SailPoint Innovation, Technology & Agility

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

At SailPoint, we think about incorporating AI in a few distinct capacities. The first is internal productivity. For example, many of our engineers use AI-powered coding tools. We’ve observed improvements in the form of reduced cycle times to complete tasks and found that developers who use these tools feel both more productive and more fulfilled by their work. My teams have also used AI to summarize meeting notes, generate documentation for code, create synthetic data for experiments or automated testing and build internal knowledge bases that enable faster and more targeted retrieval of technical information. We’re continuously exploring ways to apply AI to make ourselves more efficient and better at what we do. 

The second is incorporating AI directly into our products to help improve the overall experience and security posture of our users. SailPoint knows from experience that identity security is difficult. Organizations are dynamic. Organizations evolve, and each one’s security and compliance efforts need to evolve as well. AI provides a mechanism to help extract important insights at scale and continuously adapt to the changing security needs of our users. 

What strategies are you employing to ensure that your system and processes keep up with the rapid advancements in AI and ML?

The first priority was to cultivate a robust data infrastructure. AI relies on high-quality and accessible data. SailPoint has developed and matured that infrastructure over time and continues to invest in it. Having data in a central location with mechanisms for governance and efficient access enables faster experimentation with new tech.

We also invest in our own infrastructure, tools and environment to build and deploy ML models. This includes components, such as our model registry, which is used for version control of our models and to enable collaboration between our engineers and our feature store, which allows us to track and reuse features across different models. Together, these types of components allow us to quickly integrate new advancements.

There’s an organizational component as well. We employ a hybrid structure that encourages engineers to build expertise in specific domains and adapt to their local needs while also establishing centralized AI resources and standards. We also provide avenues for collaboration so that our engineers can integrate new technologies relevant to their domain first and then bring that knowledge back to the broader group. 

Can you share some examples of how AI and ML has directly contributed to enhancing your product line or accelerating time to market?

A key metric at SailPoint is time-to-value for our users. We recognize that identity security can feel like a heavy lift, and we want to make that user journey as seamless as possible. That starts on day one with an efficient setup. Many businesses have hundreds or even thousands of applications they want to integrate with our identity security solution. This involves a significant amount of manual effort. We recently introduced an AI-powered application onboarding capability, which automates the discovery of applications and provides configuration recommendations based on usage patterns observed over time. This helps our users get up and running faster. 

We’ve also incorporated AI capabilities into our product line to help users implement effective identity security strategies at scale. For example, Role Discovery analyzes access patterns within an organization and groups access into roles that can be more easily assigned and managed. Identity Outliers enables administrators to more quickly discover and remediate risky access. These innovations help empower organizations to enhance their security posture and focus more on their core business objectives.