As of today, Artificial Intelligence and Machine Learning have become an integral part of data-driven organizations. Therefore, the business must secure them from any harm. However, developing a formidable cybersecurity plan is often expensive and time-consuming. But in the case of AI and ML, it does not matter.
Like any other technology, AI and ML have the same chances of exploitation and misconfiguration. Moreover, it also has its unique risks. The vulnerability of an enterprise only grows with more focus on AI technology. AI has two critical assets: big data and data models. AI systems use big data to learn and train, while the data model is the outcome of training the algorithm with big data.
Thus, data-driven companies must design a system to secure both big data and data models. We will discuss the top four security threats for Artificial Intelligence and Machine Language that can affect data-driven companies.
Corruption and Poisoning of Data:
The priority of data-driven organizations is to ensure the integrity and reliability of datasets since ML systems rely on them. However, if the organization fails to do so, its AI and ML system will malfunction and provide false or unreliable predictions.
It is how an attacker corrupts or poisons the data of an organization. This attack can manipulate the learning algorithm of AI and ML systems which can impact businesses negatively. To prevent this, companies should adopt strict PAM policies to minimize the access hackers have to train data within a closed computing environment.
The internet is the primary part of any AI and ML system. Therefore, almost all systems are connected to the internet for learning, which is an open invitation for attackers. They can use the internet as a way to reach AI and ML systems to manipulate it.
Bad actors can feed the system false input or influence it to provide the wrong output, misleading ML machines. IT engineers can secure systems from this attack in many ways. They can streamline and secure system operations or maintain records of data ownership.
Maintaining the privacy and confidentiality of big data is essential for researchers. And it becomes inevitable to ignore as the machine learning model is the one that builds the data. Attackers can launch data extraction attacks. Consequently, the whole machine learning model can compromise, and confidential information may get stolen.
Moreover, attackers may attack with smaller sub-symbolic function extraction attacks that need fewer resources and efforts. Thus, protecting the system more from these attacks becomes a priority of an organization. They can safeguard their system by setting some strict policies or by installing programs that prevent data extraction attacks.
System manipulation is one of the most common attacks developed specifically to attack algorithms. By feeding AI and ML systems with malicious inputs, the attackers force the system to make false predictions. This attack compels the system to work with the wrong data, which makes it unreliable.
In other words, it shows the machine a picture that, in reality, does not exist. The effects of this attack are destructive, as they are long-lasting and unthinkable. Thus, making this threat more damaging and dangerous than other ML security risks.
As the number of cyber-attacks keeps increasing, cybersecurity has become vital for all organizations worldwide. Organizations must secure their AI applications and ML models by implementing security solutions that support hyper-secure confidential computing environments.
Organizations can combine confidential computing with appropriate cybersecurity solutions to create a security system of robust end-to-end data protection in the cloud. Thus, these are the four security threats that can cause critical damage to AI and ML systems. Tell us what you think about this article in the comments, and also check out our articles for more information. Adios!!!