The business transformations brought about by generative AI carry risks that AI itself can help secure in a kind of flywheel of progress.
Companies that quickly embraced the open Internet more than 20 years ago were among the first to reap its benefits and become proficient in modern network security.
Today, enterprise AI follows a similar pattern. Organizations seeking advancements in this field—especially with powerful generative AI capabilities—are applying those insights to improve their security.
For those just starting this journey, here are ways to approach three of the challenges with AI: Major security threats Industry experts have identified large language models (LLM (Master of Laws)).
AI guardrails prevent rapid injections
Generative AI services are subject to malicious message attacks designed to disrupt the LLM supporting them or to gain access to their data. As the report cited above notes, “direct injections overwrite system messages, while indirect injections manipulate inputs from external sources.”
The best antidote to rapid injections is AI guardrails, built into or placed around LLMs. Like metal guardrails and concrete curbs on the road, AI guardrails keep LLM requests on track and focused on the topic.
The industry has provided and continues to work on solutions in this area. For example, NVIDIA NeMo Guardrails The software enables developers to ensure the reliability, safety, and security of generative AI services.
AI detects and protects sensitive data
The responses LLMs give to prompts can sometimes reveal sensitive information. With multi-factor authentication and other best practices, credentials are becoming increasingly complex, expanding the scope of what is considered sensitive information.
To prevent disclosures, all sensitive information must be carefully removed or hidden from AI training data. Given the size of the data sets used in training, it is difficult for humans (but easy for AI models) to ensure that a data cleansing process is effective.
An AI model trained to detect and hide sensitive information can help protect against disclosure of anything sensitive that was inadvertently left in an LLM's training data.
Wearing NVIDIA Morpheusan AI framework for building Cybersecurity Applications enable businesses to build AI models and accelerated pipelines that find and protect sensitive information on their networks. Morpheus enables AI to do what no human can do with traditional rules-based analytics: track and analyze massive data flows across an entire corporate network.
AI can help strengthen access control
Finally, hackers may attempt to use LLMs to gain access control to an organization’s assets. Therefore, companies must prevent their generative AI services from exceeding their level of authority.
The best defense against this risk is to use security best practices by design. Specifically, granting an LLM the least amount of privileges and continually evaluating those permissions, so that it can only access the tools and data it needs to perform its intended functions. This simple, standard approach is likely all that most users need in this case.
However, AI can also help provide access controls for LLMs. A standalone online model can be trained to detect privilege escalation by evaluating the results of an LLM.
Start your journey towards artificial intelligence in cybersecurity
No technique is a panacea; security still depends on evolving measures and countermeasures. Those who do it best are those who use the most modern tools and technologies.
To protect AI, organizations need to become familiar with it, and the best way to do that is by deploying it in meaningful use cases. NVIDIA and its partners can help with comprehensive solutions in AI, cybersecurity, and AI cybersecurity.
Looking ahead, AI and cybersecurity will be closely linked in a kind of virtuous circle, a vicious cycle of progress in which each improves the other. Ultimately, users will come to rely on it as another form of automation.
Learn more about NVIDIA Artificial intelligence platform for cybersecurity and how is it being put into use. And listen to talks about cybersecurity from experts in the NVIDIA AI Summit in October.
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