While the Internet of Things (IoT) opens up a wide range of opportunities for the supply chain, it is also vulnerable to cyberattacks. Using a threat model can help companies assess how secure their system is.
Internet of Things (IoT) solutions are becoming increasingly common for both consumers and businesses. While consumers explore Internet-connected refrigerators and webcams, in the business world IoT solutions include:
Asset tracking: IoT tools that help companies identify the location of key assets such as trucks or IT equipment;
Smart buildings: IoT tools that use distributed sensors to improve environmental quality and lower the costs of heating, ventilation, and air conditioning (HVAC) systems;
Supply chain monitoring: IoT tools that help managers predict and avoid delays and damages of in-transit goods;
Equipment monitoring: IoT tools that monitor capital equipment to enable preventative maintenance.
While these IoT solutions offer real benefits, they also introduce new security risks, like the risk of data being intercepted or compromised. Companies need to recognize these potential threats and make informed security decisions regarding an IoT solution for their organization. To accomplish this, it's helpful to think in terms of a "threat model." In security parlance, a threat model summarizes: 1) potential attack objectives, 2) the ways in which a system might be compromised, and 3) security countermeasures. Supply chain leaders need to take each of these considerations into account as they build an accurate threat model for their particular IoT solution and environment, since different IoT solutions and environments have different threat models.
Attack Objectives
As you begin to develop a threat model for your IoT application, start by identifying plausible attack objectives. An attacker may have many objectives, but the following are some of the most common worth considering:
Physical harm: If your IoT system controls the physical activity of piece of equipment (for example, an industrial automation system), an attack could take control of that activity and do damage to your equipment or the facility.
Data corruption: An attacker could send false data (or block data from being sent), causing you to make the wrong decision but without harming any equipment directly.
Data destruction: Removing data either directly from the device or from the data-recording or storage system could help an attacker cover up some other malicious activity.
Espionage: An attacker could tap into the monitoring capabilities of your IoT system to "snoop" on sensitive data, without tampering with it.
Once you have identified the objective for a potential attack, it is helpful to prioritize which ones you should focus on preventing. For each potential attack scenario, it is useful to ask yourself, "What are the consequences?" to determine the severity of the attack and prioritize concerns. For example, the threat of losing IoT data for one hour due to a bad actor jamming a communications signal is probably less serious than the risk of damage to a facility. Next, consider what reasons an attacker might have to pursue the potential attack goals you've outlined. A scenario with a clear benefit to the attacker is often a bigger concern than one without any clear motivation to act on it. Prioritize threats with a known or conceivable motivation.
Potential Weaknesses
Once you've considered what could happen, next ask, "How likely is it to occur?" Consider potential attack pathways and the security weaknesses that might enable them. IoT vulnerabilities might include configuration errors (for example, neglecting to change a default password) or misuse of access privileges (for example, if a user copies and exports data).
Another key consideration is the potential avenue of attack presented by your IoT device's communications network protocol. This will vary widely based on the network you use:
Wired: Wired solutions use a physical connection, such as Ethernet or DSL, to transmit data. These solutions tend to avoid many of the security risks of Wi-Fi and Bluetooth solutions, but they are severely limited in scalability and mobility. As this article explains, wired solutions are generally not a great fit for many common IoT applications because they require so much infrastructure.1Â It is often preferable to rely on a wireless technology for a modern IoT implementation.
Bluetooth: Bluetooth supports a number of security mechanisms for different versions of the protocol.2 While the simplest security setting offers little protection from nearby eavesdroppers, other settings offer authentication and encryption mechanisms that improve security. That said, these security mechanisms often come at the cost of ease of deployment and maintenance.
Wi-Fi: Security for Wi-Fi-connected IoT devices is best summarized by the article "Wi-Fi access for the Internet of Things can be complicated."3 While the original Wi-Fi protocol is not well-suited for mobile IoT devices, there are mechanisms being introduced that should improve security. However, as with most wireless protocols, security improvements often have negative repercussions on operational costs, ease of setup, and compatibility with other existing systems.
Cellular: IoT devices that use cellular communication come with a fair amount of built-in security, as outlined in this paper from the cellular standards group GSMA.4 Security researchers have demonstrated ways of intercepting a cell signal with specialized equipment, but these attacks generally require the attacker to be in close proximity to the targeted device. As such, security risks with cellular-based IoT solutions are generally fairly limited.
In addition to the potential attack pathway, there are a number of other factors that you need to take into account in order to determine whether or not your IoT solution is secure. Consider, for example, whether an attacker needs physical access to the IoT device, and if so, how secure those devices are. A device on the outside of a building in a remote area may be more of a risk than a device inside a locked container, for example. Also consider the device itself—what skill set, tools, and time are required to tamper with it, and would the ends justify the means? Finally, consider whether attackers might achieve their objectives by abusing access granted to an authorized individual. What capabilities would the attacker have in this scenario? What safeguards should be established to counter this risk?
Evaluating the ways in which different IoT systems can be compromised will help you to build an accurate threat model of your particular environment. In turn, this careful consideration and evaluation will help you to determine the appropriate IoT solution for a given application.
What countermeasures can you employ?
After identifying the potential attack scenarios, consider the countermeasures that are built in to protect the IoT solution. One level is physical countermeasures—things that prevent or mitigate direct access to the device. Is the device easily accessible? Does the device have ethernet or USB ports that can be used to access the firmware? Is the firmware secured? Consider options for "hardening" the IoT device itself.
Second, consider the communications network (as discussed above). Weigh the tradeoffs of cost, ease, and security to make sure the method you've chosen meets your needs. Make sure that you are employing the safeguards available with your chosen technology.
IoT systems can also employ active countermeasures, such as scanning for unauthorized or unusual access and alerting administrators or security staff, similar to other enterprise systems. Finally, user accounts can be restricted to limit misuse, and the system as a whole can be built to maintain security even if a specific sensor has been compromised.
Making the final call
IoT is creating amazing opportunities for organizations to process data and automate environmental interactions in new ways. But as with all advances, IoT comes with risks. By applying a threat model framework and analyzing the possible attack objectives, security weaknesses, and possible countermeasures, organizations can apply a familiar security framework to this new technology. Organizations that are clear-eyed about evaluating these risks will find and deploy IoT solutions to derive enormous value while maintaining appropriate security.
3. For greater detail on these complications, see Peter Thornycroft, "Wi-Fi Access for the Internet of Things Can Be Complicated," Network World (March 21, 2016),  https://www.networkworld.com/article/3046132/internet-of-things/wi-fi-access-for-the-internet-of-things-can-be-complicated.html
The launch is based on “Amazon Nova,” the company’s new generation of foundation models, the company said in a blog post. Data scientists use foundation models (FMs) to develop machine learning (ML) platforms more quickly than starting from scratch, allowing them to create artificial intelligence applications capable of performing a wide variety of general tasks, since they were trained on a broad spectrum of generalized data, Amazon says.
The new models are integrated with Amazon Bedrock, a managed service that makes FMs from AI companies and Amazon available for use through a single API. Using Amazon Bedrock, customers can experiment with and evaluate Amazon Nova models, as well as other FMs, to determine the best model for an application.
Calling the launch “the next step in our AI journey,” the company says Amazon Nova has the ability to process text, image, and video as prompts, so customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content.
“Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” Rohit Prasad, SVP of Amazon Artificial General Intelligence, said in a release. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.”
The new Amazon Nova models available in Amazon Bedrock include:
Amazon Nova Micro, a text-only model that delivers the lowest latency responses at very low cost.
Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs.
Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
Amazon Nova Premier, the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models
Amazon Nova Canvas, a state-of-the-art image generation model.
Amazon Nova Reel, a state-of-the-art video generation model that can transform a single image input into a brief video with the prompt: dolly forward.
Economic activity in the logistics industry expanded in November, continuing a steady growth pattern that began earlier this year and signaling a return to seasonality after several years of fluctuating conditions, according to the latest Logistics Managers’ Index report (LMI), released today.
The November LMI registered 58.4, down slightly from October’s reading of 58.9, which was the highest level in two years. The LMI is a monthly gauge of business conditions across warehousing and logistics markets; a reading above 50 indicates growth and a reading below 50 indicates contraction.
“The overall index has been very consistent in the past three months, with readings of 58.6, 58.9, and 58.4,” LMI analyst Zac Rogers, associate professor of supply chain management at Colorado State University, wrote in the November LMI report. “This plateau is slightly higher than a similar plateau of consistency earlier in the year when May to August saw four readings between 55.3 and 56.4. Seasonally speaking, it is consistent that this later year run of readings would be the highest all year.”
Separately, Rogers said the end-of-year growth reflects the return to a healthy holiday peak, which started when inventory levels expanded in late summer and early fall as retailers began stocking up to meet consumer demand. Pandemic-driven shifts in consumer buying behavior, inflation, and economic uncertainty contributed to volatile peak season conditions over the past four years, with the LMI swinging from record-high growth in late 2020 and 2021 to slower growth in 2022 and contraction in 2023.
“The LMI contracted at this time a year ago, so basically [there was] no peak season,” Rogers said, citing inflation as a drag on demand. “To have a normal November … [really] for the first time in five years, justifies what we’ve seen all these companies doing—building up inventory in a sustainable, seasonal way.
“Based on what we’re seeing, a lot of supply chains called it right and were ready for healthy holiday season, so far.”
The LMI has remained in the mid to high 50s range since January—with the exception of April, when the index dipped to 52.9—signaling strong and consistent demand for warehousing and transportation services.
The LMI is a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).
Specifically, 48% of respondents identified rising tariffs and trade barriers as their top concern, followed by supply chain disruptions at 45% and geopolitical instability at 41%. Moreover, tariffs and trade barriers ranked as the priority issue regardless of company size, as respondents at companies with less than 250 employees, 251-500, 501-1,000, 1,001-50,000 and 50,000+ employees all cited it as the most significant issue they are currently facing.
“Evolving tariffs and trade policies are one of a number of complex issues requiring organizations to build more resilience into their supply chains through compliance, technology and strategic planning,” Jackson Wood, Director, Industry Strategy at Descartes, said in a release. “With the potential for the incoming U.S. administration to impose new and additional tariffs on a wide variety of goods and countries of origin, U.S. importers may need to significantly re-engineer their sourcing strategies to mitigate potentially higher costs.”
Grocers and retailers are struggling to get their systems back online just before the winter holiday peak, following a software hack that hit the supply chain software provider Blue Yonder this week.
The ransomware attack is snarling inventory distribution patterns because of its impact on systems such as the employee scheduling system for coffee stalwart Starbucks, according to a published report. Scottsdale, Arizona-based Blue Yonder provides a wide range of supply chain software, including warehouse management system (WMS), transportation management system (TMS), order management and commerce, network and control tower, returns management, and others.
Blue Yonder today acknowledged the disruptions, saying they were the result of a ransomware incident affecting its managed services hosted environment. The company has established a dedicated cybersecurity incident update webpage to communicate its recovery progress, but it had not been updated for nearly two days as of Tuesday afternoon. “Since learning of the incident, the Blue Yonder team has been working diligently together with external cybersecurity firms to make progress in their recovery process. We have implemented several defensive and forensic protocols,” a Blue Yonder spokesperson said in an email.
The timing of the attack suggests that hackers may have targeted Blue Yonder in a calculated attack based on the upcoming Thanksgiving break, since many U.S. organizations downsize their security staffing on holidays and weekends, according to a statement from Dan Lattimer, VP of Semperis, a New Jersey-based computer and network security firm.
“While details on the specifics of the Blue Yonder attack are scant, it is yet another reminder how damaging supply chain disruptions become when suppliers are taken offline. Kudos to Blue Yonder for dealing with this cyberattack head on but we still don’t know how far reaching the business disruptions will be in the UK, U.S. and other countries,” Lattimer said. “Now is time for organizations to fight back against threat actors. Deciding whether or not to pay a ransom is a personal decision that each company has to make, but paying emboldens threat actors and throws more fuel onto an already burning inferno. Simply, it doesn’t pay-to-pay,” he said.
The incident closely followed an unrelated cybersecurity issue at the grocery giant Ahold Delhaize, which has been recovering from impacts to the Stop & Shop chain that it across the U.S. Northeast region. In a statement apologizing to customers for the inconvenience of the cybersecurity issue, Netherlands-based Ahold Delhaize said its top priority is the security of its customers, associates and partners, and that the company’s internal IT security staff was working with external cybersecurity experts and law enforcement to speed recovery. “Our teams are taking steps to assess and mitigate the issue. This includes taking some systems offline to help protect them. This issue and subsequent mitigating actions have affected certain Ahold Delhaize USA brands and services including a number of pharmacies and certain e-commerce operations,” the company said.
Editor's note:This article was revised on November 27 to indicate that the cybersecurity issue at Ahold Delhaize was unrelated to the Blue Yonder hack.
The new funding brings Amazon's total investment in Anthropic to $8 billion, while maintaining the e-commerce giant’s position as a minority investor, according to Anthropic. The partnership was launched in 2023, when Amazon invested its first $4 billion round in the firm.
Anthropic’s “Claude” family of AI assistant models is available on AWS’s Amazon Bedrock, which is a cloud-based managed service that lets companies build specialized generative AI applications by choosing from an array of foundation models (FMs) developed by AI providers like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself.
According to Amazon, tens of thousands of customers, from startups to enterprises and government institutions, are currently running their generative AI workloads using Anthropic’s models in the AWS cloud. Those GenAI tools are powering tasks such as customer service chatbots, coding assistants, translation applications, drug discovery, engineering design, and complex business processes.
"The response from AWS customers who are developing generative AI applications powered by Anthropic in Amazon Bedrock has been remarkable," Matt Garman, AWS CEO, said in a release. "By continuing to deploy Anthropic models in Amazon Bedrock and collaborating with Anthropic on the development of our custom Trainium chips, we’ll keep pushing the boundaries of what customers can achieve with generative AI technologies. We’ve been impressed by Anthropic’s pace of innovation and commitment to responsible development of generative AI, and look forward to deepening our collaboration."