When Pittsburgh Regional Transit detected ransomware on December 19, 2024, rail service experienced temporary disruptions. PRT immediately launched an investigation, activated its cyber response team, notified law enforcement, and engaged third-party cybersecurity and data forensics experts. The attack exposed the transportation industry’s operational fragility to cyber threats.

Sophisticated attackers target transportation infrastructure through operational technology vulnerabilities rather than traditional IT systems. Congressional testimony confirms that aviation, rail, maritime, and logistics networks rely on complex digital systems creating extensive attack surfaces. Nation-state actors exploit weak authentication, insecure settings, and outdated protocols across systems, fleet management platforms, and logistics networks.

Traditional detection cannot identify previously unknown threats, leaving transportation exposed to zero-day attacks, advanced persistent threats, and AI-driven malware that continuously modifies signatures to evade detection. These campaigns prioritize operational disruption over data theft, potentially shutting down critical infrastructure that millions depend on daily.

Here are five steps that help implement AI-based threat intelligence in transportation:

Understanding your specific threat landscape helps security teams configure AI detection for the most likely attacks while ensuring comprehensive coverage across both operational and information technology environments. This targeted approach reduces false alarms and improves response times.

Monitor CISA advisories for vulnerabilities in transportation vendors to stay informed about supply chain risks. Additionally, focus detection efforts on common attack vectors: weak passwords, outdated security settings, and unpatched systems in your control systems and fleet management platforms. Nation-state actors specifically exploit these weaknesses to disrupt operations.

The right AI platform balances security requirements with operational needs, ensuring protection doesn't compromise service delivery. Platforms that connect seamlessly with control systems, fleet management tools, and logistics platforms protect operations without disrupting them.

Evaluate platforms using three key criteria, which include: Federal Compliance (platforms must integrate with operational technology and address sector-specific cyber threats effectively); Transportation Integration (systems should align with NIST frameworks that provide transportation security standards); and Advanced AI Capabilities (solutions need sophisticated threat detection designed specifically for critical infrastructure protection).

Transportation-specific training produces accurate alerts that security teams can trust. Teams spend less time investigating false positives and more time addressing genuine threats, improving overall security posture while maintaining smooth operations.

Feed your AI system real operational data: scheduled rail maintenance windows, airport peak traffic times, and seasonal shipping variations. This context helps distinguish between normal activities and actual threats. When AI understands that midnight system updates are routine for rail networks, it won't flag them as suspicious. Generic AI models create excessive false alarms because they don't recognize these normal transportation operations.

Risk-based automation ensures responses match actual threat levels. Critical threats receive immediate action while lower-priority alerts queue for review. This approach reduces response times for serious incidents without overwhelming teams with minor issues, maintaining both security and operational continuity.

Implement automated response using three priority categories: Critical Infrastructure Impact (threats affecting transportation operations and safety systems receive immediate automated response); Regulatory Compliance (incidents risking DOT, TSA, or other regulatory violations trigger rapid containment measures); and Operational Resilience (events impacting essential functions activate predetermined response protocols).

Regular updates ensure your AI defenses evolve faster than attacker techniques, maintaining strong protection against current and future threats. Continuous learning cycles adapt to emerging risks before they impact operations.

Build improvement processes using NIST frameworks for structured enhancement. Align with TSA guidance for sector-specific protection requirements. Monitor AI system performance regularly, measuring detection accuracy and response effectiveness. Create feedback loops that incorporate new threat intelligence into existing models. When new attack patterns emerge, update training data immediately. Track metrics like detection rates, false positive trends, and response times to identify improvement areas.

Transportation organizations find value in AI-powered threat intelligence that integrates with existing security infrastructure while providing proactive security capabilities that federal agencies now mandate. Abnormal works alongside existing transportation security systems to enhance threat detection capabilities without replacing current infrastructure.

Abnormal provides behavioral AI analysis of communication patterns and user behaviors, helping identify threats that exploit human elements within transportation security environments. This proves valuable for detecting social engineering attacks targeting airport personnel with security clearances or supply chain communication compromises that could disrupt logistics operations.

By focusing on behavioral anomalies and communication pattern analysis, AI platforms can enhance transportation security while maintaining operational efficiency. For organizations implementing these five steps, AI platforms that provide continuous learning capabilities and behavioral analysis help organizations stay ahead of evolving threats while maintaining operational efficiency.

AC Transit, serving 200,000 daily passengers across California's East Bay counties, needed to protect 1,100+ mailboxes and federal agency partnerships from sophisticated email attacks.

Abnormal's behavioral AI platform created the following impact:

• Detected and auto-remediated compromised accounts to maintain partner trust, protect reputation, and enhance security.

• Prevented sophisticated attacks from reaching executives, board of directors, partners, and employees.

• Saved 120+ employee hours each month by reducing the amount of graymail in inboxes.

The platform ensures AC Transit can focus on their core mission of moving people safely while Abnormal's AI handles sophisticated email threats that target public infrastructure.

Ready to implement AI-based threat intelligence? Learn how Abnormal helps transportation companies detect email threats while supporting broader cybersecurity objectives or book a demo today!