Artificial intelligence (AI) and machine learning (ML) are making waves across industries.
We are beginning to see these incredible technologies pop up in more areas of our lives, from self-driving cars to healthcare, finance, and even customer service.
But as more and more companies roll out these technologies en masse and start intertwining them with critical business operations, they’re also introducing new security risks.
Here are seven of the most common data security threats facing AI/ML systems on the market today.
1. Model poisoning
Model poisoning is a form of adversarial attack to manipulate the outcome of machine learning models.
Threat actors can try to inject malicious data into the model, which will cause the model to misclassify the data and make bad decisions.
For example, engineered images can trick machine learning models into classifying them into a different category than humans originally classified them (e.g., labeling an image of a cat as a rat).
Or in the instance of an AI writer switching the tagging of the Spanish language to Chinese.
Unfortunately, this is an effective way to fool AI systems because it’s impossible to tell if a particular input will result in a bad prediction until the output.
By implementing strict access management policies to limit access to training data, businesses can prevent bad actors from tampering with model inputs.
2. Data privacy
Data privacy is a sensitive matter that needs extra attention and care.
It’s an even bigger issue when your models require data from minors. For example, with some debit card options for teens, banks must ensure their security standards meet regulatory compliance requirements.
But all companies that collect customer information in any way, shape, or form need to have a data protection policy in place. That way, customers know what an organization does with their data.
Traditionally, businesses gain customers’ trust by showcasing their privacy policies on their websites. This process allows customers to contact them should they have any questions.
For instance, Tailor Brands does a great job of showcasing their privacy policy. They have a dedicated page that explains how they use customer data. This gesture helps maintain a positive relationship with users.
However, how do users know if their data gets funneled into AI algorithms? Very few (if any) privacy policies include this information.
As we progress into an AI-driven era, it’ll be important for individuals to understand how businesses use AI, its capabilities, and its impact on their data.
Likewise, adversaries may try to steal sensitive data sets containing personal information such as credit card numbers or social security numbers using malware. As an organization, you must conduct regular security audits and implement robust data protection practices at all stages of AI development.
Privacy and security risks can occur at any stage of the data lifecycle, so it’s important to have a security strategy for all stakeholders.
3. Data tampering
The risks posed by data manipulation, theft, and exposure are amplified in the context of AI and ML.
Why? Because these systems are designed to make decisions based on large amounts of data that malicious actors could have manipulated or modified.
For instance, a malicious actor could use image editing tools or software to alter an image used in training an AI system. Or modify information in the dataset itself (e.g., altering locations). The tampering would result in misclassifications when the model runs with real-world examples.
Bias is another major concern with advancements in AI.
AI algorithms and machine learning programs are supposed to be objective and unbiased, but how can we ever really know?
The threat of data tampering that feeds AI algorithms and machine learning programs is a huge problem for which there’s no easy solution, but it needs attention.
How do you ensure that data feeding into an algorithm is accurate, reliable, and not tampered with?
How do you ensure that the data isn’t used in unsavory ways?
And what does all this mean for a future where AI algorithms are driving our cars, making medical decisions about us based on our genetic makeup, and deciding who gets a loan or not?
All of these questions are very real ones that have no clear answers currently.
4. Insider threats
Regarding data security, insider threats are the most dangerous and costly.
The latest Cost of Insider Threats: Global Report reveals that the number of insider threat incidents has risen by 44% over the past two years, costing organizations $15.38 million per incident.
Insiders can access your company’s sensitive or confidential information and use it for their own benefit.
Insider threats can take many forms:
- Some employees steal data and sell it to competitors
- Some employees steal data to blackmail the company
- Some employees even steal data because they feel resentful
- Some employees are simply bored
What makes insider threats so dangerous is that they’re not necessarily motivated by money, but by other factors like revenge, curiosity, or human error.
Because of this, they’re harder to predict and stop than external hackers (who tend to have a monetary motive).
Insider threats are the most detrimental to companies that deal with the health and wellness of others. Take HelloRache, for example, a leading provider of healthcare virtual scribes.
HelloRache uses technology in favor of virtual scribes, so they can remotely accompany doctors as they care for their patients, taking notes and doing the paperwork.
But if an insider finds their way, it can cause the connections to fail, or even worse, to monitor calls and obtain medical information from patients.
5. Deliberate attacks
Are you running your business with AI and ML? If so, you’re not alone.
A 2021 study suggests that 86% of CEOs use AI as a “mainstream” technology in their office.
C-level executives are investing in these data-driven technologies to help make better decisions, improve customer service, and reduce costs.
But there’s a problem: deliberate attacks on AI systems are rising, and they can cost businesses millions of dollars without the proper controls in place.
A deliberate attack is when someone tries to disrupt an organization’s operations by tapping into its AI system.
So why do people launch deliberate attacks on AI systems? Usually, it gives them a competitive advantage over their rivals.
For example, suppose you are a courier service and you want to know how your top opponent plans to pitch their delivery services to Amazon. In that case, you can generate a deliberate attack to harm their chance of renewing the contract.
Or you can use that information as leverage during negotiations as part of your strategy for winning the contract yourself.
In the face of deliberate attacks, data security threats to AI and ML can be especially damaging. The data used in these systems is often proprietary — and therefore of high value.
Data security threats to AI and ML aren’t just about stealing information — they’re about stealing a company’s ability to compete.
6. Mass adoption
AI and machine learning are growing industries, which means they’re also still vulnerable.
As they grow in popularity and worldwide adoption, hackers will find new ways to interfere with the inputs and outputs of these programs.
AI and ML are such complex systems that it’s tough for developers to know how their code will behave in every situation. And when there’s no way to predict what could happen, it can be difficult to prevent it from happening.
The best way to protect your company from AI-related security threats is by combining good coding practices, testing processes, and frequent updates as new vulnerabilities are discovered.
And, of course, don’t let up on the traditional forms of cybersecurity prevention, like using a colocation data center to protect your servers from malicious attacks and outside threats.
7. AI driven attacks
In the past few years, we’ve seen bad actors start weaponizing AI to help them design and conduct attacks.
First, let’s talk about how threat actors use AI to design their attacks.
In this context, “designing an attack” refers to choosing a target, determining what data they’re trying to steal or destroy, and then deciding on a delivery method.
Then, to conduct the attack, malicious actors can use machine learning algorithms to find a way around security controls. Or they use deep learning algorithms to create new malware based on real-world samples.
IT and security experts must constantly defend against smarter and smarter bots, which are very hard to stop. A new type of attack emerges as soon as they block a different one.
In short, AI is making it easier to mimic trusted actors or find loopholes in current security safeguards.
Wrapping up
While AI and ML systems are becoming more prevalent in today’s digital world, they’re still at an early adoption stage.
As such, companies need to understand the risks associated with using these technologies and take the necessary steps to protect themselves from data security threats.
Organizations must use security solutions that provide ultra-secure confidential computing environments to secure their AI applications and models.
In conjunction with the right cybersecurity solutions, confidential computing can provide robust end-to-end data protection for AI applications of all sizes.
