Data Security for the
Transportation &
Hospitality Industries

Transportation data can be used to determine operational risks, enhance communication, secure proprietary data, and augment supply chain accessibility. Industries use this data in multiple ways, including predictive analytics and establishing more efficient cloud-based platforms. Detailed customer data can inform organizations as to what, when, and where customers are buying. This information can identify loyal and lost customers, popular and unpopular products and services, and the timelines and frequency of buying orders. Organizations can then use these insights to effectively improve customer satisfaction by changing service offerings or merchandising, improving targeted customer outreach, and preparing for the busiest times of the year. Analyzed data leads to greater efficiencies and shorter lead times.

The interconnectedness of the logistics industry creates multiple points where sensitive information is susceptible to a cyberattack. In addition to the ransom demand on data, disruption to transportation infrastructure is a major concern: traffic lights, toll booths, and electronic traffic signs. Interruption of ticket machines and fare gates. The potential for accidents, mass chaos, and even injuries or loss of life due to disruptions to critical infrastructure.

According to 2022 Logistics Industry Report - Forecast for 2023, a breakdown of logistics challenges include, among other things: 1. Managing complex systems (multitudes of integrations with partners, vendors, etc.) 2. Flexibility and scalability 3. Cost efficiency 4. Improved customer satisfaction 5. Compliance with global and local regulations for data protection, privacy, and security

Supply chains are part of a complex global system where data and information are shared through system integrations, thus creating multiple points where sensitive information is susceptible to a cyberattack. Valuable types of logistical data include: • Inventory data • Delivery and arrival dates/locations, • Operational data involving air, ground, and maritime cargo transportation A common tactic is criminals will break into a company’s system and hold the data for ransom until it receives payment. Companies must establish and publish a data privacy policy for both internal teams and customers. For companies storing data across multiple systems, a robust data protection platform and the use of cybersecurity tools and methods, including cross-border security measures can be used to secure PII without disrupting authorized access. Data protection methods include: • Encryption • Tokenization • Data backup tools • Firewalls • Password protection • Identity and access management • Intrusion detection and prevention software • Endpoint and network security tools

The list of privacy laws is growing every year. At present time, the following make up the top privacy laws affecting logistics data: Internationally ● General Data Protection Regulation (GDPR) ● Payment Card Industry Data Security Standard ● Australia’s amended Australia Privacy Act ● Brazil’s General Data Protection Law (LGPD) ● Chile’s Amended Consumer Protection Law ● Japan’s Personal Information Protection Act (PIPA) ● Kenya’s Data Protection Act, 2019 (DPA) ● Nigerian Data Protection Regulation, 2019 ● South Africa's Protection of Personal Information Act (POPIA) ● South Korea’s Personal Information Protection Act (PIPA) ● Qatar’s Personal Data Privacy Protection Law Within the United States ● California Consumer Privacy Act (CCPA) ● California's Privacy Rights Act, or CPRA ● Colorado Privacy Act ● Connecticut Data Privacy Act (CTDPA) ● Utah Consumer Privacy Act (UCPA) ● Virginia Consumer Data Protection Act (VCDPA)

The hospitality industry has lots of targets for cybercriminals. Often, a hospitality enterprise has multiple databases and devices containing Payment Card Information (PCI) and Personally Identifiable Information (PII) that malicious actors want to access. Safeguarding against these actors is vital for cybersecurity in transportation and related fields.