Generative AI Security Risks and How to Overcome Them

The Unintended Consequences of Generative AI’s Rapid Growth

As Generative AI models become increasingly more sophisticated, they are capable of generating content that is indistinguishable from content created by humans. What’s worse is that AI doesn’t yet have the full ability to distinguish between content that is truthful, and content that is not. This raises serious concerns about the spread of misinformation and disinformation, as AI-generated content can be designed to deceive even the most skeptical of audiences. Additionally, the increased reliance on AI-generated content may lead to a decline in human creativity and critical thinking, as well as the erosion of traditional skills and industries

Top Concerns Relating To Generative AI

Below are the most notable concerns that experts have when it comes to Generative AI security:

1. Model Safety

As AI models continue to become more popular, concerns about their safety are growing. AI Model Safety addresses concerns such as bias, transparency and accountability. Solutions include maintaining an inventory of all AI models through AI Model Discovery and identifying potential risks associated with AI model usage. For example, following an AI Model Risk Assessment security teams will need to ensure that AI models are fair, follow instructions, and are not subject to hallucinations or bias. Additionally, AI Model Security establishes safeguards to prevent the stealing or tampering of models. AI Model Entitlements are used to conduct a thorough review of all model access privileges to ensure responsible usage.

2. Data Usage

Generative AI can be trained on enterprise data, either directly or augmented with external sources. To ensure data security and regulatory compliance, it’s essential to gain a clear understanding of the data being consumed by the AI model. This includes conducting a thorough inventory of all stored and managed data, classifying data types, and identifying who has access to sensitive data. Additionally, organizations must be aware of any metadata that relates to data consent, retention, and location, as well as maintain an audit trail of data being used by AI models. Additionally, it’s crucial to be aware of the risks of automated social engineering attacks, which can compromise sensitive data or encourage users to engage in risky behaviors.

3. Data Overflow

When using Generative AI services, users can enter any type of data, including sensitive information, into text prompts. This means that individuals may inadvertently or intentionally share confidential information, such as intellectual property or proprietary information, through these services. For example, code-generating services like GitHub Copilot, designed to assist developers in writing code, may receive intellectual property or API keys that grant special access to customer information.

4. Data Storage

As generative AI models continue to improve with greater amounts of data, it is essential to store this data in a secure and reliable location. While data can be stored with a third-party service provider, this also poses risks of data misuse and leakage. To mitigate these risks and prevent breaches, organizations must use robust encryption methods to protect data, as well as implementing strict access controls to ensure that only authorized individuals can access it.

5. Synthetic Data

Generative AI may create synthetic data, that resembles real data. This can lead to concerns about individuals being able to identify the source of the data. Synthetic data may have small patterns or details that could reveal sensitive information, potentially leading to the identification of individuals. Additionally, generative models, especially those based on text or images, can unintentionally include information from the training data that was meant to be kept confidential.

6. Prompt Safety

Generative AI models require input in the form of prompts, which can be categorized into either system prompts or user prompts. System prompts are crucial in shaping the model’s behavior and should be structured with accurate, informative, and unbiased commands to steer the model towards acceptable behavior. However, even with well-designed system prompts, user prompts can pose a security threat, and therefore, generative AI systems must scan user prompts independently in real-time to detect potential security concerns. These concerns include prompt injection and jailbreak attacks, phishing attempts, model hijacking, denial of service attacks, and more.

7. AI Regulations

To effectively use Generative AI, organizations must not only comply with existing data protection laws, but also anticipate and comply with emerging AI governance laws, such as the EU AI Act, to ensure the secure handling of sensitive data. Upcoming developments include guidelines and frameworks from various countries, including the European Commission, UK, France, Spain, US, Australia, Singapore, China, India, and Vietnam. These regulations will require organizations to implement policies and processes that enable the safe use of Generative AI and ensure compliance with various obligations.

8. IP Leaks

A major concern of generative AI is IP leakage, as the ease of use of web/app-based AI tools can create a risk of exposing sensitive information. Additionally, the ease of use of these tools also poses the risk of creating another form of shadow IT. However, there are ways to mitigate these risks, and one solution is to use a Virtual Private Network (VPN) when accessing online generative AI apps. By doing so, you can gain an extra layer of security, as the VPN will mask your IP address and encrypt the data in transit, providing an added layer of protection for your sensitive information.

9. AI Misuse

The potential misuse of generative AI by malicious actors is a major concern. For example, it could be used to create convincing deepfakes that deceive individuals and manipulate public opinion. Additionally, if AI systems are not adequately secured, they may become a target for cyberattacks, which could create additional security concerns.

How Lepide Helps Deploy Generative AI Securely

AI tools like Microsoft Copilot can drastically help improve the efficiency and productivity of your employees. However, many organizations simply aren’t prepared to deploy these AI tools within their environment because of security and privacy concerns. Lepide can help ensure companies are able to deploy AI securely and smoothly with our Data Security Platform:

• Reduce threat surface: Lepide can help you understand what sensitive data AI is going have access to. It can also help to identify AI-enabled users that have excessive permissions to sensitive data based on their behavior, and also identify data that might be over-exposed through open shares or misconfigurations. This can help you reduce your overall risk or threat surface.
• Monitor AI-Created Data: Discover and classify AI generated content at the point of creation, and monitor what is happening with this data in real time so that you can detect unwanted user behavior.
• Spot threats in real time: Our intelligent behavioral analytics enables you to spot anomalies in the behavior of your AI enabled users. Trigger automated threat response off the back of real time alerts to ensure you can prevent data breaches.

To see what the Lepide Data Security Platform can do, start your free trial today.

Conclusion

While generative AI holds great promise for innovation and progress, it’s essential to acknowledge the potential security risks it poses. These risks include the unintended generation of sensitive content, data breaches, and regulatory challenges. Rather than dismissing the technology, companies should proactively develop a plan to build trust in AI and manage risks effectively.