Handling Sensitive Information with AI: Best Practices

With the rapid growth of AI and its use cases, handling sensitive information is becoming one of the top priorities for businesses and individuals.

AI presents companies with a variety of benefits but also certain risks. In this guide, we will review everything regarding handling sensitive information while using AI, including its importance and best industry practices.

Importance of Handling Sensitive Information with AI

Many companies nowadays know the risks of using sensitive data in tools such as LLMs (large language models.

Handling sensitive information while using AI will allow your organization to protect data from unauthorized access, and ensure that the usage of AI is compliant with regulations, and enhance trust.

Common Risks of Using AI

Let’s dive deeper into potential risks AI exposes organizations and individuals to.

AI Poisoning Attacks

One of the most common risks of using AI is poisoning attacks. There are a few main types of poisoning attacks: data poisoning and model poisoning.

A data poisoning attack is when a party injects malicious or corrupted data into training data sets of the AI tools. This can cause the AI model to produce false and biased results.

In model poisoning, the attacker directly tampers with the AI model. Such interference can happen either during or after model training. It can involve altering the model’s parameters or algorithms to produce specific, malicious outcomes when it processes data, even if the data itself is clean.

The main difference between data poisoning and model poisoning is that data poisoning affects the input AI learns from, while model poisoning affects the internal processing.

Adversarial Attacks

Adversarial attacks aim to cause AI systems to make mistakes through manipulations of input data. Such attacks target AI algorithms’ vulnerabilities, aiming to deceive AI tools. It is important to be aware of adversarial attacks, as these impact the level of accuracy with which the tool provides information.

Privacy Violations

Some AI systems need to be more transparent regarding where and for how long the data inputted is being stored. Thus, these tools expose users to certain types of privacy vulnerabilities, such as revealing PII (personally identifiable information) or other sensitive data.

Clearview ai is a famous case of privacy violation in Canada. The tool collected photographs of Canadian adults and children for mass surveillance to train the model for better facial recognition without the actual consent of the users.

Best Practices to Handle Sensitive Information with AI

Incorporate the Right Security Measures and Technology

To avoid too many restrictions and leverage the full power of AI while protecting sensitive data, consider incorporating the right security solutions. For instance, Wald is an excellent tool connecting enterprises with AI assistants while managing data protection and regulatory maintenance.

With Wald AI, employees can ask queries and generate code and content without worrying about compromising sensitive data. Also, the platform offers features such as intelligent data substitutions and anonymization of personal and enterprise identity for enhanced security.

Restrict the Sharing of Data

When using LLM/AI Assistants within the organization, you can restrict data sharing with LLM vendors and key stakeholders.

For instance, the AI chatbot answering employees’ questions regarding future possibilities and expectations needs training data from other employees. However, if not appropriately trained, the model can expose sensitive information such as salary and benefits to anyone within the organization who asks such questions. To prevent this from happening, you must incorporate appropriate measures to restrict data sharing.

One way to restrict data sharing is to add a layer between the user and the tool (LLM). The layer will contain filters (restrictions) so the model understands what information users can see and what information should be kept private.

Establish Clear Policies Regarding AI Usage within the Organization

Finally, to ensure the safe use of AI and proper handling of sensitive information, you can set clear policies regarding its use within the organization.

AI policy addresses essential security, enablement, and oversight concerns regarding AI while ensuring organization-wide compliance with standards and regulations.

In order to create an efficient AI policy, make sure to:

• Contact key stakeholders to ensure that the framework and policy on using AI efficiently is followed by all teams within the organization.

• Audit current processes to define security risks.

• Consider ethical issues and data privacy risks.

• Make sure to highlight the main legal issues that can arise from using AI and include points in the policy on how to navigate them.

When you assess all the areas mentioned above and develop a clear policy on using AI, make sure to train employees and agree on the AI adoption process. Finally, regular audits should be arranged to check if the employees are following the policy.

Secure Your Data with Wald AI

Wald is a robust tool that allows your teams to leverage the power of AI while ensuring high levels of data privacy and protection.

Wald comes in handy with features such as intelligent data substitutions, availability to set custom data retention policies, and anonymization of personal and enterprise identity.

Contact us to learn more about how Wald can help your organization use AI while complying with security standards and protecting sensitive data.