Just recently, Grand View Research published a report valuing the global artificial intelligence market size at $390.91 billion.
Looking ahead, the market is expected to expand at a 31.5% CAGR, translating to a volume of more than $3.5 trillion by 2033. This is no surprise considering how AI has woven itself into almost every facet of people’s daily lives. In fact, it’s nearly impossible to think of a single industry or service that this technology hasn’t touched.
However, as more industries open their doors to artificial intelligence, the risk it carries also increases. Unfortunately, many of these risks are not always visible to the surface. And when an AI system is compromised, it can have devastating consequences. Imagine, according to IBM, a cyberattack can cost you up to $4.88 million to recover. At a time when operational costs are soaring at unprecedented rates, these are not costs you want to incur.
Hackers are awake, increasingly targeting artificial intelligence models with the aim of manipulating them to produce misleading outputs and automate cyberattacks. This is why the aspect of AI security is crucial. And perhaps you are thinking: what is AI security? Well, if you have such questions, reading this article will really be handy.
The meaning of artificial intelligence security
It’s actually true that artificial intelligence can help fight cyberattacks. Using its unmatched computational ability, organizations can assess and detect suspicious patterns in large data files in real time. And according to Cobalt, over seven in ten cybersecurity professionals want to change focus to an AI-powered preventive strategy. But as much as this technology can help fight cyberattacks, it also has its challenges.
As already mentioned, hackers can manipulate and use it to automate cyberattacks. And this is not something that’s far off. A recent publication by Darktrace says over seven in ten organizations are suffering significantly from AI-related threats. But beyond just using AI to automate cyberattacks, hackers can also compromise the technology itself. Remember, artificial intelligence systems are not like traditional software that runs on deterministic logic. These systems base their decisions on patterns learned from massive datasets, which introduces unique vulnerabilities.
Think of it as an adversarial attack exploiting tiny changes in inputs that could cause a model to misclassify information. In an industry like healthcare, where decisions can literally be a matter of life and death, even a small misclassification could lead to misdiagnoses or the exposure of sensitive patient data. Imagine how dangerous it can be when an AI model that’s supposed to assist in medical imaging is tricked into overlooking a tumor. Such cases highlight what AI security really is and why it’s a necessity.
The increasing popularity of data poisoning
As you may know, an artificial intelligence system is only as good as the data it’s trained on. The days when organizations would only focus on the system’s performance are long gone. Today, concerns like where the training data comes from and whether it can be trusted have not ceased. And they actually make sense because the data pipeline often includes third-party contributors, who may intentionally or unintentionally introduce corrupted or biased data.
Sadly, the effects of poisoned data aren’t always visible. The model might look fine and even pass testing. But it’s compromised in ways that could be hard to detect, and the consequences can be far-reaching. Take, for instance, a poisoned AI model that approves loans for high-risk applicants or denies them to deserving customers.
In a study titled Detecting and Preventing Data Poisoning Attacks on AI Models, Halima Kure and other researchers attributed a 27% decline in classification accuracy to poisoned data. Another report by Yahoo Finance associated up to 26% of UK and US businesses with experiencing operational disruptions due to compromised artificial intelligence systems. Here’s a brief overview of how this poisoning works:
- A malicious person injects fictitious and deceptive data
- They could also alter genuine data points
- Others may remove critical points to create gaps, leading to poor model generalization.
Protecting your models from such attacks
One of the ways you can strengthen the security of your models is by adopting adversarial training. Intentionally expose your models to inputs deliberately perturbed to cause misclassification during the training process. This helps your models to generalize better and become less sensitive to manipulative inputs in the future.
Another option could be prioritizing explainable AI (XAI). After all, users want to understand why a model made a particular decision. That’s why it’s no surprise that, according to Bismart, global consumer trust in artificial intelligence has declined from 61% to 53% within the past few years because of growing fears surrounding biased decisions.
At the same time, governments have started to emphasize the growing need for transparency in AI systems. In the European Union and Spain, for instance, failure to ensure transparency can result in penalties of up to €35 million for severe non-compliance cases. But with XAI, you make AI decisions more interpretable and simplify detecting unusual behaviours.
Therefore, as much as artificial intelligence has become an integral part of modern life, it comes with great responsibility. Just a slight oversight in AI security can cascade into significant financial losses or even threats to human safety. As such, implementing proper defenses like adversarial training helps ensure the technology remains a tool for progress rather than a source of vulnerability.
