Nobody can say there has been a lack of interest in AI across businesses, and the logistics space is no exception. AI can be used in many different ways across logistics businesses but many professionals believe security and data risks come hand-in-hand with implementing the new technology.
In fact, data from a recent Tech.co survey found that 19% of logistics professionals cite “data quality and security” as the biggest AI blocker, preventing companies from investing into the technology further.
In this article, we break down the common AI security risks that your logistics business could face, and how to combat them specifically. If you scroll down further, you’ll find a detailed five-point checklist your business can follow when deploying AI, in order to make sure you’re doing so securely.
Common AI Security Risks for Logistics Businesses
Here are some of the most common security risks your logistics business could face when implementing AI, and the ways that you could mitigate them.
Lack of explainability
When they first start using AI, businesses can fail to understand the way the system operates. This can arise at any stage, but most commonly occurs when businesses are first getting to grips with AI, or during initial testing.
If you or your employees fail to understand AI, this can lead to reduced trust and increased risk of exploiting the AI when it does become more widely used.
How does my business combat a lack of explainability?
Ensuring your business has done its research is key. What do you need AI for? How many employees will have access to it? How will it fit in to your existing operations?
As part of this research, your business can analyze the behaviours and techniques of the AI to understand how it makes decisions. Likewise, having a dedicated AI policy outlining this preliminary information ensures everyone is on the same page.
Data breaches
Data breaches are a problem for all businesses and AI systems are definitely a target for attackers because of the amount of sensitive and confidential data they have access to, including employee and customer information.
Not only could a potential data breach harm individuals, but they can cause business disruptions and result in significant legal consequences.
How does my business combat data breaches?
Fortifying your AI system with encryption data and ensuring different privacy techniques are implemented when you are developing and training your model is critical.
Regular audits and reviews of access to sensitive data are advised, and we would also recommend following the principle of least privilege to limit the scope of access to your systems.
You should also adhere to data protection regulations on a federal and state level.
Prompt injection attacks
This happens when an attacker creates an input designed to make your AI model behave in an unintentional way. For example, this could be a prompt that makes the AI generate offensive content, reveal confidential or sensitive information, or trigger other dangerous activity.
How does my business combat prompt injection attacks?
The best way to prevent these attacks is to schedule and carry out routine model updates, and monitor activity regularly to root out any unusual patterns or deviations.
We would also recommend conducting ethical hacking and penetration testing to identify any potential vulnerabilities within your system.
Data poisoning attacks
A data poisoning attack usually comes in the early stages of AI deployment. In this case, an attacker will tamper with the data an AI model is trained on, in order to produce undesirable outcomes.
How does my business combat data poisoning attacks?
In a similar way to prompt injection attacks, ensuring your systems are up-to-date and regularly checked for any vulnerabilities is the best way to prevent against data poisoning attacks. This will allow you to track system performance and analyze model behavior over a variety of situations.
Ethical concerns
One example of this is algorithmic bias, where discrimination occurs in AI-decision making due to prejudiced data. Another issue that faces businesses is the handling of sensitive data, and how to properly protect and safeguard against the information being used in the wrong situations.
How does my business combat ethical concerns?
Again, making sure the data you’re feeding the AI is properly updated and monitoring it continuously is the best way to avoid any kind of bias. Likewise, when handling sensitive information, specific measures and instructions should be in place to tell the AI how to use it.
5-Point AI Checklist To Maintain Security For Your Business
Here are five starting points for integrating AI into your business. This list isn’t definitive, but it will definitely get you moving in the right direction.
1. Write a clear, detailed AI plan
An in-depth document is the best way to make sure everyone understands the implications of deploying AI at your company, and lays down any ground rules. But it also gives your employees a definitive stance on what happens if any issues arise, and outlines any core security procedures.
This plan could also highlight how your company would like to use AI, how it would operate within different roles, and any policies in place to protect your company and employees from any legal action.
For guidance on writing an AI policy for your business, with templates, you can check out our guide.
2. Test and review your model often
It’s not enough to just integrate a complex system like AI into your business and let it run. You’ll need to be constantly testing and reviewing your model’s data, inputs, and outputs to ensure it remains efficient and sound. This is also a great way to prevent against any attacks on your system.
Some companies would even benefit from carrying out ethical hacking themselves, to help point out where the vulnerabilities in your system could be. It’s also useful in seeing how your AI reacts to potentially dangerous or compromising situations, so these can be slowly patched up.
Finally, keeping an eye on key metrics, such as threat detection rates, false positives, and response times, will allow you to understand the effectiveness of your system.
3. Emphasize high-quality data inputs
Your AI system is only as good as the information it gets fed, so being conscious of any training data is a great way of making sure it remains healthy. It’ll also prevent potential biases or prejudices in your overall output.
Ultimately, you should be training your AI on diverse, accurate, and up-to-date information. We all know that AI can make mistakes, but making sure that your system is able to make decisions based on objective and solid data is one way to minimize this.
4. Keep humans involved
AI-fueled layoffs are newsworthy, and they are certainly on people’s minds whenever AI is brought up within a business context. However, owners and operators would do better by keeping employees involved and in-the-loop on AI projects, rather than removing them altogether.
As we said, AI can make mistakes, and human oversight can prevent this. Humans will be able to review and update your AI systems, and can catch any biases, false positives, or manipulated results. You may even want to hire designated security professionals to take care of your systems.
Similarly, you’ll want to keep employees up-to-date on any training and development they might need as the AI system at your company develops. This could be done, for example, in the form of monthly AI training sessions to let your employees know what is new with the system.
5. Limit access to sensitive data
As AI integrates into your operations, it will have to store and use sensitive and confidential data. While this poses a risk to customers and employees, a good idea is to limit who has access to this kind of information by enabling access controls. This will mean only specific individuals will have access to, and can utilize, this data.
This could help prevent any future data breaches, and could fortify your AI against other types of attack. Keeping an eye on how your system deals with requests for sensitive data is also key, and monitoring how the AI reacts to these kinds of requests will help you build solid internal protection for sensitive information.
Does business size make a difference?
While most logistics businesses will have similar security issues when using AI, some bigger businesses may face higher risk in certain areas.
They may be dealing with a bigger and more robust AI system because they have more ongoing operations. A bigger company could also mean more sensitive data to be wary of and which must be protected.
Furthermore, if you’re running a larger operation, you’ll most likely need a longer and more detailed AI policy, compared with smaller businesses, as more people will be using the system and will need delegating based on their responsibilities.
Bigger operations may also handle their IT systems in a different way to smaller businesses. If you are running a smaller operation, for example, you may have to outsource your IT infrastructure, and your security. While this could be a benefit, particularly if you have access to experts in the AI field, it also means you may not have direct control over any AI systems you adopt.
In cases like these, sharing your AI policy with every third-party your company outsources to and making sure you’re both on the same page is key.
Ultimately, if you’re dealing with the needs of a larger operation, it’s just as important to make sure all bases are covered. In particular, you must ensure that your data and systems are consistently monitored and updated, and that everyone in your company understands the day-to-day role AI will play.
Expert Tip - Vendor Red Flags
The AI hype can be overwhelming, but also exciting. There are now plenty of AI-powered logistics solutions out there, but here are some of the things I would keep an eye on when considering a new product:
You’ll want a vendor to be transparent about any data they’ve used to train an AI model, so you’ll be able to review whether it’s free from biases or prejudices, and is up-to-date and accurate. If they don’t want to disclose this, it’s a big concern.
Secondly, any inability to provide proof of previous results or testing shouldn’t be taken lightly. Again, you’ll want to see how the product has performed in the past and that it’s secure and efficient enough to handle the demands of your business.
Finally, a vendor should be able to provide an in-depth overview of its security measures and policies. AI is a great tool, but it’s liable to a host of security vulnerabilities, which could land your company in legal hot water. Any vendor should be able to give you guidance on how to assure best security practices are in place.
How Your Logistics Business Can Safely Use AI
AI can be intimidating, particularly as security is a top priority for many logistics businesses today. Our latest round of research showed that “data quality and security” ranked highest as the biggest AI blocker, outranking internal resistance (13%) and skills gaps (13%).
However, there are ways you can safely deploy AI within your company, and mitigate any of the potential risks. As long as you have procedures in place – where your AI can be safely monitored, tested, and reviewed regularly – then you’re already headed in the right direction.