Blogs

Featuring insights from Navrina Singh, Founder and CEO at Credo AI

Tell us about Credo AI and your role there.

I am the Founder and CEO of Credo AI. We created the AI governance category six years ago with one core goal: how do we provide control, accountability, and oversight for these very capable AI systems, especially as they start to influence decision-making in customer-facing applications and become embedded in workflows.

Credo AI focuses on the control plane – the trusted operating system for AI – so organizations can manage risk and compliance across the entire AI lifecycle.

How have your priorities evolved as AI has advanced? What are you focused on today?

Since founding the company, our mission has been consistent: ensuring AI is always in service of humanity.

We started in predictive ML, moved through generative AI, and now we’re in the agentic AI era. Across all of these shifts, our core value has remained the same. Helping organizations, especially Fortune 500 companies, adopt AI seamlessly while embedding it into real-world applications.

We work across many sectors, but healthcare, pharma, and biotech are some of our fastest-growing. In these regulated environments, AI adoption is accelerating quickly, but regulatory requirements are fragmented and complex. Credo AI aims to take on that burden – bringing operational certainty instead of fragmentation. At the same time, we’re entering a phase of autonomous AI systems. That makes continuous, contextual governance essential, not optional.

Where do organizations struggle most when moving from AI experimentation to real-world deployment?

We see three primary gaps.

First is context alignment. Organizations have multiple stakeholders across the AI lifecycle, and aligning on what “good” looks like for a specific use case is difficult. Second is the lack of a single system of record. Organizations need one place to track AI across procurement, development, and production so they can embed trust at every step. Third is the AI literacy gap. Even in advanced organizations, stakeholders come in with different levels of understanding. Creating a baseline level of AI literacy across builders and governors is critical.

How has AI governance evolved as use cases shift toward clinical and autonomous systems?

The biggest shift is that governance is no longer a point-in-time activity. It has to be embedded into workflows. If you can’t bridge the gap between builders and governors, you can’t achieve effective governance. It has to start from the beginning – during design or procurement – not after deployment.

We’re also seeing more clarity around what “good” looks like, even without consistent regulatory frameworks. Organizations are leaning on internal policies and external standards to guide them.

Finally, “shifting governance left” is now a reality. Governance has become foundational to building and deploying AI, not something layered on afterward.

What capabilities are most important to get governance right early on?

The starting point is visibility. Do you actually know where AI is being used across your organization? At Credo AI, one of the first steps in governance is cataloging all AI systems within a registry – applications, agents, models, and datasets – across the enterprise.

Once you have that visibility, the next step is risk triage. Start with high-risk use cases and take at least one through end-to-end governance. That process helps you define what to measure, where to get evidence, how to validate it, and how to generate outputs like risk or compliance reports.

Finally, governance is a multi-stakeholder effort. Builders and governors need to speak the same language. The more they understand each other’s roles, the more effective governance becomes.

How does Credo AI help organizations align perceived risk with actual risk?

This is a critical challenge. There’s often a disconnect between perceived and actual risk. We address this by making risk configurable. What’s risky for one organization may not be risky for another. For example, a patient-facing chatbot might need to meet fairness and toxicity thresholds. But those thresholds can vary between organizations. So we allow customers to define their own governance posture. What’s acceptable risk versus not, based on their context. That flexibility helps organizations move forward with greater clarity and confidence.

What role do partnerships play in advancing responsible AI adoption?

Partnerships are essential, especially in industries like healthcare where the stakes are high and the ecosystem is deeply interconnected. From the beginning, we’ve seen our role as more than just providing tooling. We’re building an ecosystem that brings together builders, policymakers, providers, and standards organizations.

In healthcare, no single organization can define “good” in isolation. It has to be shaped collaboratively, grounded in real-world constraints like patient safety, clinical workflows, and evolving regulatory requirements. Partnerships create that shared understanding. They allow organizations to align on best practices, reduce fragmentation, and move forward with more confidence as AI adoption accelerates.

How did you get involved with CHAI?

We’ve been very intentional about ecosystem partnerships, and when we were introduced to CHAI, there was immediate alignment around the mission.

What stood out was CHAI’s focus on defining what “good” looks like for healthcare AI in practice. Healthcare brings unique complexity—patient safety, sensitive data, and a fragmented regulatory landscape across frameworks like HIPAA, HITRUST, and NIST AI RMF.

We were excited to join the CHAI Partner Program this month. Through this collaboration, CHAI develops frameworks to evaluate AI solutions using consensus-based standards, and Credo AI operationalizes them. This allows healthcare organizations and developers to govern AI across clinical, operational, and emerging agentic use cases with a system of record, risk-based oversight, and audit-ready evidence.

Any final advice for organizations looking to strengthen AI governance?

Organizations need to think about how they will win with AI. Trust is the competitive moat. Governance is how you get there. AI governance provides the control, visibility, and oversight needed to build trust—whether with patients, providers, or partners. And in this next phase of AI, that trust will be the key differentiator.