What is AI Data Security? Examples & Best Practices

In the last few years, AI has taken over the complete tech industry. This includes companies using LLMs (Large Language Models) to solve various business and day-to-day problems. It’s not just tech giants such as Apple, Google, and Microsoft that are using and integrating artificial intelligence in their production. Small and medium-sized companies are also moving in the AI race. With so many users and companies using AI, the amount of data it processes has significantly increased, making it a target for threat actors. AI systems use data in multiple steps, starting from training data to users entering information to get a response from them. Due to the sensitive data that AI systems deal with, securing them and the data becomes important. This is where AI data security comes into the picture.

In this blog post, we will discuss the role of data in AI (Artificial intelligence) and the challenges that organizations may face with data security in AI. We will also explore the best practices for implementing AI data security for better results and how SentinelOne can be used for the same.

Overview of AI and Data Security

Artificial Intelligence, commonly known as AI, is the area of computer science that focuses on the creation of intelligent machines that resemble natural human intelligence and logical power. AI can essentially perform human cognitive functions often faster and more accurately than people could.

We know that AI is data-dependent. Data is what keeps AI systems running and allows them to learn and predict new information in an improved way over time. Machine learning, a part of artificial intelligence, is used by computer systems to learn from data without being programmed particularly for that. AI systems perform better with different kinds of data.

The Role of Data in AI

The importance of data in AI is significant. It is applied at different stages to help with AI development and processing.

• Training: The first phase of training is where AI algorithms learn from data to identify patterns and make predictions.
• Testing: Multiple datasets are used to test the capability and efficiency of the model.
• Operation: AI systems process fresh data to help with real-time decision-making or predictions once deployed.
• Improvement: Most AI systems are trained on new data to enhance algorithms and improve performance.

Importance of Data Security in AI

There are multiple factors that show the importance of data security and privacy when dealing with machine learning systems. AI deals with sensitive and confidential information, which is why it is important to protect the privacy of this data.

Compromised data risks the integrity of AI models, and failures in applications such as healthcare or finance can result in severe consequences. AI systems also need to comply with data protection regulations, such as PCI DSS, HIPAA, etc. Some of the most common threats to AI are as follows:

• Data manipulation: Attackers can use specially modified training data to introduce biases and reduce the accuracy of the AI model.
• Insider threats: This threat is caused by a person who attacks the AI system from inside the organization. Such a person can steal and sell data, modify models to intercept results, and degrade overall system performance.
• Data breaches: Attackers usually gain access to large amounts of valuable data, such as personal information, financial data, trade secrets, or information about the infrastructure from a data breach.

Identifying Threats to AI Data Security

In order to implement AI data security, it is important for organizations to understand the different kinds of threats to it. Let’s discuss some of the following:

• Data Poisoning (How Attackers Manipulate Training Data)

Data poisoning is a serious threat to AI systems. Creating false examples is basically where people play with the training data of AI models. Attackers can easily change the behavior or decision-making process of AI systems by adding fake data points.

One example is an image recognition system, where an attacker may inject mislabeled images during the training. Such mislabeled or faulty data could cause the AI to incorrectly classify objects in real-world use cases, with extremely damaging consequences like practicing autonomous driving or making a medical diagnosis.

• Model Inversion Attacks (Retrieving Sensitive Data from Models)

Model inversion attacks are another important threat to AI data security. Such attacks try to deconstruct or reverse engineer the AI model in order to gain information about patterns used in training data.

Attackers essentially call the model multiple times with some cleverly chosen inputs and study its outputs to understand probable data used for training the model. This can be a serious privacy concern, especially when the training data includes sensitive personal or business information such as medical records and financial details.

• Adversarial attacks (Manipulating AI Models Through Input Changes)

Adversarial attacks target AI inputs to force their errors. Data poisoning happens during training, while adversarial attacks are performed on deployed models. Attackers carefully create inputs designed specifically to trick the AI model by changing very small values that are nearly different from real data but can make a huge difference in any data-based model.

A typical example of this is slightly tweaking an image to completely mistarget it by a classification AI, such as making a stop sign become classified as another speed limit one. These types of attacks can pose a danger to the security-critical applications in which AI systems form part of their environment and may result in errors being made by an affected system.

• Automated Malware

Automated malware is AI-powered malware that can execute a targeted attack. It can be used to avoid threat detection as well and improve the effectiveness of infection by identifying optimal time and suitable circumstances to deliver a payload.

DeepLocker is a proof-of-concept AI-powered malware that hides its malicious intent within an application, and it does not unlock its malicious payload for execution until it reaches a specific victim by crunching on a prespecified marker.

How to Secure AI Models

AI models require security in both the training phase and when it is deployed. Let’s go over some common strategies to secure AI models for proper AI data security in both phases.

Securing AI Model Training

Securing AI model training is the primary AI safety technique, which is based on reliance and training. Training in secure environments is important. They need to be isolated and controlled systems that have controlled access mechanisms. For AI training, cloud-based solutions come with a number of security measures that make it difficult for data to get stolen or leaked.

Before securing the AI, it’s important to ensure data validation and sanitization. This includes looking at data inputs in detail for irregularities, discrepancies, or potential attack vectors. Using ways such as outlier detection and data cleaning can maintain an approximation to integrity in training datasets, which will act as a fundamental system preventing poisoning attacks.

This involves the use of optimization techniques, which enables us to craft models that are less vulnerable to attacks. Cross-validation and techniques like regularization help to improve the generalization ability of the model and increase its resistance against adversarial attacks. Adversarial training works by stimulating potential attack scenarios for AI to learn and recognize.

Protecting Deployed AI Models

The challenges for an AI model when it is deployed are quite different. You need to make sure only the intended users can execute calls and that the model hasn’t been tampered with as it makes its way through various services/devices/gateways in a served pipeline that includes authentication & encryption.

Validation and sanitization are mandatory for deployed models. All input should be thoroughly validated and sanitized before it is passed to the AI for processing. This helps prevent all kinds of prompt injection attacks and ensures that your model is only fed clean data.

Anomaly Detection

Anomaly Detection Systems are monitoring systems that can run in real time and check for abnormal patterns & behavior. For example, there is a sudden increase in the flow of requests that do not look like natural load, an external request that comes from IP, which is prohibited, etc. It will give you information on what can possibly be wrong without providing enough details as to the actual nature/type of attack. They always monitor unexpected outputs, abnormal input patterns, or large deviations in normal behavior to have an immediate answer on possible risks and deal with the situation.

Different Ways to Keep AI Data Private

With AI systems being common, it’s important to protect the privacy of the data used to train the AI systems. Let’s discuss a few different ways to keep AI data secure:

Anonymization and Pseudonymization

Anonymization is used to erase or encrypt personally identifiable information in datasets, basically turning that data into a form from which an outside source could never converge it back and associate with the customer, employee, or any person. This is what pseudonymization does. Instead of revealing personally identifying information, it substitutes real identifiers with artificial identifiers. Although this is often kept separate so the original data can be reconstituted, pseudonymization makes it more difficult to link personal information with an individual.

Following is an example of Pseudonymization

Before Pseudonymization:

After Pseudonymization:

In this example, personally identifiable information (names and specific cities) has been replaced with pseudonyms (IDs) and more general location data. This makes it harder to identify individuals while still preserving useful information for analysis.

K-Anonymity and L-Diversity

K-anonymity is when, for every possible value of an identifier attribute, there are k other tuples in the table that have the same values. Simply put, L-diversity makes sure there are at least L distinct sensitive attribute values in each group of records that should contain similar data. The redactable signature can give much stronger privacy guarantees than mere anonymization.

Original dataset:

After applying 2-anonymity:

In this example, we’ve achieved 2-anonymity by generalizing age into ranges and ZIP codes by removing the last digit.

Privacy-Preserving Record Linkage (PPRL)

PPRL, unlike traditional cross-linking methods, is when separate organizations can connect their datasets based on a shared human or entity but do not have to reveal the real identifying details. For example, someone conducting medical research might want to combine data from hospitals without compromising patient confidentiality. Commonly, cryptographic techniques are employed to match records between datasets without revealing the actual data.

Synthetic Data Generation

Resampling methods are innovative techniques that generate artificial data that acts like the original table. More advanced techniques, such as Generative Adversarial Networks (GANs), can produce synthetic datasets that look and feel just like real data. This, in turn, helps AI models learn from data that is indistinguishable from real-world information and does not contain any proprietary personal identifying details. It has become a part of multiple industries, such as Healthcare, where AI-trained models are used for rare disease diagnosis. It is also used in the finance industry for fraud detection and risk modeling.

Best Practices for AI Data Security

Implementing privacy control is one of the steps in ensuring AI data security, but it is not the only step. Companies need to implement data protection strategies to protect the AI system and the data they use.

#1. Establishing a Security Framework

An organization must implement well-defined security policies that help security engineers implement access control and identity management (IAM). For the storage and transfer of data, proper authentication mechanisms should be set up. Organizations should conduct regular assessments and develop recovery plans in case of AI-related disasters.

#2. Continuous Monitoring and Updates

AI systems should be monitored regularly to find any risks and upgraded regularly. Regular audits can help organizations highlight any potential threats before they can be exploited by attackers.

#3. Employee Training and Awareness

The security and development team manages the security of AI data. Organizations should educate their employees on how to protect their data and implement AI best practices. Regular training sessions and workshops can help staff stay updated on the latest security threats and mitigation techniques specific to AI systems.

#4. Collaboration and Information Sharing

Organizations should work with educational institutes and research centers that focus on AI security and might have more vision for unique threats. Working with regulatory bodies helps organizations remain compliant and influence policy development.

Regulatory and Ethical Considerations

With the development of AI technology, it is important for regulatory bodies from all around the world to take some actions that will ensure individual privacy and help in stopping the abuse of AI. Some of the most commonly known regulations are:

General Data Protection Regulation (GDPR)

GDPR needs organizations to follow strict guidelines which include the collection, processing, and storing of personal data. It also states that the data stored within AI should have management constraints. GDPR emphasizes data minimization and the purpose of the restriction, and it grants the right to be forgotten.

Businesses that are using AI for their operations should follow these standards and should obtain legal permission for data processing and need to state the clear use of AI in their operations, which can affect its customers directly.

Californian Consumer Privacy Act(CCPA)

CCPA allows very limited rights to organizations. CCPA has the right to know what data is being collected and how it’s being used. It even allows residents of the USA to make a choice if their data can be sold or not.

Importance of Ethical AI Practices

It is important for the organizations to be ethical. These ethics make sure that AI systems are always in check not only for the sake of public trust but also to do some good in society with the help of these systems. The three principles that should be followed are:

1. To avoid discrimination against races, genders, and ages, it is important to check any issues in training data. Regular audits of AI outputs should be done to make sure they are not unethical.
2. It is important for AI systems to be transparent on how they make some decisions, especially for organizations that deal with healthcare data or criminal justice.
3. It should be clearly stated who or what will be accountable if any unethical action or decision is taken by an AI.

SentinelOne for AI Data Security

SentinelOne products are one of the best tools for protecting AI systems and their data. The platform provides behavioral analysis and machine learning to create multi-layer security that can protect organizations from all kinds of threats.

Key Products and Features

• Behavioral AI: SentinelOne uses machine learning models to detect any behavior that could indicate a cyber attack, including potential threats to AI systems.
• Automated response: The platform could automatically respond to threats, avoiding risks to AI data and the infrastructure of an organization.
• Cloud workload protection: This feature helps secure AI systems and data present in cloud environments.

Conclusion

AI has become a part of our lives, and it will continue to grow with time. Thus, it is very important to protect AI and the data used for AI to be protected from cyber threats. This should be done while keeping in mind the safety of customers and organizations. This makes sure that AI will not be threatened and will not threaten the lives of consumers.

Organizations use AI to increase the efficiency of their daily operations. It is important for organizations to learn about the security of AI models that they might be using or they might have developed. They will be able to do that if organizations understand the threats will affect the AI used by them. This blog will help organizations secure the AI models and find different ways to keep the AI data secure. Best practices should be implemented while applying AI data security, and organizations can make use of SentinelOne for better security.

SentinelOne is an intelligent platform that makes use of behavioral analysis to find out about any potential threat to AI data security. It provides different features such as automated response, cloud workload protection, and data protection capabilities to help organizations make their business secure.

FAQs

1. How is AI used in data security?

AI helps with real-time threat detection and analysis of enormous amounts of data. Responses to attacks can be automated with AI, which helps in limiting the damage to resources. AI also helps in detecting suspicious behavior which can lead to security breaches.

2. Is AI good for cyber security?

AI is super helpful for cybersecurity. When it comes to the timely work of identifying and responding to rapidly evolving cyber threats, AI operates faster than humans. AI systems learn quickly and can evolve alongside new threats.

3. What are AI and IoT security?

Artificial Intelligence can be used by the Internet of Things (IoT) ecosystem as well for security. AI helps in tracking the unusual behavior of IoT devices, which helps the security team learn about network traffic for threat detection and helps resolve cybersecurity risks by sorting the security vulnerabilities.