Securing AI Systems: Ultimate Practices for Protecting Artificial Intelligence Technology

Meta Description: While AI systems can be extremely beneficial for businesses, they can fall victim to cyberattacks. Let’s discuss how to secure AI technology properly.

AI (Artificial Intelligence) technology is becoming increasingly popular because of the benefits it brings to the table, especially for businesses. According to recent statistics, 80% of tech and business leaders believe that AI can improve productivity and create new jobs.

Additionally, AI can also play a crucial role in addressing customer and employee churn rates. Statistics show that 64% of business owners say that AI can help them improve customer relationships, and it has the potential to help 52% of employees simplify their job responsibilities.

However, the growing reliance on AI systems has also opened up new doors for hackers and other malicious online actors. They now use the same AI technology to create highly sophisticated cyberattacks to poison/corrupt the data these systems use.

Therefore, it’s critically important to implement robust security measures to address vulnerabilities in AI systems. Let’s discuss the best practices that you can use to protect artificial intelligence technology.

What is AI Security?

Definition for Google Snippet: In simple words, AI security is the process of taking protective measures to secure an AI system by making it resilient to cyberattacks. It helps you improve the security posture of a system and ensure its confidentiality, reliability, integrity, and availability.

The following are the three different levels that you need to keep in mind while implementing AI security.

Software Level

At the software level, it’s essential to conduct security audits regularly to analyze the code for vulnerabilities. It’ll help you find and address programming weaknesses that cybercriminals can exploit to make sure that the system itself is secure and resilient.

Distributed Level

If your AI system is composed of multiple devices/components working together, you need to secure all of them. It includes verifying that all components are operating as intended and that the results they produce are accurate and reliable.

Learning Level

The learning level of AI security focuses on protecting the databases used for training AI models. It’s crucial to control the type of data that is fed into the system carefully. It’ll help you ensure its integrity and prevent the introduction of malicious or misleading information.

Additionally, it’s also important to monitor the performance of the AI model to detect any unusual behavior or potential threats.

Why is Securing AI Systems Important?

Almost all AI systems possess large amounts of data that includes sensitive and personal information, making them attractive targets for hackers. If these systems aren’t secured properly, attackers can gain unauthorized access to this information, causing privacy breaches and identity theft.

Not only will it damage your organization’s reputation, but it can also lead to financial loss and legal issues.

The main challenge in securing AI systems is that traditional security measures used for other types of software may not be enough. For example, you can use two-factor authentication and strong password techniques to protect cloud service accounts.

However, the same practices won’t be enough against AI-specific attack types, such as adversarial attacks. This means that securing AI systems requires tailored security measures that address the unique vulnerabilities and risks associated with AI technologies.

Most Common Cyber Threats Facing AI Technology

Here’s a list of the most common cyber threats that target AI systems.

  • Data Breaches: In this type of cyberattack, cybercriminals gain unauthorized access to AI systems, which allows them to steal sensitive information. It includes confidential business data, financial records, and personal information, leading to reputational damage, financial fraud, and identity theft.
  • Adversarial Attacks: Adversarial attacks involve manipulating AI systems by introducing intentionally crafted false data or images. The goal is to deceive the system and make it bypass security measures and/or produce incorrect outputs. It can lead to unauthorized access or compromised decision-making.
  • Malware and Ransomware Attacks: Malicious software, such as ransomware or viruses, can damage AI systems badly. Ransomware holds data hostage and demands a ransom for its release, while malware can disrupt system operations and/or steal valuable information.
  • Insider Threats: Insider threats occur when contractors or employees with authorized access to AI systems misuse their privileges. This can be intentional, such as leaking or stealing sensitive data, or unintentional, like misconfigurations that open security vulnerabilities or accidental data exposure.
  • Social Engineering Attacks: Social engineering attacks are about deceiving employees of an organization so that they grant unauthorized access or reveal sensitive information unintentionally. Cybercriminals use different tactics like impersonating trusted entities or using phishing emails and phone calls to gain access to AI systems and compromise their security.
  • DoS (Denial-of-service) Attacks: In a DoS attack, the AI system is flooded with an overwhelming number of requests (or traffic), which causes it to crash or become unresponsive. It disrupts normal operations and leads to reputational damage, financial losses, and service interruptions.
  • Physical Attacks: Physical attacks, as the name implies, involve accessing AI systems physically and tampering with hardware, such as a key fob access system, CCTV cameras, or software components. It includes installing malware directly onto the system, performing unauthorized modifications, or stealing information, which results in system malfunction, data breaches, or compromised security.
  • IoT Security Threats: AI systems connected to the IoT (Internet of Things) can be vulnerable to security threats from other connected devices. For example, an insecure IoT device can serve as a gateway for hackers to gain access to an AI system. It can lead to unauthorized control, data theft, or damage to functionality.

Best Practices to Protect Artificial Intelligence Technology

As mentioned already, securing AI systems is different from securing a traditional software solution. That’s because these systems are complex and use very big amounts of data.

Additionally, hackers also use AI and ML (Machine Learning) algorithms to target AI systems, which makes securing them even harder.

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If you’re running a business that uses AI systems, you can use the following best practices to improve your organization’s security posture.

Follow Cybersecurity Standards

Following cybersecurity standards not only allows you to ensure the protection of sensitive data but also helps you maintain regulatory compliance. Regulations such as CCPA (California Consumer Privacy Act) and GDPR (General Data Protection Regulation) establish guidelines for collecting, storing, sharing, and processing data.

To comply with these standards/regulations, you need to pseudonymize/anonymize data used in your AI system and use transparent data processing practices. Failure to comply with these rules can result in large legal fines and reputational loss.

Additionally, industries like finance and healthcare have specific regulations such as PCI DSS (Payment Card Industry Data Security Standard) and HIPAA (Health Insurance Portability and Accountability Act), to follow. These standards outline data security standards that related organizations must follow to maintain data security and meet legal requirements.

Collect Only What’s Needed

To ensure the security of an AI system, it’s essential to collect only the data that is necessary in order to reduce the potential risks associated with sensitive data. It means that you should avoid the collection of unnecessary information to minimize the chances of data loss or breaches.

Even when data collection is warranted, it’s essential to gather the absolute minimum amount required for the intended purpose. While it may appear advantageous to collect and hold excessive data, it increases the vulnerability of the system to cybersecurity incidents as it expands your attack surface.

So, you should strictly adhere to the “take only what you need” approach to prioritizing data security in your AI system(s).

Perform Data Normalization

Data normalization is the process of assessing and organizing data and removing redundancies. You can start by evaluating the sensitivity of each dataset and disposing of any unnecessary information.

Not only will it help you free up valuable storage space, but it’ll also reduce the risk of data breaches. That’s because it’ll enable clear visibility of where data is stored and eliminate unknown endpoints for unauthorized access to sensitive information.

Apply Data Classification

Data classification involves identifying and labeling sensitive data and applying strong protection measures like encryption and access controls (discussed below). It helps you ensure compliance with regulations and enables effective response strategies during security incidents.

Additionally, it also minimizes the risk of data breaches, improves AI model performance, and maintains data integrity and confidentiality.

Use Tokenization

Tokenization is an excellent technique to ensure AI data security. It replaces sensitive data with meaningless tokens, keeping it safe from unauthorized access. Even if a breach occurs, the original data remains secure since it’s stored separately as tokens.

Another benefit of tokenization is that it helps you with regulatory compliance and reduces risks during data transfer in AI systems. It also allows you to perform secure data analysis without altering the original format, making it useful for training AI models on sensitive datasets.

Utilize Data Masking

Data masking is a security technique that replaces sensitive information with artificial or scrambled data while keeping the original structure intact. It allows AI systems to work with datasets without exposing sensitive data to ensure privacy and enable secure testing.

Just like tokenization, it helps you comply with privacy laws and reduces the impact of data breaches. Plus, it also allows you to share data safely and collaborate for analysis or train an AI model.

Important Note: While both data masking and tokenization seem similar, they’re different. Data masking is mainly used for data in transit and tokenization is used to secure data that’s at rest.

Encrypt All Data

Encrypting your stored data, whether it’s at rest or in transit, is a crucial step to enhance your AI system’s security. While encryption might not guarantee absolute protection, it’s a cost-effective strategy to safeguard your data in case of a breach.

Therefore, if you handle sensitive information, you should enable encryption by default.

One of the biggest concerns about encryption is that it slows down the system’s performance. However, it’s becoming less relevant, as many modern applications and services now include built-in encryption features.

For example, Microsoft Azure SQL Database offers encryption options, making performance concerns less of an issue.

Use-Data Level Access Control

Data-level access control uses detailed policies to determine who can access specific data and what actions they can perform using an AI system. The primary purpose of using this technique is to minimize the risk of unauthorized access and prevent data misuse.

Along with helping you comply with data protection laws, data-level access control can also allow you to identify possible breaches by monitoring data access and detecting unusual patterns.

Utilize the Power of DLP (Data Loss Prevention)

DLP techniques aim to prevent the unauthorized disclosure or loss of sensitive information that an AI system uses. The main purpose of these techniques is to help you monitor, control, and protect data movement across storage systems, endpoints, and networks.

While DLP can help prevent accidental data leaks by employees, its effectiveness in combating malicious activities is still a matter of debate. Although not explicitly mandated, it’s often considered an implied control for regulatory compliance, such as GDPR and PCI DSS.

It’s important to note that implementing DLP can be complex and resource-intensive, as it requires tailored solutions to address specific threats. However, it’s one of the best ways to protect data in transit and block access to sensitive information in a system.

Protect Could Services

It’s important to remember that even if your cloud services (that you use in your AI system) are managed by others, you still need to prioritize their security. Don’t assume that someone else will take care of it for you.

You need to familiarize yourself with the recommended best practices for protecting these systems, as suggested by other users of similar services. These precautions include enabling authentication for file storage systems and strengthening server ports to allow only necessary access.

It’s also equally important to restrict service access to authorized IP addresses to prevent data breaches. You can use even a cheaper version of a VPN service, for the same purpose, as it’ll help you create a VPN tunnel to prevent unauthorized access. Make sure to match your VPN with your device’s Operating System. For users with Windows, VPN should be precisely fitted to have all the needed settings, just like with Mac, it should work well with MacOS.

Educate Your Team

Educating your team about AI system security is crucial to protecting against internal and external threats. All employees should receive training on the specific security considerations and best practices related to AI systems.

It includes understanding the unique risks and vulnerabilities associated with AI technology, such as the potential for adversarial attacks or data manipulation.

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This way, you’ll be able to empower your team members to recognize and address potential security risks, ensuring the integrity, confidentiality, and availability of AI systems.

Stay Up to Date

As technology evolves, new vulnerabilities and threats emerge, which makes it critical to stay informed. To achieve that, you can start attending conferences and workshops and reading publications from industry experts.

It’ll help you address emerging cyber security risks to improve the security posture of your AI system proactively.

Have a Crisis Plan in Place

Creating a crisis plan is essential in case of a cyberattack on your AI system. This plan should outline the necessary steps and procedures that your team will need to follow when a security incident occurs. Each team member should know their role and be prepared to act quickly and efficiently.

Assigning specific roles and responsibilities ensures that the right people are handling the appropriate tasks, minimizing confusion and delays during an incident.

For example, there should be someone responsible for communication with stakeholders, someone for technical response and investigation, and someone for coordinating with law enforcement if necessary.

Keep in mind that conducting practice drills regularly is essential to test the effectiveness of the crisis plan. These drills simulate potential cyberattack scenarios, which will help your team practice their responses and identify any weaknesses or areas for improvement.

This proactive approach will help you increase the chances of minimizing damages and restoring normal operations after a security incident quickly.

Important Note: You should implement the following practices alongside your existing security strategy that you use to protect traditional software solutions. It includes using anti-malware/virus solutions and firewalls, following best password practices, monitoring user activity, etcetera.

Final Words

As more industries adopt AI systems in their business processes, it becomes crucial to have a holistic security strategy in place. It’ll help you protect the integrity, availability, and confidentiality of your AI systems and prevent financial, reputational, and legal issues.

We hope this guide has provided you with valuable insights and practical steps to enhance the security of AI technology and use its transformative potential effectively.

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