Artificial intelligence applications face seven major data security threats

Artificial intelligence technologies such as autonomous driving technology, intelligent assistants, facial recognition, smart factories, and smart cities are now widely implemented. These incredible technologies are rapidly changing our lives. However, security incidents in related fields are also increasing rapidly, which makes researchers and users increasingly worried about the security of artificial intelligence. The dividends brought by the application of artificial intelligence and the security risks caused by it are like two sides of the same coin, requiring the entire industry to pay close attention and find effective response methods.

Recently, security researchers sorted out and summarized 7 data security threats that artificial intelligence technology often faces in practical applications.

Threat 1 Model poisoning

Model poisoning is a form of adversarial attack designed to manipulate the results of a machine learning model. Threat actors can attempt to inject malicious data into the model, causing the model to misclassify the data and make incorrect decisions. For example, engineered images can fool 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 mouse). Research has found that this is an effective way to trick AI systems because it is impossible to tell whether a specific input will lead to an incorrect prediction before outputting it.

To prevent malicious actors from tampering with model inputs, organizations should implement strict access management policies to limit access to training data.

Threat 2 Privacy Leakage

Privacy protection is a sensitive issue that requires additional attention and attention, especially when the AI ​​model contains data of minors, the problem is more complicated . For example, with some debit card options for teenagers, banks must ensure their security standards meet regulatory compliance requirements. All companies that collect customer information in any form or means are required to have a data protection policy in place. This way, customers can know how the organization handles their data. However, how do users know if their data is flowing into the application of artificial intelligence algorithms? Very few (or almost none) privacy policies contain this information.

We are entering an era driven by artificial intelligence, and it will become very important for individuals to understand how enterprises use artificial intelligence, its capabilities and its impact on data. Likewise, attackers may attempt to use malware to steal sensitive data sets containing personal information such as credit card numbers or Social Security numbers. Organizations must conduct regular security audits and implement strong data protection practices at all stages of AI development. Privacy risks can occur at any stage of the data lifecycle, so it is important to develop a unified privacy security strategy for all stakeholders.

Threat 3 Data Tampering

The risks caused by data manipulation, exposure and tampering are being continuously amplified in the context of large-scale application of AI, because these systems require analysis and decision-making based on large amounts of data , and this data can easily be manipulated or tampered with by malicious actors. In addition, algorithmic bias is another major problem faced in the large-scale application of artificial intelligence. AI algorithms and machine learning programs are supposed to be objective and unbiased, but they are not.

The threat of data tampering with artificial intelligence algorithms is a huge problem that has no easy solution, but it needs attention. How to ensure that the data fed into the algorithm is accurate, reliable, and not tampered with? How to ensure that data is not used in objectionable ways? All of these questions are very real issues, but the industry has not yet found clear answers.

Threats 4 Insider Threats

In terms of data security, threats from insiders are undoubtedly the most dangerous and costly type. According to the latest Cost of Insider Threats: A Global Report, the number of insider threat incidents has increased by 44% over the past two years, with the average cost per incident being $15.38 million.

What makes insider threats so dangerous is that their motivation is not necessarily financial, but may be motivated by other factors such as revenge, curiosity, or human error. Because of this, they are harder to predict and stop than external attackers.

For companies involved in the health of citizens, insider threats are undoubtedly more harmful. Take the healthcare service provider HelloRache as an example. The company uses AI-based virtual scribes (assistants that assist doctors with computer-related tasks) tools so that they can remotely assist doctors in caring for patients and documenting conditions. But if insiders find a way, it may cause the system to be incorrectly connected, and even monitor and obtain patient medical information.

Threat 5 Targeted Deliberate Attack

A research data shows that 86% of enterprise organizations have begun to regard artificial intelligence as a "mainstream" technology for future digital development and have increased investment in various data Powered by AI technology to help businesses make better decisions, improve customer service and reduce costs. But there’s a problem: Deliberate attacks on AI systems are on the rise, and without controls in place, they could cost organizations upwards of millions of dollars.

A "deliberate attack" refers to the purposeful attempt to disrupt an organization's business operations by hacking into an artificial intelligence system with the goal of gaining a competitive advantage over an opponent. Data security threats to AI and ML can be particularly damaging in deliberate attack scenarios. Because the data used in these systems is often proprietary and of high value. When artificial intelligence systems are targeted and deliberately attacked, the consequences are not just theft of data, but the destruction of the company's competitiveness.

Threat 6 Mass Adoption

Artificial intelligence is a rapidly growing industry, which means they remain vulnerable. As AI applications become more popular and adopted around the world, hackers will find new ways to interfere with the input and output of these programs. AI is often a complex system, making it difficult for developers to know how their code will perform in various application situations. When it's impossible to predict what will happen, it's difficult to prevent it from happening.

The best way to protect your enterprise from large-scale application threats is to combine good coding practices, testing processes, and timely updates when new vulnerabilities are discovered. Of course, don't abandon traditional forms of cybersecurity precautions, such as using colocation data centers to protect servers from malicious attacks and external threats.

Threat 7 AI-driven attacks

Researchers have found that malicious attackers are weaponizing artificial intelligence to help them design and carry out attacks. In this case, "designing an attack" means selecting a target, determining what data they are trying to steal or destroy, and then deciding on a delivery method. Malicious attackers can use machine learning algorithms to find ways to bypass security controls to carry out attacks, or use deep learning algorithms to create new malware based on real-world samples. Security experts must constantly defend against increasingly intelligent bots because as soon as they thwart one attack, a new one emerges. In short, AI makes it easier for attackers to find holes in current security safeguards.

Reference link:

​​https://www.php.cn/link/d27b95cac4c27feb850aaa4070cc4675​​

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