Cyberattacks are not rare in the construction, glass and glazing industries. While most go unreported, some, such as C.R. Laurence's ransomware attack in late October 2024, remind us that no company is safe. The threat of cyberattacks to the construction industry will only grow as the industry further embraces automation, artificial intelligence and robotics.

That said, this article provides five actionable steps that construction companies can implement to deploy AI-based threat intelligence in the construction industry.

Construction companies store vast amounts of valuable data across temporary job sites and remote networks, including proprietary building designs, financial records, and client information. When cybercriminals successfully breach these distributed systems, the damage goes far beyond data theft. Million-dollar projects grind to a halt when project management platforms get encrypted by ransomware.

Payment systems freeze, blocking critical vendor transactions. Communication channels between contractors and suppliers shut down completely, triggering cascading delays that compound costs daily.

Construction firms also handle government contracts requiring strict compliance frameworks, making breaches particularly costly. This combination of valuable intellectual property, distributed vulnerabilities, and severe operational impacts makes construction companies attractive targets for sophisticated cybercriminals seeking maximum financial gain.

Construction operations create ideal conditions for cyberattacks due to their distributed nature and resource constraints. For instance, some companies manage dozens of temporary job sites where project managers access critical systems from trailers with minimal security controls. Field supervisors connect through unsecured mobile networks, creating multiple entry points that bypass traditional perimeter defenses.

Meanwhile, Building Information Modeling (BIM) systems contain detailed facility blueprints valuable to both competitors and nation-state actors. Most construction firms lack dedicated cybersecurity teams, forcing project managers to handle IT security alongside their primary responsibilities. The industry's communication-heavy workflows, involving constant email exchanges between contractors, suppliers, and clients, enable sophisticated impersonation attacks.

These operational realities, including distributed networks, valuable data repositories, limited security expertise, and intensive digital communications, combine to create critical vulnerabilities that demand proactive threat intelligence rather than reactive security measures.

Legacy security approaches fail construction companies because they assume fixed office environments with centralized IT infrastructure. Construction operates differently as it includes temporary job sites, changing network configurations, and distributed teams. All these make perimeter-based models obsolete.

Construction firms generate massive data volumes through BIM systems, project management platforms, and communication tools. Yet they lack the resources to monitor security events across these systems. Traditional SIEM platforms require dedicated analysts that most construction companies cannot afford.

Modern attacks exceed legacy detection capabilities. Cybercriminals research project details and impersonate known contacts, crafting social engineering attacks that bypass signature-based filters. These targeted campaigns require behavioral analysis capabilities beyond traditional tools.

Enterprise security solutions demand investments exceeding construction budgets. Platforms designed for large corporations include unnecessary complexity that small and medium firms cannot justify. This gap drives the need for accessible, AI-powered threat detection that scales with construction operations without requiring extensive security expertise.

These challenges make AI-based threat intelligence essential. Here are five ways construction companies can implement these capabilities:

Business email compromise represents one of the most common attack vectors against construction companies. Cybercriminals target project managers and executives with sophisticated impersonation attacks designed to initiate fraudulent wire transfers or steal sensitive project information. Traditional password-based email security provides insufficient protection against these AI-enhanced threats.

AI-powered email security combined with behavioral analysis provides the most effective defense against sophisticated attacks. Modern AI platforms learn normal communication patterns for each user, identifying anomalies that suggest compromise or manipulation. These systems analyze sender reputation, communication timing, language patterns, and contextual relationships to detect threats that bypass traditional filters.

Construction companies can implement AI-based threat detection through cloud platforms like Microsoft 365 or Google Workspace enhanced with specialized behavioral AI solutions. When properly configured, these systems provide automated threat response, quarantining suspicious messages before users can interact with them while maintaining legitimate business communications.

Construction companies need proactive threat intelligence that anticipates attacks before they occur. Traditional reactive approaches leave companies vulnerable during the critical window between threat emergence and signature updates. AI-driven threat intelligence platforms aggregate global threat data, identifying patterns that predict future attacks.

Machine learning algorithms analyze threat indicators across the construction industry, recognizing attack patterns targeting specific project types or contractor relationships. These systems identify emerging threats like new ransomware variants or phishing campaigns before they reach your organization. Predictive analytics enable preemptive defense adjustments, closing vulnerabilities before exploitation.

Construction firms should integrate threat intelligence feeds with existing security infrastructure, creating automated response workflows. When the system identifies threats targeting peer organizations, it automatically adjusts security controls, updates email filters, and alerts security teams. This proactive approach reduces dwell time and prevents successful breaches.

Construction companies need rapid incident response capabilities, but cannot maintain 24/7 security teams. AI-powered automation bridges this gap, providing intelligent response actions that contain threats before human intervention. Machine learning models analyze incident patterns, learning optimal response strategies for different attack types.

Automated incident response systems perform immediate containment actions when detecting threats. They isolate compromised accounts, revoke suspicious OAuth tokens, quarantine malicious emails, and reset compromised credentials automatically. AI orchestration coordinates response across multiple systems, ensuring comprehensive threat elimination without manual coordination.

For construction companies, AI-driven response means attacks detected at 2 AM on remote job sites get immediate attention. The system documents all actions for compliance reporting while escalating complex incidents to human analysts. This approach reduces incident response time from hours to minutes, minimizing operational impact and financial losses.

Construction projects generate thousands of emails, documents, and messages daily. Natural language processing analyzes this communication flow, identifying social engineering attempts that exploit construction-specific terminology. NLP algorithms understand context, detecting subtle manipulation tactics that fool human readers.

AI language models trained on construction communications recognize legitimate project discussions versus phishing attempts. They identify unusual payment requests, fake change orders, and impersonated vendor communications. The technology analyzes sentiment, urgency, and authority markers that indicate business email compromise attempts targeting construction workflows.

Implementation involves deploying NLP-enhanced security across email, collaboration platforms, and document management systems. The AI system learns your organization's communication patterns, adapting to project-specific terminology and relationships. This creates a semantic security layer that protects against sophisticated attacks crafted specifically for construction targets.

Construction companies work with dozens of subcontractors and suppliers, each representing potential supply chain risks. AI-driven vendor intelligence continuously monitors these relationships, detecting compromised vendor accounts and fraudulent communications. Machine learning establishes behavioral baselines for each vendor, identifying deviations that signal compromise.

Vendor risk intelligence platforms analyze communication patterns, payment behaviors, and security postures across your supplier network. They detect invoice fraud, identify compromised vendor emails, and prevent payment redirection attacks. AI algorithms correlate threat data across multiple vendors, identifying coordinated attacks targeting construction supply chains.

The system provides real-time vendor risk scores, alerting procurement teams to security concerns before approving transactions. When detecting compromised vendor accounts, it automatically blocks suspicious communications while maintaining legitimate business operations. This protects both your organization and your vendors from financial losses.

Abnormal understands the unique challenges facing construction companies, and its behavioral AI platform addresses these by detecting sophisticated threats traditional tools miss, without adding operational complexity.

Berkeley Group, a leader in sustainable community development, faced relentless phishing and BEC attacks that continued penetrating their defenses despite having Microsoft 365 native tools, and a secure email gateway. The sophisticated impersonation attacks targeting executives threatened both the company's financial assets and its reputation.

After implementing Abnormal's behavioral AI solution, Berkeley Group achieved the following:

• 5-minute POV setup

• 366 SOC hours saved in the first 4 months on user email reports

• 18.9% of attacks stopped in 90 days

The platform's behavioral AI learns normal communication patterns to detect anomalies that suggest compromise, while VendorBase intelligence monitors the supply chain for compromised vendor accounts. Head of Security Ash Hughes emphasized the operational impact: "The context Abnormal provides, indicators within emails and who's interacted, delivers huge efficiencies. Whether QR code attacks or new phishing strategies, Abnormal's speed in detecting emerging threats has been fantastic."

Want to implement AI-based security to protect your construction projects? Request a demo to or read customer stories to discover how Abnormal's behavioral AI safeguards against sophisticated threats without straining budgets.