Artificial intelligence (AI) requires data and a lot of data. In today’s environment, collecting the necessary information is not always a challenge, with many public data sets every day and a lot of data is generated every day. But, making sure it is another matter.
The huge size of the AI training dataset and the impact of AI models have attracted the attention of cybercriminals. With reliance on AI, teams developing this technology should practise caution to ensure they ensure the security of training data.
Why AI training data needs better security
The data you use to train your AI model may reflect people, businesses, or events in the real world. Therefore, you can manage a large amount of personally identifiable information (PII), which can cause serious privacy vulnerabilities if exposed. In 2023, Microsoft suffered such an incident and was accidentally exposed 38 private information In an AI research project.
AI training datasets may also be vulnerable to more harmful adversarial attacks. Cybercriminals can change the reliability of machine learning models by manipulating training data to gain access. This is a type of attack called data poisoning, and AI developers may not notice that the effect is too late.
Research shows poisoning Only 0.001% of the dataset Enough to destroy the AI model. Without proper protection, once the model sees real-world implementation, it can lead to serious impacts. For example, a damaged autonomous driving algorithm may not notice pedestrians. Additionally, AI tools for resume scanning may produce biased results.
In less severe cases, an attacker can steal proprietary information from the training dataset during industrial espionage. They can also lock authorized users in the database and ask for ransom.
As AI becomes more important to life and business, cybercriminals will gain more from the target training database. In turn, all of these risks become worrying.
5 Steps to Ensure AI Training Data
Given these threats, take security seriously when training AI models. Here are five steps to secure your AI training data.
1. Minimize sensitive information in training data set
One of the most important measures is to remove the number of sensitive details in the training dataset. The less PII or other valuable information in your database, the less target the hacker is. If violations do occur in these cases, it will also be reduced.
During the training phase, AI models usually do not need to use real-world information. Synthesized data is a valuable option. The model trained in synthetic data can be It doesn’t seem to be more accurate More than others, so you don’t have to worry about performance issues. Just make sure the generated data set is similar to real-world data.
Additionally, you can delete existing sensitive detail datasets, such as people’s names, addresses, and financial information. If your model requires such factors, consider replacing them with alternate virtual data or exchanging them between records.
2. Restricted access to training data
Once the training dataset is compiled, access to it must be restricted. Following the principle of at least privilege, which states that any user or program can only access what is needed to do their work correctly. No one who is not involved in the training process needs to view or interact with the database.
Remember that privilege restrictions are only valid if you also implement a reliable method to authenticate users. The username and password are not enough. Multi-factor authentication (MFA) is essential because it stops 80% to 90% of all attacks For accounts, not all MFA methods are equal. Text-based and application-based MFAs are often safer than email-based alternatives.
Make sure to limit software and devices, not just users. The only tool that can access the training database should be the AI model itself and any program you use these insights during training.
3. Encrypt and backup data
Encryption is another crucial protection measure. While not all machine learning algorithms can actively train encrypted data, you can encrypt and decrypt it during the analysis process. Then, once you’re done, you can rejoin it. Alternatively, investigations can analyze the model structure of information during encryption.
It is important to keep a backup of your training data in case anything happens. The backup should be different from the primary replica. Depending on the critical tasks of the dataset, you may need to keep an offline backup and one in the cloud. Remember to encrypt all backups, too.
When it comes to encryption, please choose your method carefully. Higher standards are always desirable, but you may need to think of quantum-resistant encryption algorithms as an increase in threat to quantum attacks.
4. Monitoring access and usage
Even if you follow these other steps, cybercriminals can break your defense. Therefore, you have to constantly monitor access and usage patterns through AI training data.
An automated monitoring solution may be needed here, as few organizations can pay attention to suspicious activity 24/7. When abnormal situations occur, automation is also much faster in action, resulting in $2.22 Lower data breach cost On average, faster and more efficient response.
Record each time someone or something accesses a dataset, requests access to it, change, or otherwise interact with it. In addition to observing potential violations in this activity, it is also subject to a larger trend review on a regular basis. The behavior of authorized users may change over time, and if you use such a system, you may need to transfer access or behavioral biometrics.
5. Regularly reassess risk
Similarly, AI development teams must realize that cybersecurity is an ongoing process, not a one-time solution. Attack methods evolve rapidly – some vulnerabilities and threats may slide through cracks before they are noticed. The only way to stay safe is to reevaluate the safety posture regularly.
At least once a year, view your AI model, its training data, and any security incidents that affect it. Audit data sets and algorithms to ensure they work properly and are free from poisoning, misleading, or other harmful data. Adjust your security controls as needed to suit anything unusual you notice.
It is also beneficial for security experts to test your defense by trying to break it down. Apart from 17% of cybersecurity professionals Pen tests at least once a year, and 72% do say they think this has stopped the organization’s violations.
Network security is the key to secure AI development
Ethical and secure AI development is becoming increasingly important as potential issues surrounding machine learning become increasingly prominent. Ensuring that training databases are a critical step to meet demands.
Artificial intelligence training data is too valuable and it is easy to ignore its network risks. Follow these five steps today to ensure your model and its dataset are secure.
