Generative AI Security or GenAI Security encompasses all the measures, technologies, policies, and security controls to protect an organization 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 simply refers to any algorithm, usually a deep neural network, that can generate new content, from text to images, based on its training data. These AI models, including GPT and DALL-E, are known for their creativity and efficiency but also raise security concerns.
Now, let's talk about GenAI Security!
GenAI Security encompasses everything you need to implement to ensure that your organization is not harmed by GenAI, in simple terms.
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 of any department from using GenAI tools such ChatGPT or 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).
When it comes to the ‘Usage’ of GenAI apps by employees in the organization to help them with their tasks and workflows, there are several associated risks. Some of them are:
- Shadow AI: Adoption, usage, and integration of various GenAI tools without any visibility to security teams, opening the door for data exfiltration and exposing critical company assets and IP.
- Sensitive data disclosure or leakage through user prompts: Once sensitive data from the organization is being streamed to these GenAI tools, there's a significant probability that this data will be used for future training of the LLMs and potentially be generated by these tools on external endpoints.
In the case of the ‘Integration’ of GenAI capabilities and features into homegrown applications, you’d want to protect your organization from multiple risks, including:
- Prompt Injection: When attackers manipulate an LLM through carefully crafted inputs to behave outside of its desired behavior. This manipulation, often referred to as "jailbreaking," 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, 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, which 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, there are at least 50 different GenAI tools 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 part of the race of organizations to adopt AI and offer innovative new products.
Bottomline: this new attack surface is significant, highly probable, and ever-growing.
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.
