As homes, workplaces, and cities digitally transform during our Fourth Industrial Revolution, many of those charged with securing this digital future can find it difficult to “level up” from the endpoints and focus on defining and solving the larger problem sets. It is easy to get bogged down in the myriad of smart and smart-enough devices that constitute “IoT” in isolation of the overall security scope of the smart city – losing both valuable context and constraints.
While “smart city” can mean a bunch of things to different people, for city planners and officials, it’s definition and implementation problems are quite well understood. The vendors that come knocking on their doors promote point solutions – smart traffic control systems, 5G and ultra-high bandwidth wireless communications, driverless vehicles, etc. – leaving the cities’ IT, operational technology (OT), and infosec teams to bring it all together.
An essential part of a security professional’s work is diving deep into the flaws and perils of individual products and clusters of technologies. But trying to “solve security” at a city level is an entirely different paradigm.
A substantial number of my peers and security researchers I’ve worked with over the past couple of decades have focused their energies on securing autonomous vehicles. The threats are varied – ranging from bypassing emission and speed controls to evading the next generation of city road taxes and insurance regulations to malicious remote control of someone else’s vehicle – yet mostly isolated to the vehicles themselves. From what I’m seeing and hearing, they’re doing a great job in securing these vehicles. Their security successes also advance traditional transit solutions, which helps smart cities keep pace with the transportation needs of a growing population.
Given the continued urbanization of human population, the growth and attraction of megacities (10 million plus inhabitants), and the strains on traditional transport systems, the thought of increasing personal-use autonomous vehicles in these heavily congested cities is outdated and arguably ludicrous. Today’s megacities are already battling traffic congestion with zoned charging, elimination of fossil fuels, and outright banning of private transport. Tomorrow’s megacities – jumping from 33 cities today with the largest holding 38 million people to over 100 with populations in excess of 88 million people by 2100 – need to completely rethink their transport systems and the security that goes with it.
Oddly enough, securing mass transit for megacities come with some advantages. Mass transport systems that evolve from trains, trams, and subways, have embedded within them design constraints that positively influence security. For example, driverless cars of today have to navigate and solve all kinds of road and traffic problems while trams stick to pre-defined paths (i.e. rail networks) with greatly simplified routing and traffic signaling. Research papers covering adversarial AI in recent years have focused on attacking deep learning and cognitive AI systems used by autonomous vehicles (e.g. adding stickers to a stop sign and making the driverless car think the sign says 45 mph), but these tactics would have negligible to no impact on reasonably scoped public transport systems.
It is reasonable to assume that the smart cities of the near future will consist of trillions of smart devices – each of them semi or fully managed, providing alerts, logs, and telemetry of their operations. For those city leaders – particularly CIOs, COOs, CTOs, CISOs, and CSOs – the changes needed to manage, secure, certify, and govern all these devices and their output are mind bogglingly huge.
Interestingly enough, the framework for managing data security for millions of chatty networked devices has largely been solved. Having become cloud-native, modern Security Incident and Event Management (SIEM) technologies have proved to be remarkably successful in identifying anomalies, attacks, and misconfigurations.
The data handling capabilities and scalability of cloud-native SIEM may be just the right kind of toolkit to begin to solve smart city operations (and security) at the megacity level. In addition, with advanced AI being a core component of SIEM, the systems that identify and construct attack kill chains and mitigate threats through conditional access rules could instead be used and trained to identify surge transport requirements (due to concerts ending on a rainy day) and automatically reroute and optimize tram or bus capacity to deliver citizens safely (and dryly) to their destinations – as an example.
Securing smart cities offers many opportunities to rethink our assumptions on security and “level up” the discussion to solve problems at the ecosystem level. Advancements in AI analytics and automated response technologies can handle the logs, alerts, and streaming telemetry that contribute to OT infrastructure security for mega cities. In turn, this increase in data volume fine tunes anomaly and behavioral-based detection systems to operate with higher efficiency and fidelity, which helps secure city-wide IT infrastructure.