Cybersecurity tools remain largely reactive, searching for known malware signatures or obvious policy violations. While this approach can identify common cybercrime, it proves ineffective against sophisticated state actors who craft unique tools and carefully mimic legitimate activities.
Security information and event management (SIEM) platforms attempt to address this gap by correlating data across security systems. However, these platforms typically operate within narrow time windows and lack the analytical depth to identify long-term patterns characteristic of APT campaigns. When state actors spread activities across months while staying below alert thresholds, SIEM correlation rules fail to detect the broader pattern.
Even advanced endpoint detection and response (EDR) tools struggle with sophisticated state actors. While EDR can identify unusual process behavior, it lacks visibility into broader patterns of activity across an organization. A series of seemingly legitimate administrative actions might only appear suspicious when viewed alongside changes in network traffic, user behavior, and external connections.
Threat intelligence platforms provide valuable insights into known adversary tools and techniques but often miss novel approaches. APT groups regularly modify their malware, shift infrastructure, and develop new ways to bypass security controls. By the time threat intelligence identifies new indicators of compromise, sophisticated actors have already altered their methods.
Manual analysis by security teams cannot scale to the challenge. The most skilled analysts can’t process the volume of security data generated. More importantly, humans find it extremely difficult to identify subtle patterns distributed across months of activity in multiple security domains.
These limitations create a fundamental mismatch between defensive capabilities and adversary sophistication. While state actors orchestrate carefully planned campaigns combining multiple techniques over extended periods, defenders remain largely confined to looking for individual indicators of compromise within short time windows.
APT campaign attribution involves more than simply identifying attack patterns or malware signatures. Modern state actors deliberately structure their operations to create layers of deniability. We need to get better at attributing these campaigns so that we can have more effective deterrence.
Most of our adversaries mask their gray zone cyberactivities by using proxy forces, usually in the private sector. Often, these proxies are known cybercriminal organizations that also work for the respective nation to advance its strategic objectives.
For example, North Korea's Lazarus Group conducts financially motivated cybercrime alongside strategic espionage, blurring the lines between criminal and state-sponsored activity. China increasingly outsources cyber operations to ostensibly independent contractors who maintain just enough separation to create deniability.
APT groups route traffic through compromised systems across multiple countries, leverage legitimate cloud services, and rapidly shift their infrastructure. Salt Typhoon's 2024 telecommunications campaign, for instance, used infected enterprise systems in six different countries to proxy their communications.
Technical indicators are unreliable since tools and infrastructure can be deliberately manipulated to point to the wrong actor. State actors deliberately plant false indicators linking their activities to other nations. They reuse tools from other groups, mimic their tradecraft, and time operations to coincide with their targets' working hours.
APT actors regularly change methods. An APT group might maintain consistent objectives while completely overhauling their technical approach between campaigns.
APT perpetrators often conduct operations during their targets' working hours rather than their own. Chinese groups have been observed timing their activities to U.S. East Coast business hours to blend in with normal traffic.
The above attribution challenges compound each other. When an APT group combines proxy operations, multinational routing, and deliberate false flags while constantly evolving their techniques, current analysis methods hit a wall. Each additional layer of deception exponentially increases attribution complexity.
This complexity explains why APT operations often go unattributed or are attributed well after they've achieved their objectives. The time between initial detection and confident attribution is often several months or even more, which gives adversaries ample time to accomplish their goals.
When we talk about AI for cybersecurity purposes, we're talking about a broad ecosystem of technologies, not just large language models. This ecosystem encompasses neural networks, computer vision systems, quantitative analysis engines, natural language processing, and more. Each brings distinct capabilities to the challenge of identifying state-sponsored campaigns.
Network behavior analysis leverages multiple AI approaches for anomaly detection. Isolation forests identify outliers in network traffic patterns, while autoencoders learn to spot deviations from normal system behavior. Recurrent neural networks analyze sequences of events over time to detect subtle changes that might indicate APT activity. These models can be configured to adapt continuously to learn new patterns of legitimate business operations while flagging potential threats.
Large language models (LLMs) and other transformer architectures can contextually understand unstructured data such as emails and system logs. These models' attention mechanisms help identify social engineering attempts. Small language models (SLMs) trained on specific security datasets can often outperform general-purpose LLMs at detecting domain-specific threats.
For analyzing system behavior, specialized neural networks can track command sequences and process relationships. Long short-term memory (LSTM) networks are particularly effective at detecting living-off-the-land techniques because they can detect suspicious patterns in legitimate administrative tool usage over extended periods. Convolutional neural networks can analyze process memory patterns to detect fileless malware that traditional tools miss.
Cross-domain correlation requires multiple specialized AI components working in concert. Graph neural networks map relationships between network entities and events. Temporal convolutional networks process time-series data from multiple sources. Reinforcement learning models help prioritize which patterns warrant deeper investigation. Together, these technologies can identify coordinated campaigns that would be invisible to any single detection method.
This multi-domain approach demands sophisticated orchestration. The AI components must share insights while maintaining independence, correlate findings without false positives, and provide clear evidence chains for attribution.
While AI technologies offer powerful capabilities for APT detection, AI security products struggle to deliver on this potential. Their monolithic architectures create fundamental constraints that state actors regularly exploit.
Organizations need to assemble specific combinations of AI capabilities based on their operational focus, threat exposure, and desired detection granularity. A defense contractor might need a detailed analysis of supply chain compromise attempts, while a telecommunications provider requires broad monitoring of infrastructure integrity. Meanwhile, an intelligence agency may want higher-level visibility into multiple attack vectors and how they intersect.
Rather than deploying unnecessary capabilities or missing critical ones, organizations should be able to configure their AI defenses precisely. Yet monolithic AI products offer fixed feature sets that rarely align with actual operational requirements.
Most AI security products also operate as "black boxes," making decisions through opaque processes that resist scrutiny. When these systems flag potential APT activity, security teams cannot examine their logic or understand how they reached their conclusions. This opacity creates serious risks in national security contexts where each alert must be explainable and every action must leave a clear audit trail.
While vendors periodically release updates, the core capabilities of each product remain largely fixed. This rigidity creates a fundamental mismatch with modern APT campaigns, where state actors constantly refine their techniques and develop new evasion methods. By the time vendors push new detection capabilities, sophisticated adversaries have often moved on to different attack patterns.
Different AI tools often use incompatible data formats, conflicting APIs, and isolated processing frameworks. Even when organizations manage to connect multiple tools, the resulting systems operate more like a collection of independent sentries than a coordinated defense force. This fragmentation creates seams between systems that sophisticated attackers can exploit.
Many AI products demand specific infrastructure configurations or cloud connectivity that prove impossible in classified environments. Others require extensive retraining to handle new data sources or threat types, making rapid adaptation impractical. These operational limitations often force teams to compromise between security requirements and technological capabilities.
Rather than deploying monolithic products with fixed capabilities, CHAI creates ensembles of specialized AI modules that work together like analysts in a security operations center. Each module contributes unique capabilities while working in concert.
For detecting sophisticated APT campaigns, CHAI can deploy module types such as the following:
For attribution of state sponsorship, CHAI can leverage other types of modules, such as:
First, organizations can configure exactly the capabilities they need at every level. A defense contractor might focus on granular detection of supply chain compromise attempts alongside attribution modules tuned to specific state actors. The Department of Defense might require strategic modules correlating cyber intrusions with signals intelligence to identify coordinated campaigns. A critical infrastructure provider might emphasize early detection of sabotage attempts combined with rapid attribution capabilities. As requirements evolve, new modules can be added or analysis levels adjusted without disrupting existing capabilities.
Second, CHAI maintains clear accountability through auditable decision trails. When the system flags potential APT activity, analysts can examine exactly how different detection modules spotted the threat. When attributing to a specific actor, they can trace how the system connected infrastructure patterns, code characteristics, and operational timing to establish state sponsorship. This transparency builds trust in detection and attribution while supporting decisive action.
Third, individual modules can be updated independently as adversary tactics evolve. Rather than waiting for monolithic product updates, security teams can deploy targeted improvements for new attack methods, false flag techniques, or proxy operations. This agility helps organizations stay ahead of sophisticated actors who regularly modify their approaches.
Fourth, CHAI's standardized interfaces enable modules to share insights while maintaining independence. Detection modules can feed findings directly to attribution modules, which correlate patterns across multiple incidents. When multiple organizations using CHAI detect similar patterns, their systems can collaboratively build stronger detection and attribution cases while maintaining appropriate security controls.
Most importantly, CHAI can be deployed flexibly based on security requirements. Modules can operate in air-gapped environments, coordinate across classification boundaries, and adapt to operational constraints while maintaining rigorous standards for detection confidence and attribution evidence.
At a higher level, CHAI's "queen bee" coordination module orchestrates this complex ensemble of detection and attribution capabilities.
Unlike other security tools that simply broadcast alerts, CHAI enables true two-way interaction between analysts and AI modules. Analysts can query the system in natural language to probe specific findings or explore potential patterns. For example:
CHAI's large language models translate these natural queries into precise analytical tasks, coordinating multiple modules to deliver comprehensive answers. The system can generate clear narratives explaining its findings, create visualizations of complex relationships, and suggest next steps for investigation.
This interactivity extends beyond simple queries. When CHAI's modules encounter ambiguous patterns or face decisions with significant operational impact, they can pause to consult human experts. The system presents its analysis, explains its uncertainty, and incorporates analyst feedback to improve future decisions.
For example, if correlation modules spot a potential false flag operation but lack confidence in attribution, they might engage threat intelligence analysts to validate their findings. The system can explain exactly which patterns triggered its suspicion and what additional evidence would help confirm attribution. This creates a genuine partnership between human expertise and AI capabilities that grows more sophisticated over time.
Through this combination of modular detection, attribution capabilities, and interactive analysis, CHAI helps organizations identify and respond to state-sponsored campaigns with unprecedented speed and confidence.
Following the Department of Defense's Modular Open Systems Approach (MOSA) principles, CHAI enables integration with existing security infrastructure while supporting nested capabilities from tactical to strategic levels.
This system of systems architecture allows organizations to build security capabilities incrementally while maintaining clear paths for expansion. A CHAI implementation might begin with modules focused on specific security functions, such as analyzing network traffic patterns or monitoring system behaviors. Through standardized interfaces, these modules integrate with existing security tools and feed insights to higher-level analysis frameworks.
These tactical capabilities can then nest within broader operational ensembles. For instance, network monitoring modules might connect with endpoint detection systems and threat intelligence platforms to create comprehensive cyber defense frameworks. These frameworks can, in turn, feed into theater-wide security architectures that correlate cyber patterns with signals intelligence, satellite data, and physical security monitoring.
Each level maintains operational independence while contributing to higher-order insights. When a tactical module identifies potential APT activity, that intelligence flows upward through increasingly sophisticated analysis layers. Higher-level modules correlate these tactical indicators with broader patterns of state actor behavior, enabling the detection of coordinated campaigns that would be invisible at any single level.
MOSA compliance extends this nesting capability beyond any single organization. CHAI frameworks can share insights across organizational boundaries while maintaining appropriate security controls. This interoperability is especially valuable when tracking sophisticated state actors who distribute their activities across multiple targets.
Cognitive Hive AI enables continuous evolution. Organizations can add new capabilities, adjust analysis levels, or integrate emerging technologies without retooling the entire system. The AI ensemble grows more sophisticated over time while maintaining the clear interfaces and accountability that high-stakes security operations demand.
Here's how CHAI might work for detecting and attributing a sophisticated state-sponsored cyberintrusion campaign targeting critical telecommunications infrastructure.
At the tactical level, specialized CHAI modules analyze distinct data streams:
Initial indicators emerge when network analysis detects subtle anomalies in DNS request patterns. Simultaneously, system monitoring identifies a previously unknown backdoor using legitimate Windows services for persistence. These tactical findings propagate through CHAI's standardized interfaces to higher-level analysis.
Correlation engines then identify broader patterns: the DNS techniques match methods used by known state actors, while the backdoor shares code characteristics with malware from previous campaigns. Graph analysis reveals similar intrusion patterns across multiple telecommunications providers, suggesting a coordinated effort to establish persistent access.
At the strategic level, CHAI's analysis reveals the full campaign.
Through this nested analysis, CHAI maintains clear evidence chains showing how each component contributed to attribution. Security teams can examine exactly how the system correlated tactical indicators into identification of a state-sponsored intrusion campaign.
By correlating insights across multiple levels, while maintaining clear audit trails, CHAI helps identify and attribute state-sponsored intrusions before attackers can achieve their objectives.
When analysts need deeper insight, they can query CHAI directly to steer its focus and derive guided intelligence.
As the investigation continues, CHAI's components adapt their detection parameters based on new findings.
When attackers modify their techniques, CHAI's adaptive modules detect the changes immediately, whereas static security tools would miss the evolution.
Higher-level analysis combines tactical findings with broader context.
This synthesis exposes the campaign's strategic objective: establishing persistent access to telecommunications infrastructure for future operations.
The technological advantage that nation-state adversaries currently hold in cybersecurity poses an existential threat to U.S. national security, critical infrastructure, and economic competitiveness. When state actors can maintain undetected network access for months or years while systematically extracting intellectual property and positioning for future operations, traditional concepts of deterrence break down. The accelerating use of AI by our adversaries to enhance their cyber campaigns only increases this strategic gap.
Yet the same technologies that make modern APT campaigns so effective also enable sophisticated new defensive capabilities. By combining modular AI architectures with system-of-systems integration approaches already proven in other defense domains, the U.S. can regain its strategic advantage. The key lies in deploying AI frameworks that match our adversaries' adaptability while maintaining the explainability and control that national security applications demand.
Organizations across government, defense, and critical infrastructure sectors have an opportunity to strengthen their defenses against state-sponsored campaigns. Those who move quickly to implement modular, adaptive AI architectures will be best positioned to detect and attribute sophisticated attacks before they achieve their objectives.
Talbot West guides organizations in deploying CHAI frameworks tailored to their specific security requirements and operational constraints. To explore how modular AI can enhance your APT detection and attribution capabilities, contact us for a detailed capability assessment.
While cybercriminals typically focus on immediate financial gain through ransomware or data theft, APTs conduct long-term strategic campaigns targeting specific organizations or sectors. They employ sophisticated techniques, maintain persistent access, and often operate as part of broader state-sponsored initiatives rather than pursuing purely financial objectives.
Most APT campaigns begin with sophisticated spear-phishing attacks targeting specific employees, supply chain compromises, or exploitation of public-facing vulnerabilities. These initial access vectors are carefully chosen based on detailed reconnaissance of the target organization and its personnel.
Living-off-the-land attacks use legitimate system administration tools in ways that appear normal when viewed in isolation. Traditional security tools struggle to distinguish between legitimate administrative actions and malicious use of the same tools without a broader context across time and systems.
APTs use sophisticated persistence mechanisms including modified system files, credential theft, and compromised software update processes. They carefully monitor target networks to ensure their activities stay below detection thresholds while establishing multiple redundant access methods.
States increasingly outsource cyber operations to criminal groups to maintain deniability. These groups conduct both profit-motivated cybercrime and state-directed campaigns, making it difficult to distinguish between criminal and state-sponsored activity.
APTs employ sophisticated techniques to bypass EDR including in-memory malware, rootkit capabilities, and process injection methods. They also carefully study EDR products to understand detection methods and develop evasion techniques.
APTs deliberately vary their techniques, infrastructure, and timing across different targets. They also use different proxy groups and false flag operations, making it difficult to connect seemingly unrelated incidents into a clear picture of coordinated campaigns.
State actors compromise trusted vendors, software providers, and service companies to gain access to their customers' networks. These supply chain attacks are particularly effective because they exploit existing trust relationships and legitimate access paths.
Cyber operations often support broader gray zone campaigns including economic pressure, influence operations, and technology transfer efforts. Understanding these relationships requires correlation across multiple intelligence domains beyond just technical indicators.
State actors increasingly leverage AI to automate reconnaissance, improve social engineering, generate convincing phishing content, and identify potential vulnerabilities. They also use AI to better evade detection by learning normal network behavior patterns.
Effective APT defense requires close collaboration between government agencies and private sector organizations to share threat intelligence, coordinate responses, and protect critical infrastructure. No single organization can address sophisticated state actors alone.
State actors leverage legitimate cloud services for command and control, data exfiltration, and attack staging. This allows them to blend malicious traffic with normal business operations while exploiting the trust placed in major cloud providers.
APTs employ sophisticated deception including false flags, shared tools between groups, proxy operations, and multi-stage infrastructure. They also deliberately plant misleading indicators to complicate attribution efforts.
Most APT compromises are discovered through external notification from law enforcement or security researchers, anomaly detection by advanced security tools, or during incident response to other security events. Direct detection of initial compromise remains rare.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
