Do built-in LLM guardrails provide enough protection for your enterprise when using GenAI applications?
Built-in LLM guardrails are tools designed to improve the safety of AI interactions. They do this by filtering harmful or inappropriate content. This includes various services aimed at filtering, moderating, and managing content to prevent the spread of harmful or inappropriate material.
While GenAI applications provide some basic content moderation, they don’t go far enough to deliver the full protection that meets the needs of large enterprises. Enterprises need a more sophisticated, purpose-built security solution to protect their AI models and applications.
In this blog, we discuss why built-in guardrails aren’t enough to protect your GenAI applications. We also give you three compelling reasons why a purpose-built firewall provides better security to protect the enterprise.
Built-in LLM guardrails aren’t enough to protect the enterprise
There are a number of reasons that built-in guardrails don’t deliver enough protection for the enterprise. I’ve listed just a few below.
AI use cases are complex and diverse
Enterprises often have specific security or compliance requirements that are unique to their industry or business. For example, industries like healthcare and finance are more highly regulated or have more stringent data privacy requirements than other industries. To add to the complexity, what may be acceptable for an internal business process may not be appropriate for an external consumer-facing application and vice versa. Additionally, an enterprise may even want to filter inputs related to sensitive topics specific to their organization like filtering out negative press.
Built-in guardrails are designed for broad use and are not fine-tuned to an enterprise’s unique requirements. General-purpose guardrails are unable to fully address strict regulations and requirements that vary by industry and even by company. One size does not fit all.
AI attacks are evolving in real time
Adversaries are constantly looking for ways to bypass built-in guardrails, and they are getting better at attacking AI applications every day. Common attack methods include direct or indirect prompt injection to alter the behavior of the model in unintended or unexpected ways. Attackers are becoming more sophisticated at manipulating models, yet traditional guardrails are not able to adapt in real-time to stop evolving attacks.
As models evolve and new types of attacks or vulnerabilities emerge, built-in guardrails may not always keep pace with these changes, leaving gaps in protection.
Security is not the focus
The bottom line is that security is not the primary focus for those companies building LLMs and AI/ML applications. Instead their primary goal is to advance the models’s performance, scalability, and usability. The emphasis is on making the models as high-performing and versatile as possible. Though security is a consideration, it often takes a backseat to improving the model's underlying functionality. This leads to potential vulnerabilities and gaps in security, as the models are deployed with general-purpose guardrails that are not fine-tuned for specific use cases or enterprise needs.
Top 3 reasons why you need a specialized firewall for your AI applications
You can still leverage the existing guardrails built into your GenAI solution with a specialized firewall. The TrojAI firewall and built-in protections are not mutually exclusive. In fact, they are complementary. Together they offer both resilient protection and give you the flexibility to adjust to specific use case needs.
The following are the top three reasons why you should consider adopting a best-of-breed firewall to protect your AI applications.
1. Gain full control and centralized management of AI security across all LLMs
Native guardrails are insufficient. They are applied to LLM traffic indiscriminately, forcing the same set of protections onto all users regardless of use case.
The reality is that each use case requires its own set of policies and protections to be truly effective. Enterprises also need centralized management of AI security to apply protections consistently across multiple LLMs. By centralizing this control with your AppSec team, you can apply fine-grained policies and consequence management across users and teams. This ensures you close all gaps in your security program.
2. Prevent sensitive data leaks and loss
No matter how effective native guardrails are, they are still developed and enforced by the LLM provider. By the time the LLM safety controls flag your sensitive information, your PII or IP has already left your environment and reached a third party. Your organization now has little or no control over what happens with this data as it has already been sent to a third-party environment. The compliance implications are critical and could result in significant legal and financial consequences.
Preventing data loss and data leak must begin before your data leaves your environment and your control.
3. Instantly adapt to emerging threats
A purpose-built firewall can adapt to emerging threats faster than built-in guardrails. It is both more powerful and more flexible with the kinds of protections it delivers, and it can update quickly as new needs arise. Built-in guardrails offered by a third-party provider offer no such guarantee of speed and agility. Additionally, firewall policies from best-of-breed solutions are heavily parameterized and more configurable allowing more fine-grained control over content moderation.
Solutions like TrojAI adapt to emerging threats faster than native guardrails. They are more flexible and offer broader and deeper protections and can react instantly when new threats arise. LLM providers offer no guarantee of speed or response.
Better together
Built-in guardrails and best-of-breed third-party tools complement each other to provide a multi-layered approach to AI security. Though built-in guardrails offer a first line of defense, protecting against basic misuse, they are general-purpose and may not fully address specific enterprise needs, such as compliance with industry regulations, safeguarding proprietary data, or covering complex industry-specific use cases.
Third-party tools like TrojAI enhance the built-in guardrails by providing deeper, more customized protection. These tools offer advanced threat detection, real-time monitoring, and more granular control over data usage and model outputs.
By integrating an advanced tool like TrojAI with existing built-in guardrails, enterprises can address vulnerabilities more effectively, adapt to evolving risks, and ensure that their AI implementations align with organizational security and compliance requirements. By adopting a best-of-breed solution like TrojAI, enterprises can create a robust security ecosystem that is far more capable of mitigating a wide range of AI risks.
How TrojAI can help
Our mission at TrojAI is to enable the secure rollout of AI in the enterprise. We are a comprehensive AI security platform that protects AI/ML applications and infrastructure. Our best-in-class platform empowers enterprises to safeguard AI applications and models both at build time and run time. TrojAI Detect automatically red teams AI models, safeguarding model behavior and delivering remediation guidance at build time. TrojAI Defend is a firewall that protects enterprises from real-time threats at run time.
By assessing the risk of AI model behaviors during the model development process as well as protecting model behavior at runtime, we deliver comprehensive security for your AI models and applications.
Want to learn more about how TrojAI secures the largest enterprises globally with a highly scalable, performant, and extensible solution?
Visit us at troj.ai now.
