As AI becomes more integrated into enterprise technology stacks, AI applications become prime targets for cyberattacks. In response, many cybersecurity teams are adapting existing cybersecurity practices to mitigate these new threats. One such practical measure is red teaming: the effort to reveal weaknesses in a system and develop responses to observed threats by playing the role of the enemy.
Although this exercise is certainly essential, recent reports and anecdotal evidence shows us that red teaming is not that simple when it comes to securing AI applications.
To effectively protect these new environments, cybersecurity teams must understand the changing nuances of red teaming in the context of AI. Understanding what has changed with AI (and what hasn’t) is a critical starting point for guiding red team efforts in the years to come.
Why AI is flipping the red team script
In the pre-AI era, red teaming meant conducting a stealthy procedure aimed at finding vulnerabilities and exploiting them, usually without warning to the security team and with a specific goal in mind (e.g., accessing a server essential to company operations). But with the advent of AI, the red teaming process is evolving. Instead of being a one-time ordeal with a single goal, the process becomes much more frequent and widespread.
Unlike previous types of software, AI models become smarter over time. This constant change means that new risks can emerge at any time, making them incredibly difficult to anticipate. A one-size-fits-all approach to red teaming simply won’t work. Because the capabilities of these models increase over time, cyber teams are no longer red teams based on a static model.
Another change: when you start working with a third party LLM, all you can see is the model itself, not the data and code behind it. This is like assessing a car’s problems without being able to look under the hood, and it’s in stark contrast to what we’re used to with traditional software.
Red teaming AI applications is no longer just about having a checklist of things to watch out for and going through it. To identify vulnerabilities, cyber teams must constantly find creative ways to poke holes in models and closely monitor model behavior and results.
On top of that, teams need to be carefully considered when combining an LLM with external plugins. The interconnectivity of LLMs requires that you reorganize the entire system, starting with a very clear goal. For example, let’s say you want an LLM to disclose sensitive information. Once you have successfully generated this vulnerability, you need to identify not only model weaknesses, but also system-wide protective measures to mitigate the downstream effects of this type of attack.
When it comes to AI, it’s not just about red teaming your models: red teaming for interconnected applications is also important. Only by broadening the scope of your red teaming efforts can you sufficiently identify potential vulnerabilities and proactively build operational protection around them.
Cybersecurity 101 still applies with AI
As with any type of software, red teaming alone is never enough. With LLMs in particular, operational protections are essential to preventing attacks. New threats will emerge every day and you need frameworks and procedures that protect your applications at all times. Conclusion : AI Security requires you to pull out all the stops and all traditional cybersecurity practices must remain in effect.
For example, you need to disinfect your databases. If you have an LLM that can access an internal database, make sure the data is cleaned before entering the model. Additionally, keep your access controls in check. LLMs should have as few privileges as possible to minimize damage in the event of a compromise.
Securing AI models is a whole new challenge that almost all cybersecurity companies will eventually have to address. Red Teaming is a great place to start, but it also requires you to challenge your understanding of the Red Teaming process and complement those efforts with proven security strategies. The more cybersecurity professionals who master these nuances, the closer we will be to delivering on the promise of AI.
