What Is AI Security?

In one of the recent interviews, Elon Musk has warned humanity that artificial intelligence could lead to “civilization destruction”. While talking about AI, so advanced that it will threaten our society, to most experts seems preliminary, one thing is certain: we depend on artificial intelligence as much as AI depends on us.

Artificial intelligence systems run in your phone to help you capture better pictures, find relevant materials online, and optimize your storage space. But it all comes with a cost. To be able to enjoy all of these benefits, you have to sacrifice your privacy.

AI also is used at your local bank to decide whether your credit profile is convincing enough to issue you a credit to start your business. Your potential employer may use AI to estimate your level of competence and match it against the profiles of other candidates. Finally, if you ever do anything illegal, an AI risk prediction system may be used to establish how likely you are to recommit crime and, therefore, decide on your punishment.

And here comes the big question — can we as a society trust these systems? While there are many ethical and legal questions surrounding AI systems, there is one in particular that I want to discuss today. Can you be sure that nobody, including your ex, career rival, or the government can steal the data that you entrust the algorithm with or tamper with its decisions concerning your life?

The short answer is: no. No, you can’t be sure. The long answer: the situation is complicated.

Let’s find out what kind of challenges AI security faces today and how they could be solved.

The definition of AI security

AI security refers to the measures that one can take to protect AI systems from cyber attacks, data breaches, and other security threats. As AI systems become more prevalent in businesses and homes, the need for robust security measures to protect these systems has become increasingly important.

AI system security checks should happen at three dimensions:

• Software level. To ensure that your AI software is secure, you need to do classic code analysis, investigate programming vulnerabilities, and conduct regular security audits.

• Learning level. Learning level vulnerabilities are exclusive to AI. You need to protect the databases and control what kind of data gets there and monitor unusual performance of the model.

• Distributed level. If the AI model is composed of many components that do their thing and then merge the results for the final decision, then you need to make sure that at every instance the system works as it should.

Why is AI security important?

It’s quite obvious that by tampering with a self-driving car’s AI, the attackers may cause it to behave unpredictably on the road and cause a car crash. A great story for a new Netflix’s thriller. However, the consequences of somebody hacking into your car may be much less subtle. Hackers may steal data about your transportations to break into your home when you’re at work. Or, even sell it to some corporations for marketing purposes. You might not even know that somebody got their hands on your private data.

The thing with AI technologies even more than with any other software systems is that they knows too much about you. For example, recently there has been a big scandal involving ChatGPT and Samsung employees. They have allegedly leaked confidential company information to the AI-powered chatbot. How many things does ChatGPT know about you? Have you trusted it with your name, address, or perhaps even credit card number and email contact list?

However, the challenge is that regular security measures that are used to protect other types of software aren’t always applicable to AI. For example, you can protect your account in some cloud service with a secure password and a two-factor authentication. With AI, there are specific types of attacks such as adversarial attacks that don’t really care whether your password was secure or not.

Let’s have a closer look at AI security attacks and what can be done to prevent them.

Types of AI security threats

The following are some of the most common types of AI security threats:

Malware and ransomware attacks

Malicious software can infect an AI system and steal data or hold it hostage for a ransom. This type of attack can cause significant financial damage to businesses and individuals.

Data breaches

Hackers can gain unauthorized access to an AI system and steal sensitive data such as personal information or business secrets. This type of attack can lead to identity theft, financial fraud, and other serious consequences.

Adversarial attacks

This involves manipulating AI systems by introducing false data or images to trick the system into making incorrect decisions. Adversarial attacks can be used to bypass security measures and gain access to sensitive data.

Insider threats

Employees or contractors with access to an AI system can intentionally or unintentionally cause security breaches. This type of attack can be particularly damaging as insiders have knowledge of the system and its vulnerabilities.

Denial-of-service (DoS) attacks

Attackers can overload an AI system with traffic, causing it to crash or become unavailable. This type of attack can disrupt business operations and cause financial losses.

Physical attacks

Hackers can physically access an AI system and tamper with its hardware or software components. Physical attacks can be difficult to detect and can cause significant damage to the system.

Social engineering attacks

Attackers can use social engineering tactics such as phishing emails or phone calls to trick individuals into revealing login credentials or other sensitive information. Social engineering attacks can be used to gain access to an AI system and steal data.

IoT security threats

AI systems that are connected to the internet of things (IoT) can be vulnerable to security threats from other connected devices. This type of attack can be used to gain access to an AI system and steal data or cause damage to the system.

How to protect your AI systems

Protecting AI systems from hacker attacks is hard because these systems are complex but also because attackers use AI as well. However, there are some measures you can take to protect AI systems from security threats.

Educate your team members

As in Samsung’s example above, sometimes the threat might come not from the outside but the inside of the company. All employees must be instructed on the basics of cybersecurity to not make silly mistakes that make the organization’s system vulnerable such as sending confidential information to each other in social media or storing passwords on their computer. In fact, 98% of all cybersecurity attacks are social engineering attacks, which means that they exploit human factors rather than technical vulnerabilities. AI security attacks are no different.

Monitor for unusual activity

Regular review of the AI system’s security protocols and conducting penetration testing can help to identify any potential vulnerabilities. These measures are aimed at making sure that the technical side of the projects is secure. A methodology that has been proved the most effective when protecting AI systems is MLOps.

Since artificial intelligence systems are created by ML engineers using ML techniques, MLOps helps to establish a process of taking ML models to production, supporting, and monitoring them. MLOps allows the user to constantly monitor the model’s performance and report about unusual activities and suspicious actions. Set up alerts to notify administrators of any unusual activity on the AI system, such as multiple failed login attempts or unusual data access patterns.

Use encryption

One way to prevent data leakage is to encrypt all sensitive data stored on the AI system to prevent unauthorized access in case of a breach. No encryption is absolutely safe. However, according to statistics, the average savings from each attack with robust encryption accounts to $1.4 million. By using encryption, you protect your customers’ data and avoid potential damage to your reputation in the future.

Limit access

Finally, a simple but effective measure that companies can implement to increase their AI security is to simply limit access to the AI system to only those who need it, and ensure that each user has appropriate permissions based on their role. Like educating your team, this measure helps to bring the human factor to the minimum.

Conclusion

The future of AI security is likely to focus on more advanced technologies such as machine learning and artificial intelligence itself. As AI systems become more complex and sophisticated, the security measures used to protect them will need to evolve as well. By staying ahead of emerging threats and investing in advanced security technologies, businesses can help ensure that their AI systems remain secure and protected from cyber attacks.