The role of prompt intelligence in enhancing AI security

The unique nature of GenAI coupled with rushed rollouts can present security challenges for enterprises. As GenAI technology evolves, new and sophisticated security threats are emerging alongside it. Attackers are taking advantage of GenAI, which is increasing the sophistication and impact of their attacks. 

Organizations that embrace the GenAI gold rush without adequate AI security measures put themselves in a dangerous position. Robust and trustworthy AI systems are essential to protect sensitive information. 

Defining prompt intelligence for AI security 

A primary attack vector for GenAI applications is the user prompt. Prompt intelligence is a technique used for optimizing GenAI applications and fortifying their security. It enables enterprises to monitor and analyze GenAI systems usage to identify risks, develop security policies, and implement protective countermeasures. 

Prompt intelligence operates in a layer between the user-facing application and the underlying large language model (LLM). It analyzes and potentially modifies both the user prompt and model responses. 

In the context of AI security, prompt intelligence can be used to: 

• Identify attacks in the form of malicious prompts 
• Detect abnormal usage patterns 
• Collect data about unsafe and abusive prompts 

Some uses of prompt intelligence do more than just capture and analyze usage data. They also take proactive remediation steps such as rejecting prompts that violate policies before passing them to the LLM. 

Alternatively, it could modify the prompt by adding content or system messages to increase the security and safety of the input. 

After the LLM generates a response, prompt intelligence may perform post-processing if it determines the response violates security policy. This post-processing may remove sensitive information, adjust the tone, or replace it with a generic rejection message. 

Prompt intelligence is a critical component of the broader picture of AI security, which includes other elements such as model security, access control, and AI incident response.

It’s worth noting that prompt intelligence not only enhances security. Prompt intelligence can also generate insights to help with the following: 

• Improving models for better response relevance and accuracy 
• Capturing operational metrics, such as task performance or time savings 
• Determining ROI and performing cost analyses 
• Improving understanding of end user intent and behavior 

Prompt-based attacks and security risks in GenAI applications 

Several broad categories of attacks cann be leveled via user prompts, exposing an organization to various risks. Those categories are the following: 

• Data privacy: Because an LLM may be fine-tuned on proprietary data or a GenAI application may be enhanced with retrieval-augmented generation (RAG), the risk of inadvertently exposing sensitive data is substantial. 
• Prompt injection: A prompt injection is an attack in which a malicious user provides harmful or misleading instructions in the prompt, seeking to guide an AI system toward producing incorrect or unexpected behavior. 
• Indirect prompt injection: Indirect prompt injection occurs when an LLM processes input from attacker-controlled external sources, such as specific websites or tools. In these scenarios, the attacker can embed a concealed, harmful prompt in the external content. 
• Toxic, biased, or harmful content: An LLM can produce harmful content, causing serious damage to the organization. The consequences can range from embarrassing social media posts to negative customer experiences, and they may even have legal ramifications. 
• Denial of Wallet attacks: Denial of Wallet attacks are a close cousin to Denial of Service attacks. In this attack, abusing a GenAI application to create excessive interaction with an LLM can cause massive resource consumption. This can impact service availability for other users as well as incur high costs for the enterprise. 
• Jailbreaking: In this attack, a malicious user carefully crafts prompts to circumvent the built-in defense and system instructions of the LLM. Many of the other risks mentioned above become easier to exploit if the model is jailbroken. One of the most infamous examples of AI jailbreaking is Do Anything Now (DAN), where the model is told to play a role that doesn’t abide by the system guidelines. 
• Prompt leak: Prompt leak is a form of prompt injection that causes the model to reveal its own system instructions or internal logic. This information, beyond being sensitive itself, can be used to craft jailbreaking prompts. 
• Tool abuse: As more and more applications allow LLMs to invoke external tools, attackers can attempt to use malicious prompts to cause unintended consequences through those automatically called tools. 

The benefits of using prompt intelligence for AI security 

Prompt intelligence can curb many of these security risks, bolstering the robustness and reliability of GenAI systems. 

Enhanced threat detection 

Prompt intelligence can detect AI adversarial attacks through prompt analysis. It can also block requests from untrusted sources or sources that demonstrate suspicious usage patterns. This proactive approach allows for quicker identification and mitigation of potential security breaches by rejecting such prompts before they reach the LLM. 

Increased trust and compliance 

Prompt intelligence yields data-driven insights into AI system usage. This data can be validated against an organization’s usage policies, thereby maintaining compliance with regulatory standards and fostering user confidence in the system’s integrity. 

When prompt intelligence is used as a defense layer, rejecting policy-violating prompts before they reach the model reduces the likelihood of a model behaving in unpredictable ways. Similarly, when prompt intelligence is used in post-processing to vet responses for non-compliance before they make their way to the end user, an enterprise has another layer of protection against compliance violations. 

Cost efficiency 

Prompt intelligence can prevent Denial of Wallet attacks or non-malicious prompts that are simply outside the scope of the model, thereby reducing the usage of precious resources. 

Best practices for incorporating prompt intelligence into security measures 

The value of prompt intelligence for AI security is clear. Organizations that want to leverage prompt intelligence techniques should consider several AI security best practices. 

Set up robust monitoring systems 

Prompt intelligence starts by monitoring the input prompts and the model responses. After analyzing each prompt carefully, one of the following three paths may be taken: 

1. Reject the prompt with an appropriate error message. 
2. Pass the prompt as is to the model. 
3. Modify the prompt before passing to the model. 

 Both the prompt and the path taken should be captured for audit and analysis. A similar process takes place for the response generated by the model. Prompt intelligence analyzes the response before sending it to the user (either as is or modified). Again, the response and any post-processing actions taken should be captured for audit and analysis. 

Implement policy controls 

Prompt intelligence must be built on a foundation of governance policies that define what would cause a prompt or response to be rejected. These policies should augment the built-in guardrails of the LLM. Expect some overlap between prompt intelligence policies and LLM policies. Prompt intelligence can short-circuit prompts, rejecting prompts that would have otherwise been rejected by the LLM regardless. 

This same need for establishing and enforcing policies also applies to the responses generated by the LLM. 

Regularly update AI models and policies 

The insights generated from prompt intelligence can be used for fine-tuning the underlying LLM and updating usage and security policies. This creates a virtuous cycle of continuous learning, as prompt intelligence improves the overall security and effectiveness of the AI system. 

Leverage advanced tools 

Building sophisticated prompt intelligence from scratch is incredibly complex. It’s also a moving target as attackers learn to circumvent existing state-of-the-art prompt intelligence security measures. Look for platforms that offer built-in policy controls, monitoring, and prompt intelligence. These platforms will be updated to respond to new threats so that you can focus on your core business. 

Enhance AI security with prompt intelligence insights 

As enterprises rapidly integrate GenAI into their business, the significant productivity gains are accompanied by security challenges. Sophisticated attackers target GenAI applications because this is presently a novel and weakly defended threat vector. 

Prompt intelligence is a critical defense strategy that fortifies GenAI systems by monitoring, analyzing, and potentially modifying user prompts and model responses to enhance security. It can identify various security risks, such as data privacy breaches, prompt injections, and Denial of Wallet attacks by rejecting or modifying policy-violating prompts and responses. 

The first step for organizations to protect their GenAI systems is to study the terrain, learning about the risks and how to mitigate them. As your enterprise continues on its GenAI journey, learn more by reading about how to reduce generative risks and improve compliance for your GenAI landscape.