Artificial intelligence (AI) needs data and a lot of it. Gathering the necessary information is not always a challenge in today’s environment, with many public datasets available and so much data generated every day. Securing it, however, is another matter.
The vast size of AI training datasets and the impact of the AI models invite attention from cybercriminals. As reliance on AI increases, the teams developing this technology should take caution to ensure they keep their training data safe.
Why AI Training Data Needs Better Security
The data you use to train an AI model may reflect real-world people, businesses or events. As such, you could be managing a considerable amount of personally identifiable information (PII), which would cause significant privacy breaches if exposed. In 2023, Microsoft suffered such an incident, accidentally exposing 38 terabytes of private information during an AI research project.
AI training datasets may also be vulnerable to more harmful adversarial attacks. Cybercriminals can alter the reliability of a machine learning model by manipulating its training data if they can obtain access to it. It’s an attack type known as data poisoning, and AI developers may not notice the effects until it’s too late.
Research shows that poisoning just 0.001% of a dataset is enough to corrupt an AI model. Without proper protections, an attack like this could lead to severe implications once the model sees real-world implementation. For example, a corrupted self-driving algorithm may fail to notice pedestrians. Alternatively, a resume-scanning AI tool may produce biased results.
In less serious circumstances, attackers could steal proprietary information from a training dataset in an act of industrial espionage. They may also lock authorized users out of the database and demand a ransom.
As AI becomes increasingly important to life and business, cybercriminals stand to gain more from targeting training databases. All of these risks, in turn, become additionally worrying.
5 Steps to Secure AI Training Data
In light of these threats, take security seriously when training AI models. Here are five steps to follow to secure your AI training data.
1. Minimize Sensitive Information in Training Datasets
One of the most important measures is to remove the amount of sensitive details in your training dataset. The less PII or other valuable information is in your database, the less of a target it is to hackers. A breach will also be less impactful if it does occur in these scenarios.
AI models often don’t need to use real-world information during the training phase. Synthetic data is a valuable alternative. Models trained on synthetic data can be just as if not more accurate than others, so you don’t need to worry about performance issues. Just be sure the generated dataset resembles and acts like real-world data.
Alternatively, you can scrub existing datasets of sensitive details like people’s names, addresses and financial information. When such factors are necessary for your model, consider replacing them with stand-in dummy data or swapping them between records.
2. Restrict Access to Training Data
Once you’ve compiled your training dataset, you must restrict access to it. Follow the principle of least privilege, which states that any user or program should only be able to access what is necessary to complete its job correctly. Anyone not involved in the training process does not need to see or interact with the database.
Remember privilege restrictions are only effective if you also implement a reliable way to verify users. A username and password is not enough. Multi-factor authentication (MFA) is essential, as it stops 80% to 90% of all attacks against accounts, but not all MFA methods are equal. Text-based and app-based MFA is generally safer than email-based alternatives.
Be sure to restrict software and devices, not just users. The only tools with access to the training database should be the AI model itself and any programs you use to manage these insights during training.
3. Encrypt and Back Up Data
Encryption is another crucial protective measure. While not all machine learning algorithms can actively train on encrypted data, you can encrypt and decrypt it during analysis. Then, you can re-encrypt it once you’re done. Alternatively, look into model structures that can analyze information while encrypted.
Keeping backups of your training data in case anything happens to it is important. Backups should be in a different location than the primary copy. Depending on how mission-critical your dataset is, you may need to keep one offline backup and one in the cloud. Remember to encrypt all backups, too.
When it comes to encryption, choose your method carefully. Higher standards are always preferable, but you may want to consider quantum-resistant cryptography algorithms as the threat of quantum attacks rises.
4. Monitor Access and Usage
Even if you follow these other steps, cybercriminals can break through your defenses. Consequently, you must continually monitor access and usage patterns with your AI training data.
An automated monitoring solution is likely necessary here, as few organizations have the staff levels to watch for suspicious activity around the clock. Automation is also far faster at acting when something unusual occurs, leading to $2.22 lower data breach costs on average from faster, more effective responses.
Record every time someone or something accesses the dataset, requests to access it, changes it or otherwise interacts with it. In addition to watching for potential breaches in this activity, regularly review it for larger trends. Authorized users’ behavior can change over time, which may necessitate a shift in your access permissions or behavioral biometrics if you use such a system.
5. Regularly Reassess Risks
Similarly, AI dev teams must realize cybersecurity is an ongoing process, not a one-time fix. Attack methods evolve quickly — some vulnerabilities and threats can slip through the cracks before you notice them. The only way to remain safe is to reassess your security posture regularly.
At least once a year, review your AI model, its training data and any security incidents that affected either. Audit the dataset and the algorithm to ensure it’s working properly and no poisoned, misleading or otherwise harmful data is present. Adapt your security controls as necessary to anything unusual you notice.
Penetration testing, where security experts test your defenses by trying to break past them, is also beneficial. All but 17% of cybersecurity professionals pen test at least once annually, and 72% of those that do say they believe it’s stopped a breach at their organization.
Cybersecurity Is Key to Safe AI Development
Ethical and safe AI development is becoming increasingly important as potential issues around reliance on machine learning grow more prominent. Securing your training database is a critical step in meeting that demand.
AI training data is too valuable and vulnerable to ignore its cyber risks. Follow these five steps today to keep your model and its dataset safe.
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