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In a world where your next-day delivery could hinge on lines of code and machine learning algorithms, logistics is no longer just about moving goods, it’s about moving data securely. The global supply chain has become both a marvel of AI-powered efficiency and a high-value target for cybercriminals. In 2024 alone, over 183,000 customers were affected by supply chain cyberattacks worldwide, a sharp contrast from the staggering 263 million impacted in 2019, yet still a sobering reminder that cyber threats are evolving, not disappearing. From counterfeiting to malware infections and drive-by compromises, the logistics sector has become a digital battlefield. Meanwhile, AI’s footprint is growing fast, the cargo drone market alone is projected to hit $17.88 billion by 2030, fueled by AI’s promise of faster, smarter, and more autonomous delivery systems. But this raises a critical question: Will AI drive the next wave of innovation in logistics, or open new doors for cyber threats to exploit? To learn more about this evolving landscape, The Cyber Express sat down with Dhruvil Sanghvi, Founder and CEO of LogiNext, a global leader in AI-driven logistics automation. In this wide-ranging conversation, Sanghvi delves into emerging cyber risks, why automation must be built with security at its core, and how different regions are tackling the cybersecurity puzzle in their own unique ways. The Rise of AI and the Growing Threat Landscape With the rapid adoption of AI in logistics, one might assume that technology is the answer to most operational problems. However, Sanghvi offers a measured perspective. "The more interconnected and intelligent supply chains become, the more vulnerable they are to attack vectors that exploit those very integrations," he says. I see the highest risks in API vulnerabilities, unsecured IoT devices across fleet networks, and AI models being fed malicious or manipulated data. Threat actors no longer just target servers, they aim for the data pipelines and learning systems themselves." Indeed, as companies push for seamless data flows, faster decision-making, and end-to-end visibility, every integration becomes a potential point of entry for cybercriminals. Building Security into Automation Automation is now table stakes in logistics. From warehouse robotics to predictive route planning, the industry thrives on operational speed. But what happens when security takes a backseat? "Efficiency and security aren't mutually exclusive, they must be engineered together," Sanghvi insists. "At LogiNext, we’ve built automation workflows that are permission-controlled and fully auditable. Automation without guardrails leads to incidents like the 2017 Maersk ransomware attack, which paralyzed global shipping. We believe automation should accelerate compliance, not bypass it." This philosophy of "secure automation" is gradually being adopted across the logistics landscape. Companies are increasingly embedding cybersecurity principles into their DevOps pipelines, ensuring that automation doesn’t equate to exposure. AI in Threat Detection: Early Radar, Not Autopilot The cybersecurity community has been abuzz with the potential of AI to detect and mitigate threats before they escalate. But can AI be trusted as the first, and only, line of defense? AI is essential for real-time anomaly detection and predictive threat intelligence," says Sanghvi. "While traditional systems wait for a signature, AI can detect patterns and proactively raise red flags. That said, it’s not yet mature enough to act without human validation in high-stakes environments. We see AI as the early-warning radar; human judgment is still the command center." AI's strength lies in its ability to parse enormous volumes of data and identify unusual behavior patterns that traditional tools often miss. But over-reliance without proper oversight could lead to false positives, or worse, missed attacks. One Size Doesn’t Fit All Operating across the US, UAE, and India, LogiNext has a global footprint, which gives Sanghvi a unique lens on how cybersecurity threats, and responses, differ across borders. "In the US, the emphasis is on regulatory compliance: SOC2, ISO/IEC 27001, CCPA," he explains. "In the UAE, there’s a stronger focus on infrastructure control and national security. In India, the challenge is scale: protecting millions of endpoints at low cost. Each region requires a tailored strategy, but one thing remains universal: ransomware doesn’t respect borders." This underlines the importance of localized cyber strategies that account for not just the technological landscape, but also regulatory frameworks and threat actor behaviors. Data Protection in the Logistics Sector “Data is the new oil” has become a truism in technology circles, but nowhere is it more evident than in logistics. Route data, customer information, delivery schedules, and warehouse analytics—all of it is prime target material. "Encryption (in transit and at rest) is table stakes. Role-based access, frequent token refreshes, and audit logs are the next layers," Sanghvi explains. "At LogiNext, we also leverage anomaly detection algorithms that monitor abnormal location pings or route deviations." These protocols have paid off. "Our platform has maintained a 99.96% uptime on web apps and a 99.67% crash-free rate on mobile—proof that stability and security can go hand in hand," he adds. Business Continuity in the Face of Cyber Disruption Cyberattacks on logistics firms don’t just affect one company, they ripple across industries. A delay in the delivery of semiconductors or medical supplies can have wide-reaching consequences. "Redundancy across infrastructure, distributed data centers, and zero-trust frameworks are non-negotiable," says Sanghvi. "The 2025 Oracle Clouds’ security breach reminded the industry that centralized failures hurt everyone in the supply chain. At LogiNext, we’ve architected our systems for high availability across regions with real-time failover and backup strategies. We simulate attack scenarios regularly to pressure-test our preparedness." The lesson is clear: resilience needs to be proactive, not reactive. Cybersecurity and Startups: Still an Afterthought? As an investor and mentor to several tech startups, Sanghvi has seen how early-stage companies often neglect cybersecurity. "Unfortunately, many still see it as a Series B problem rather than a seed-stage priority. Founders must internalize that every line of code is a potential vulnerability," he warns. "The best teams I’ve worked with are the ones who integrate security reviews into CI/CD and think about threat modeling even before launch." The shift toward “security-first startups” is slow, but vital, especially as more tech disruptors enter sensitive sectors like logistics, healthcare, and finance. Closing the Gap Between Innovation and Enforcement Is the regulatory environment keeping pace with the rapid innovation in AI and logistics tech? "Regulations are catching up, but innovation still outpaces enforcement," Sanghvi observes. "What’s encouraging is the global shift toward data localization and mandatory breach disclosures. However, unless regulators work more closely with tech providers on standards, as aviation does with aircraft safety—we’ll always be reacting to the last breach, not preventing the next." It's a call for collaborative regulation, where lawmakers and technologists co-design frameworks that anticipate risk rather than just respond to it. The Blind Spot in Cyber Strategies When asked about the most common mistake startups make, Sanghvi doesn't hesitate: "They underestimate insider threats and over-focus on perimeter security. Access logs, permission hygiene, and behavioral monitoring are often ignored. And when startups do get breached, they lack a clear incident response plan, making recovery slower and costlier." Organizations must broaden their threat models to account for not just outside hackers, but also internal actors with privileged access. Best Practices from Around the World Having worked across continents, Sanghvi believes that some regions have lessons to offer others. "The US culture of ‘security by design’ needs to be adopted more broadly. In India and the UAE, security is often retrofitted. Starting from secure architecture rather than patching it later makes systems more resilient. That mindset shift alone could prevent a significant chunk of vulnerabilities." Security as a Foundation, Not a Feature When asked about his "aha" moment regarding cybersecurity, Sanghvi’s answer is revealing. "Honestly, we never had an 'aha' moment. Security has always been a core principle at LogiNext since day one. In logistics, where systems are deeply interconnected and operate in real-time, we’ve always believed that even a minor breach can have outsized consequences." He adds, "High-profile incidents like the 2025 BlueYonder outage have only reinforced our conviction. Watching companies suffer ripple effects across industries due to avoidable security lapses validates the path we’ve taken, building with security as a foundation, not an afterthought." Autonomous AI Agents Outside of corporate strategy, what tech is Dhruvil personally excited about? His answer reflects both enthusiasm and vision. "I’m deeply excited about the rise of autonomous AI agents, especially their potential in operationalizing complex workflows without human intervention," he says. "Tools like AutoGPT, Devin, and emerging enterprise-grade agents are redefining how we think about task delegation." He continues, "Imagine a logistics coordinator that never sleeps, learns from every delivery exception, and autonomously re-optimizes routes in real-time across cities. That’s no longer science fiction." Conclusion The integration of AI in logistics is no longer a futuristic concept, it’s today’s reality. From autonomous drones to real-time tracking, AI is powering an industry that once ran on paper trails and manual schedules. But as the sector races toward automation, it must also confront an uncomfortable truth: innovation is outpacing security. In 2023 alone, the FBI logged nearly 300,000 phishing incidents in the U.S., while tools like WormGPT have emerged as dark web alternatives capable of crafting highly convincing, malicious content. In logistics, these technologies pose real risks, not just to backend systems but to the very customers companies aim to serve. The ability of drones to record and transmit footage wirelessly, for instance, introduces new surveillance risks that few have begun to address seriously. And the issue isn’t just about breaches or bots. It’s also about trust and experience. In the U.S., more than 40% of consumers reported dissatisfaction when interacting with AI-powered customer service tools. This suggests that while AI can mimic conversation, it hasn’t mastered context or empathy, both essential in high-pressure logistics scenarios where every delayed shipment or rerouted package can trigger a ripple effect. To its credit, the industry isn’t standing still. We are beginning to see more holistic strategies, including threat modeling for AI systems, real-time anomaly detection, and regulatory frameworks aimed at closing the security gap. But these responses still feel fragmented and reactive. For a sector that operates 24/7 and spans continents, that’s not good enough. As Dhruvil Sanghvi aptly pointed out, cybersecurity in logistics can’t be an afterthought. It needs to be designed into the system, not duct-taped on after a breach. And that mindset shift has to happen now, not when the next high-profile attack makes headlines. AI will undoubtedly remain central to the future of logistics. But the question isn't just what it can do—it's how safely it can do it. Because when automation fails, it’s not just code that crashes—it’s confidence, continuity, and sometimes even commerce. In the race toward a smarter supply chain, speed and security must run side by side. Anything less is a risk the industry can’t afford.
Analysis Summary
# Best Practices: Cybersecurity in the Logistics Sector
## Overview
These practices address the critical need for robust cybersecurity hygiene in the logistics industry, emphasizing that security must be integrated by design, especially given the rapid adoption of technologies like AI and the operational risks associated with system failures, data breaches, and physical security vulnerabilities. The core principle is that speed and security must advance concurrently.
## Key Recommendations
### Immediate Actions
1. **Prioritize Cyber Hygiene Remediation:** Immediately address known vulnerabilities, particularly in operational technology (OT) and IT systems underpinning logistics processes.
2. **Review AI Integration Security Posture:** Conduct an immediate, high-level risk assessment on all existing or planned AI-powered customer service tools and automation systems to identify immediate exposure points related to data handling and logic failures.
3. **Address Critical Vendor Patching:** Immediately patch critical vulnerabilities identified in hardware and software components (e.g., addressing known CVEs like CVE-2025-20309 if applicable to deployed systems).
### Short-term Improvements (1-3 months)
1. **Implement Comprehensive Threat Modeling for AI Systems:** Perform specific threat modeling exercises focused on potential adversarial attacks against AI/ML models used in supply chain optimization, routing, or customer interaction.
2. **Enhance Anomaly Detection:** Deploy or tune real-time anomaly detection systems across networks and operational platforms to quickly identify unauthorized access or unusual system behaviors characteristic of modern threats like ransomware.
3. **Address Emerging Device Risks (Drones/IoT):** Develop and enforce specific security protocols for wireless-enabled devices, such as drones, monitoring for unauthorized surveillance or data transmission vectors.
4. **Strengthen Customer Interaction Security:** Review AI-powered customer service tools to ensure they handle sensitive contextual information securely and do not inadvertently expose operational data or lead to phishing/smishing/vishing scenarios due to imperfect voice imitation or context understanding.
### Long-term Strategy (3+ months)
1. **Design-In Security (Shift Left):** Mandate that cybersecurity requirements are integrated into the design phase of all new logistics technology procurements and internal development projects, rather than being applied reactively (moving away from "duct-taping").
2. **Develop Holistic Security Frameworks:** Establish industry-specific regulatory frameworks or adhere strictly to recognized standards to close security gaps consistently across continental or global operations.
3. **Invest in Resilience and Continuity Planning:** Develop and rigorously test detailed business continuity and disaster recovery plans specifically tailored to scenarios where major automated or AI-dependent systems fail catastrophically.
4. **Foster Cross-Functional Security Culture:** Cultivate a mindset shift among leadership that views cybersecurity as foundational to confidence, continuity, and commerce, ensuring security investment keeps pace with modernization efforts.
## Implementation Guidance
### For Small Organizations
- **Focus on Foundational Controls:** Prioritize basic cyber hygiene, ensuring timely patching, strong access control (MFA everywhere), and regular backups that are tested for restoration viability.
- **Leverage Managed Services:** Outsource complex security monitoring (like 24/7 anomaly detection) to MSSPs if in-house expertise is limited.
### For Medium Organizations
- **Formalize Threat Modeling:** Begin utilizing formal risk assessment methodologies (like threat modeling) when rolling out new core logistics software or customer-facing AI features.
- **Segment Networks:** Begin segmenting IT, OT, and wireless networks explicitly to limit the blast radius of any potential breach, especially protecting core operational systems.
### For Large Enterprises
- **Establish Proactive Governance:** Implement robust governance structures to ensure security strategies align with digital transformation goals (e.g., ensuring security leads digital transformation, rather than lagging).
- **Implement Advanced Monitoring:** Deploy sophisticated, scalable solutions for real-time anomaly detection across complex, geographically distributed networks.
- **Engage Regulatory Advocacy:** Actively participate in developing or adhering to emerging regulatory frameworks addressing sector-specific risks (e.g., critical infrastructure protection).
## Configuration Examples
*No specific technical configuration examples were provided in the source text.* However, general best practice configurations inferred include:
- **Wireless Device Configuration:** Ensure drones and IoT logistics devices have strong initial authentication, encrypted communications (TLS 1.3 mandatory), and minimal external exposure.
- **AI Interface Hardening:** Implement strict input validation, rate limiting, and context verification layers on APIs interacting with customer-facing AI systems to prevent prompt injection or data leakage.
## Compliance Alignment
While the text does not mandate specific standards, the emphasis on moving from fragmented/reactive responses toward holistic, designed-in security aligns with:
- **NIST Cybersecurity Framework (CSF):** Strong alignment with the **Identify** (risk management for emerging tech) and **Protect** (security by design) functions.
- **ISO/IEC 27001/27017:** Establishing formal policies and controls for managing information security risks in cloud services and critical supply chains.
- **Sector-Specific Regulations:** Need for adherence to evolving regulatory frameworks specifically targeting critical infrastructure security pertinent to logistics.
## Common Pitfalls to Avoid
- **Treating Cybersecurity as an Afterthought:** Deploying new technologies (especially AI) without security integrated from the start.
- **Underestimating Operational Technology (OT) Risk:** Focusing only on backend IT systems while ignoring vulnerabilities in the physical systems that manage movement, routing, and tracking.
- **Reactive Patching:** Waiting for high-profile attacks to occur before addressing known vulnerabilities in deployed systems.
- **Ignoring Customer Experience Security:** Assuming AI interaction security ends at basic authentication; failing to account for context loss or impersonation risks during customer service interactions.
## Resources
- **Framework Reference (Inferred):** NIST CSF, ISO 27001.
- **Actionable Information Source (Explicitly mentioned but defanged):** Focus on industry best practices concerning **Supply Chain Resilience and Physical Security**.