A traffic signal that adapts in real-time to ease congestion. A connected vehicle that warns drivers of black ice ahead. An ambulance that gets priority at every intersection on its route to an emergency.

These are all examples of smart transportation technology already transforming how we move through cities and states. The stakes couldn’t be higher — and understanding what ‘smart transportation’ really means is the first step to getting it right.

Smart transportation leverages interconnected technologies to help cities and states better allocate resources, reduce energy consumption, cut costs, and provide a more inclusive and equitable experience for communities. At its core are smart technologies: connected, data-driven systems that use sensors, automation, and analytics to collect and act on information autonomously, continuously adapting to real-time conditions rather than simply following pre-programmed instructions.

But beyond efficiency gains, these systems are also becoming vital for public safety, from managing emergency response times to protecting drivers, transit users, cyclists, and pedestrians from danger.

As these networks grow more connected and automated, they demand a new approach: one where robust cybersecurity and intelligent edge computing work together to protect lives and maintain the critical services communities depend on every day.

The collision of cybersecurity and AI

Understanding this new landscape requires looking at how smart transportation technologies nest within each other — each layer expanding capabilities while amplifying both opportunities and risks.

At the foundation sits intelligent transportation systems (ITS), the broadest infrastructure — integrated systems using sensors, connected devices, and analytics to monitor and manage traffic flow, signals, tolling, transit operations, and more. 

Within this ITS infrastructure, vehicle-to-everything communications (V2X) enable a vast network of real-time data exchange between vehicles, traffic signals, emergency systems, pedestrian devices, and other infrastructure — sharing information about road conditions, hazards, and traffic patterns.

Among the various participants in this V2X ecosystem, connected and automated vehicles (CAVs) represent a particularly transformative category: vehicles with internet connectivity and varying levels of automation that can not only receive information but actively respond to their environment and coordinate with surrounding systems in real time.

From ITS infrastructure upgrades to V2X pilot programs to CAV-ready corridors, cities and states across the US are actively investing in developing and implementing smart systems to improve efficiency, safety, and mobility. 

But as these systems grow more connected, they also become more vulnerable.

Key risks and challenges in smart transportation 

The same connectivity that enables innovation introduces new vulnerabilities. Following are key risks transportation managers need to be aware of — and guard against:

1. Growing connectivity means growing cyber risk. Every connected device — from traffic cameras to in-vehicle systems — represents a potential entry point for cyberattacks.
   
   A compromised traffic management system or connected vehicle network can present a public safety crisis that could delay emergency responders, cause gridlock, or worse.
   
2. Public safety stakes are immediate — and potentially catastrophic. When these systems fail or are breached, they can pose existential threats. State and city transportation agencies must contend with risks from criminals, terrorists, and nation-state adversaries who could manipulate traffic signals to cause crashes at every intersection or grind entire cities to a halt in gridlock.
   
   Attackers could intercept payment credentials at EV charging stations, knock critical charging infrastructure offline, or disable crash detection systems precisely when they're needed most.
   
   Even without malicious attacks, poorly maintained or outdated systems can leave emergency vehicles stuck in traffic unable to reach those who need help, or allow accidents to go undetected until it's too late.
   
3. Bandwidth constraints are mounting. Smart transportation systems generate massive amounts of data — video feeds from traffic cameras are particularly data-intensive, but connected vehicles, sensors, and infrastructure devices all contribute to the growing demand.
   
   Backhauling all this data to centralized cloud systems quickly overwhelms network capacity, creating bottlenecks that can compromise system performance. Edge processing is critical to filter, analyze, and act on data locally rather than transmitting everything across constrained networks.
   
4. Latency undermines real-time operations. In smart transportation, milliseconds matter. A connected vehicle detecting black ice, a traffic signal coordinating with approaching emergency vehicles, an automated system identifying an accident — these scenarios require split-second decisions and responses. Cloud-based processing introduces delays that prevent these systems from operating effectively, undermining the very efficiencies and capabilities they were designed to provide.
   

The upside: When implemented securely and with edge computing, smart transportation delivers on its promise. 

Making transportation smarter and safer

When secure, smart transportation delivers transformative results that save lives and improve how cities function. Consider the range of what’s possible:

• Crash detection systems become true lifesavers. Sensors and connected vehicles can identify collisions the moment they occur and automatically alert emergency services with precise location data, shaving critical minutes off response times. In remote areas or on highways where crashes might otherwise go unnoticed, this capability can mean the difference between life and death.
  
• Pedestrian safety reaches new levels. Smart crosswalks equipped with detection technology can identify when someone is crossing — even if a driver doesn't see them — and alert both the vehicle and adjust signal timing to prevent accidents.
  
  For vulnerable populations like children, elderly residents, and people with disabilities, these systems create safer, more accessible streets.
  
• Emergency response is seamlessly coordinated. When an ambulance, fire truck, or police vehicle is en route to a crisis, connected traffic management systems can prioritize their path in real-time — turning lights green, alerting other drivers, and calculating the fastest route based on current conditions. Every second saved can be the difference in an emergency outcome.

Leveraging edge AI and cybersecurity for effective smart transportation

The societal benefits of smart transportation are immense. But they can only materialize when security is the technology foundation. With robust cybersecurity measures, including secure, plug-and-play combinations of edge AI and cybersecurity, agencies can deploy smart transportation solutions with confidence.

Edge AI deployments solve the infrastructure challenge by processing data locally, keeping response times low and reducing network burden — while also minimizing the attack surface by limiting data transmission to the cloud. 

Paired with quantum-resilient encryption, zero-trust networking, and built-in update mechanisms, these measures make thousands of distributed ITS devices resilient against evolving threats from day one.

Ultimately, proactive cybersecurity and infrastructure strategy empower municipal and state transportation agencies to stay ahead of a range of threats — while keeping roads safer and smarter for the public.

Intelligent transportation systems are only as safe as the security behind them. Contact our team to discuss your transportation system needs.

Image: iStock | Josh Kizziar Photography

AI infrastructure is scaling at breakneck speed, with massive investment fueling new data centers, GPU clusters, and edge systems. That growth also expands the attack surface, raising the risk of costly breaches, operational disruption, and compromised trust for organizations across industries.

“Building the Backbone of AI,” a new report by Brookfield, a global investment firm whose Brookfield Asset Management (BAM) division boasts more than $1T in assets under management (AUM), estimates that more than $7 trillion will be invested in AI infrastructure over the next decade — spanning upgraded power grids, global connectivity from fiber and telecommunications to satellites, and modular hardware designed to keep pace with rapid innovation. 

This expansion is actively multiplying the attack surface. Every new node, from hyperscale cloud facilities to portable edge units, introduces fresh opportunities for cyber and physical threats. (Crucially, our SanQtum cybersecurity solution secures nodes as this attack surface expands and infrastructure becomes more distributed.) And as AI gets baked into more software and apps that didn't have it before, the digital attack surface multiplies, too — making every added piece of intelligence an exponential vulnerability multiplier.

For leaders across industries like healthcare, energy, industrial, financial, government, manufacturing, and transportation and logistics, it’s critical to understand the scope of this emerging threat landscape.

Key trends from Brookfield’s report — and their cybersecurity implications

The Brookfield report — which outlines opportunities to invest in the infrastructure that is expected to support the next industrial revolution — sheds detailed light on key trends with deep implications for cybersecurity.

1. Inference will dominate AI workloads by 2030. The report forecasts that most compute will soon be spent on inference, not training. AI inference is the ability of a trained AI model to recognize patterns and draw conclusions from information it hasn’t seen before. It underpins many of AI’s most exciting applications, such as generative AI, and allows models to imitate the way people think, reason, and respond to prompts.
   
   While AI training typically occurs in centralized, hyperscale cloud data centers, inference increasingly happens at the edge — on distributed, sometimes portable devices that require near real-time, ultra-low latency access to compute resources. 
   
   This shift means that model integrity, runtime protection, and on-device data security will be just as important as securing training pipelines in centralized environments.
   
2. Distributed deployment expands the attack surface. Edge systems, mobile units, and geographically scattered nodes offer inherent security advantages — deployments with limited access points can be more resilient against cyber attacks targeting centralized infrastructure like telecom networks or power grids. But distributed deployments are also often physically exposed, harder to monitor, and attractive targets for theft or tampering.
   
   These mixed advantages and risks require security strategies that go beyond traditional firewalls, demanding comprehensive protection to address firmware, hardware, and physical threats simultaneously.
   
3. Hardware must be built for upgradeability. Brookfield’s report emphasizes modular, upgradeable hardware to keep pace with AI innovation. This imperative for adaptability extends directly to cybersecurity systems, which must be designed with the same flexibility in mind. Security architectures need trust anchors, cryptographic modules, and firmware that can be upgraded or replaced without disrupting operations, while organizations must stay current with emerging technologies, and be prepared to deploy them accordingly.
   
   Implementing a trusted cybersecurity SaaS strategy can give you the power of the most robust technology, while freeing you from having to invest directly in the hardware or constantly monitor for updates. For example, as a managed service, SanQtum and SanQtum AI take care of this for you. We’ve designed our hardware to be modular, so we can swap and upgrade switches, routers, chips, and other components. But you don’t need to worry about that. We handle the headache, just as if we were updating software.
   
4. Cyber-physical convergence increases risk. AI infrastructure relies on integrated systems like IoT cooling sensors, power distribution, and robotics. Each system adds new cyber-entry points and interdependencies. This convergence means that edge security must evolve beyond traditional software patches, including tamper detection, geofencing, robust encryption, and rapid remote wipe capabilities that can respond to threats across both digital and physical domains.

Security priorities for edge AI stakeholders

Whether building AI infrastructure or deploying it, all organizations need zero trust architecture and robust security practices. Beyond these fundamentals, here are priorities by role:

For infrastructure providers:

• Embed hardware roots of trust in AI accelerators and ensure supply chain transparency.
• Harden distributed units with physical protections like locks, GPS tracking, and kill switches.
• Design failover capabilities so operation continues even if core networks are compromised.

For enterprise AI deployers:

• Implement quantum-resilient encryption to protect against current and future threats.
• Deploy continuous threat monitoring that can isolate emerging threats in real time.

Why now is the time to defend your organization

Brookfield’s report on the trillions pouring into AI infrastructure investment highlights how massive and distributed growth will be — and with that scale comes a wider range of potential attack surfaces. Many organizations are rushing to deploy AI infrastructure and models without prioritizing security from the outset, waiting until deployment risks unpatchable vulnerabilities and exposes critical models, data, and physical systems to attack. 

Now is the time to build in zero trust cybersecurity. Unsecured AI is an inexcusable, massive risk. AI infrastructure, model training, and inference need to grow hand-in-glove with cutting-edge cyber protections to stay ahead of risks that have never been greater.

Security needs to grow with AI, not after it. One of the easiest and fastest ways to do so is via an as-a-service model, such as SanQtum and SanQtum AI. Contact our team to learn more.

Image: Unsplash | Paul Hanaoka

When a cybersecurity expert recently tested a simple AI-powered shopping list app, everything seemed perfect. The AI helped add items, suggested cheesecake ingredients, and even corrected typos with impressive accuracy. But when he asked it to add "the most healthy food in the world," the app responded with rat poison. This wasn't a glitch — it was AI poisoning in action.

As artificial intelligence becomes deeply embedded in critical infrastructure, healthcare systems, financial networks, and manufacturing operations, a new category of cyber threat is emerging. The National Institute of Standards and Technology (NIST) warns that adversaries can deliberately confuse or "poison" AI systems to make them malfunction, with attacks possible both during training and throughout an AI system's operational life.

Understanding AI poisoning 

AI poisoning occurs when attackers target the data used to train and operate AI systems, corrupting their decision-making processes. The threat encompasses three primary attack vectors:

1. Training data manipulation: Injecting malicious samples, biased datasets, or incorrect labels into training data to corrupt the model's foundational logic.
2. Model manipulation: Infiltrating the model itself through adversarial attacks, backdoor insertion, or parameter corruption to make outputs unreliable.
3. Output interference: Using prompt injection, jailbreaking techniques, or response spoofing to manipulate what users receive from AI systems.

What makes these attacks particularly dangerous is their accessibility.

“Most of these attacks are fairly easy to mount and require minimum knowledge of the AI system and limited adversarial capabilities,” said Alina Oprea, a professor at Northeastern University and co-author of NIST’s report outlining adversarial machine learning strategies. “Poisoning attacks, for example, can be mounted by controlling a few dozen training samples, which would be a very small percentage of the entire training set.” 

Real-world implications across critical sectors

The consequences extend far beyond shopping list mishaps.

In healthcare, poisoned AI could misdiagnose patients or recommend harmful treatments. Financial institutions could see fraud detection systems corrupted to miss suspicious transactions. Transportation systems managing autonomous vehicle networks could be compromised to cause accidents or traffic disruptions. Energy grids — like California's soon-to-be AI-enabled power system — could face dangerous instability if their decision-making algorithms are poisoned.

Recent research, including a study by security researchers on enterprise AI systems, found that AI systems can be manipulated by poisoned documents containing hidden instructions, causing them to ignore legitimate sources, spread misinformation, or leak sensitive data. For organizations across healthcare, energy, industrial, financial, government, manufacturing, and transportation sectors, these vulnerabilities pose serious risks to operations, safety, and reputation.

Government agencies face particular exposure, as data poisoning attacks can distort AI outputs, undermine public trust in services, and reduce reliability of mission-critical systems. But the threat extends to any organization relying on AI for competitive advantage or operational efficiency.

Securing your AI systems

While there's no silver bullet against AI poisoning, organizations can implement comprehensive protection strategies:

• Strict input sanitization: Strip or flag adversarial instructions in documents before ingestion.
• Prompt/data separation: Enforce clear boundaries so user prompts aren’t influenced by document text.
• Citation enforcement: Require traceable sources for all AI outputs.
• Cache management: Purge deleted or outdated content immediately.
• Access control and monitoring: Limit who can upload data and audit for anomalies.
• Adversarial training: Expose models to poisoned data during testing to build resilience.

Traditional cybersecurity approaches are insufficient for AI-specific vulnerabilities. Effective protection requires a zero-trust architecture that secures not just network pipes but the data flowing through them. Organizations need continuous integration and deployment practices that safely test AI models before production deployment — preventing the kind of untested updates that can bring entire systems offline.

Next steps in AI security

As AI poisoning attacks grow more sophisticated and widespread, organizations across critical infrastructure and enterprise systems cannot afford reactive approaches. The time for comprehensive AI security is now — before a poisoned algorithm makes decisions that affect lives, operations, or competitive position.

Ready to protect your AI systems from poisoning attacks? Learn more about implementing robust AI security solutions tailored to your industry's unique risks.