Top Strategies for Securing Data with AI Technology

In the past few years, large enterprises have faced ongoing data breaches leading to customer data being leaked and high penalties for lax security measures. The reality is that the size of the company does not matter, as cyberattacks can happen to every company.

The key to securing data from breaches and privacy violations is to assess common risks and incorporate data security strategies. In this guide, we will review strategies to secure data with AI usage.

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‍Importance of AI Data in Data Protection

The number of data breach victims in just the second quarter of 2024 was 1 billion representing a 1,170% increase over Q2 2023, according to Fast Company

With the proliferation of tools to optimize business processes, be it open-source technology or regular SaaS software solutions, or AI tools, businesses are constantly exposing themselves to data privacy risks.

AI itself is crucial for many industries. It is used not only to optimize business processes but also to enhance data protection. For instance, AI security tools have algorithms that instantly detect fraud or abnormal activities. Machine learning algorithms are able to forecast potential trends and risks.

The role of AI in data protection is critical, as it helps to:

• Analyze network data in real time and identify potential threats in a matter of seconds.
• Predict possible vulnerabilities using historical data to proactively protect the organization from possible risks.
• Monitor usage behavior to secure data against unauthorized access.

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Common Risks with AI and Modern-day Technology

Even though AI security solutions help prevent risks, generative AI, such as AI assistants and open AI tools, pose certain risks.

For instance, common risks of using generative AI tools include:

• AI model manipulation during which the training data is not representative of the results you expect. The model learns from the incorrect dataset and produces biased outcomes.
• Data breaches. Generative AI systems learn from vast amounts of data, including interaction with different users. Thus, the inputted data is stored in the system in most cases, making it prone to data breaches.
• Data loss is the most common risk of AI, as it includes the loss of confidentiality of sensitive or personally identifiable data. Traditional DLP solutions fail in securing interactions with AI agents.

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Top Strategies for Securing Data with AI

Let’s review strategies to secure data with AI.

Set Ethical AI Usage Guidelines and Policies

One of the most essential things every company using AI tools must take care of is establishing clear guidelines on how employees interact with AI. Such policies must address a variety of data security concerns and showcase clear standards and regulations on using particular tools. You can also add a layer of security with DLP 2.0 solutions.

It is vital to consider tools that can and cannot be used by employees depending on the transparency levels they offer regarding further data use.

In a nutshell, the organization must address:

• What AI tools can and cannot be used to increase employee productivity levels?
• Risk management strategy that addresses issues such as data privacy, bias, and misuse.
• The compliance aspect ensures the guidelines allow the organization to use AI while staying compliant with data privacy laws and regulations such as HIPAA and GDPR.
• How and when employees can use the AI tools - use cases.
• Technology that will help to ensure regulatory compliance and data protection.

You can secure your data when using AI systems by using dedicated AI security solutions, such as Wald. This tool provides necessary security measures to ensure regulatory compliance with a variety of data protection policies, including GDPR, GLBA, and CCPA. Also, it is worth mentioning that you can set your own custom data retention policy with this tool.

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Do not use Confidential Data

It is best to avoid inputting confidential data such as trade secrets and other forms of sensitive data into AI systems. By avoiding the usage of confidential data, organizations eliminate the risk of such data becoming part of the AI system training dataset and being at risk of data leaks.

The main benefits of this strategy include risk minimization, regulatory compliance, and improved customer trust.

To safeguard confidential data, data anonymization can be used to safeguard your data from generative AI systems. An example of such a tool is Wald, which offers complete security when using AI assistants such as ChatGPT by intelligent data substitutions and enterprise/employee identity anonymization.

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Incorporate Auditing Measures

To ensure that data entry guidelines are being followed properly, you need to conduct regular audits. For instance, you can establish role-based permissions to ensure that only authorized employees can access certain types of data.

Audits help to identify any suspicious or unauthorized activities. Thus, you will be able to protect data and ensure compliance with privacy regulation policies regularly.

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Use Encryption

Another way to protect the organization’s sensitive data from a data breach or leak is to incorporate encryption methods to allow for the protection of conversations using customer-supplied encryption keys so no one outside the customer organization can ever have access to confidential data.

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Protect Your Data with Wald AI

Wald is a robust software solution that will help you protect your data by offering features such as intelligent data substitutions and enterprise identity anonymization.

Also, Wald offers features such as sensitive PII and trade secrets detection, allowing admins to set custom data retention policies and ensure compliance with CCPA, GLBA, GDPR, and other security regulations.

Contact us to find out more about how Wald can help your organization leverage the power of AI while ensuring high levels of data security.

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