Generative AI Security (GenAI Security) encompasses all the measures, technologies, policies, and security controls that protect organizations from risks associated with the use of Generative AI.
In this short post, we will cover the basics of GenAI Security.
But first, what is Generative AI?
Generative AI (GenAI) simply refers to any algorithm, usually a deep neural network, that can generate new content. It can create anything, from text to images, based on its training data. GenAI models, including GPT and DALL-E, are known for their creativity and efficiency. However, they also raise security concerns.
Now, let's talk about GenAI Security!
GenAI Security encompasses everything you need to prevent your organization from being harmed by GenAI.
What are the risks associated with GenAI?
GenAI Security risks can be divided into two main areas:
- Usage: Protecting your organization from the risks of employees in any department from using GenAI tools such as ChatGPT, Jasper, or AI code assistants.
- Integration: Protecting your organization from the risks posed by homegrown applications that leverage 1st or 3rd party large language models (LLMs
‘Usage’ of GenAI apps by employees in the organization has to do with their tasks and workflows. But it comes with several associated risks, including:
- Shadow AI: Adoption, use, or integration of various GenAI tools without the organization’s approval or oversight. When security teams lack visibility, it opens the door for data exfiltration and the exposure of critical company assets and IP.
- Sensitive data disclosure or leakage through user prompts: Sensitive data from the organization can be streamed to these GenAI tools. When that happens, there's a significant probability that this data will be used for future training of the LLMs. Plus, it could potentially be generated by these tools on external endpoints.
When it comes to the integration of GenAI capabilities and features into homegrown applications, you’ll want to protect your organization from numerous risks, including:
- Prompt Injection: When attackers use carefully crafted inputs to manipulate a large language model (LLM) into behaving in an undesired manner. This manipulation is often referred to as "jailbreaking." It tricks the LLM into executing the attacker's intentions while ignoring its developer’s design. A malicious actor could craft a prompt, not necessarily too sophisticated, and expose sensitive data. But it can go as far as causing denial of service attacks (DoS), RCE, or SQL injections, with the associated legal and financial implications.
- Toxic or harmful content: When your users are exposed to inappropriate, toxic, or off-brand content generated by LLMs, it can lead to reputational or legal damage.
Drilling down on the probability of GenAI risks
We can agree that there's a wide array of new risks brought by GenAI, but what is the likelihood of these risks actually happening?
In the case of internal ’Usage’, GenAI is already widespread in almost any organization. From what we’ve seen in companies that have deployed Prompt Security, at least 50 different GenAI tools are being used every week in the average organization.
In the case of the ‘Integration’ of GenAI capabilities for homegrown apps, this is accelerating exponentially as organizations race to adopt AI and launch innovative new products.
Bottom line: this new attack surface is significant, highly probable, and ever-growing.
GenAI security best practices
Generative AI models are becoming a common part of business operations. So it’s important to implement effective AI security practices to safeguard your organization.
Here are some practical strategies to help you manage the risks associated with GenAI.
1. Create Clear Policies for GenAI Use
Start by establishing clear guidelines on how GenAI tools should be used within your organization. These policies should outline:
- Which tools are approved
- How data should be handled
- Any restrictions on sharing sensitive information
Providing training on these policies helps employees understand how to use AI responsibly and safely. With clear expectations, you can reduce the risk of data exposure and keep AI usage under control.
2. Keep an Eye on Shadow AI Usage
Shadow AI can be a real challenge when employees start using unapproved AI tools without IT’s knowledge. To address this, make it a priority to monitor the digital environment for any unauthorized AI application.
Setting up monitoring systems to detect unexpected software use can help you stay on top of shadow AI. Auditing devices and networks also helps catch unapproved installations early on. This way, you can address them before they become a bigger problem.
3. Regularly Evaluate Your GenAI Security Measures
It’s important to regularly check how well your AI system is protected. This can include:
- Running vulnerability scans
- Conducting red team exercises
- Performing security audits
Keeping up with regular assessments helps identify weak spots before they turn into actual issues. Being proactive with your security checks ensures that your AI implementations stay secure as threats evolve.
4. Protect Data Privacy in AI Implementations
When deploying generative AI, data privacy should always be a priority. Minimizing the amount of personal or sensitive data processed by an AI system reduces exposure risks. Anonymization techniques, like pseudonymization, can further protect user identities, especially when handling large datasets.
Access to AI-generated data should be restricted to authorized personnel to prevent data misuse. Maintaining strong data privacy practices maintains compliance with regulations and builds trust within your organization.
5. Have an Incident Response Plan Ready
Despite your best efforts, incidents can still happen, so it’s important to be prepared. Develop a response plan that outlines:
- How to detect unusual AI behavior
- Isolate affected systems
- Investigate what went wrong.
Knowing the steps to take ahead of time reduces disruption and allows your team to respond quickly when an issue arises. Prioritizing data security throughout the process will help protect sensitive information during incident handling.
What should I do to protect my organization from GenAI risks?
Well, first and foremost, get familiar with this new attack vector. GenAI unlocks almost endless possibilities to innovate in any organization and make employee’s lives better, but it’s important to stay on top of the ever-growing number of risks and be informed and prepared accordingly.
There are numerous resources available for both AI and Security professionals. You can start by reviewing the OWASP Top 10 for LLM Applications.
If you want to explore how you can navigate the GenAI risks in your organization and protect against them, book a demo with our experts.
