Are You Purple Teaming to Secure Your Generative AI Solution?

The infusion of artificial intelligence into product development, particularly the use of generative AI technologies like large language models (LLMs), has undoubtedly paved the way for unprecedented innovation. These technologies bring with them a suite of capabilities that are transforming how we interact with digital products, from conversational AI and real-time translation to more sophisticated cybersecurity frameworks. However, as we usher in this age of AI-driven product development, it is crucial to address not just the functionality but also the security and trustworthiness of these innovations.

Cybersecurity is fundamental when it comes to products powered by AI, especially as these products become more integrated into our daily lives and business operations. As developers experiment with open models and open-source frameworks like Meta Llama 2, the need for robust cybersecurity measures becomes even more evident. The stakes are high – a failure to secure these systems could lead to exploitable vulnerabilities, data breaches, and a loss of user trust.

Recognizing this need, there is an increasing focus on an integrated approach to security known as purple teaming. Traditionally in cybersecurity, “red teams” attack and probe for system vulnerabilities while “blue teams” defend against those attacks. However, as the complexity of security challenges grows, especially with AI technologies, the need for these teams to collaborate more deeply is clear. A purple team is the embodiment of this collaboration, designed to maximize the effectiveness of both red and blue operations by combining their insights and methods into a coherent defense strategy.

In the context of AI, purple teaming takes on additional dimensions. Not only must AI-driven products be protected from external threats, but they also must be safeguarded from within, ensuring that the AI does not inadvertently generate harmful or unethical content. In this way, purple teaming plays a critical role throughout the entire product lifecycle of AI tools – from inception to deployment and beyond – and encompasses both cybersecurity and ethical AI deployment.

The advantages of purple teaming in developing AI-driven products are manifold. First, it ensures that security considerations are not an afterthought but a foundational component of the development process. By engaging both red and blue teams, vulnerabilities can be uncovered and mitigated early on. Cybersecurity benchmarks and evaluations, as seen with initiatives like CyberSec Eval, play a pivotal role here, providing metrics and tools that quantify cybersecurity risk and evaluate the propensity for AI systems to suggest insecure or malicious code.

Additionally, purple teaming extends its protective oversight to input/output operations of LLMs. This is where the safety classifiers, such as Meta Llama Guard (1), NVIDIA Nemo Guardrails (2), OpenAI Moderation API (3) and Microsoft Azure Content Filtering (4) are instrumental. They serve as critical filters, ensuring that the AI does not produce or respond to risky content. By adhering to clearly defined content guidelines, developers can preclude potential misuse or harmful outputs – thereby maintaining the integrity of AI interactions and bolstering user trust.

When seen as a comprehensive framework, purple teaming is at the core of establishing an open trust and safety ecosystem. It promotes transparency and rigor in developing generative AI models and fortifies industry standards. To translate this framework into practical application, the following strategies are recommended for integrating purple teaming into the AI product development lifecycle:

1. Before commencing testing, it's crucial to assemble a diverse team of red teamers, drawing from various disciplines and backgrounds. This diversity brings unique perspectives to the table, helping in identifying a wide array of potential risks – especially those that might not be immediately apparent to individuals with a singular focus or expertise. The recruiting process should balance those with an adversarial mindset against ordinary users to ensure comprehensive coverage of potential security and ethical issues.
2. As testing commences, a structured approach to both open-ended and guided testing is advisable. Initial open-ended tests can uncover unexpected harm areas, while subsequent guided probes, based on initial findings, will ensure that known vulnerabilities and their mitigations are stress-tested thoroughly. A structured recording system for data collection is also vital to ensure that the results from these tests are accurately captured, analyzed, and acted upon.
3. Upon completing each testing round, reporting back to stakeholders with clear and concise information is essential for continuous improvements. Red teamers' findings should inform systematic measurements and validation of mitigation strategies, refining the development of the AI product with real-world defensive applications in mind.

For product managers, the starting point for integrating purple teaming into the development lifecycle of generative AI technologies is in understanding the unique security and ethical challenges these technologies present. I suggest all product managers to be proactive in planning for security from the inception of the AI solution:

1. Firstly, they should familiarize themselves with best practices in AI trust and safety, keeping abreast of the latest guidelines and tools available for securing AI systems—such as the Responsible Use Guide provided for Meta Llama models and the tools encompassed within the Purple Llama project that include CyberSecEval and Llama Guard.
2. Once aware of the resources and tools at their disposal, product managers should strategize on assembling a diverse purple team. This includes recruiting individuals with the adversarial acumen of red teamers and the protective insight of blue teamers, ensuring that a variety of disciplines and backgrounds are represented. In this way, potential security vulnerabilities and ethical issues can be identified and addressed through various lenses and expertise.
3. Engaging with the broader AI community is also crucial for product managers. Collaborating with partners and utilizing open-source security tools enables the leveraging of collective knowledge and also sets a sturdy foundation for community-led safety standards. These alliances facilitate a more robust cycle of feedback and improvement, promoting shared learning and progress.
4. And lastly, product managers must ensure that the purple teaming approach is embedded in the product roadmap. Defining clear milestones for security assessments and ethical reviews throughout the development process will help foster a culture of continuous vigilance. In doing so, the product manager champions a product ethos where security and ethical considerations are not just an adjunct but are seamlessly integrated into the entire lifecycle, guaranteeing that the final product aligns with the highest standards of responsible AI.

As LLM technologies continue to grow and evolve, the role of purple teaming becomes ever more critical. By fostering an environment where attack and defense strategies inform one another, developers and security professionals can anticipate and neutralize threats effectively. This collaborative, dynamic approach to cybersecurity will not only fortify the AI systems of today but also pave the way for more secure, reliable, and trustworthy AI development in the future.

If you have any questions or learn more about any Generative AI Product Development Lifecycle aspect, please reach out to [email protected] or visit AI Product Institute at https://aiproductinstitute.com.

(1) https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/

(2) https://github.com/NVIDIA/NeMo-Guardrails

(3) https://platform.openai.com/docs/guides/moderation

(4) https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/abuse-monitoring