As AI systems become more powerful and integrated into everyday life, governance is no longer a “nice-to-have”; it’s a must. Whether you’re aligning to emerging regulations like the EU AI Act, meeting internal standards for risk and safety, or ensuring your AI systems are meeting your enterprise’s business goals with scale and efficiency, the ability to govern AI responsibly at speed is a game-changer.

Yet, governance and developer velocity often feel fundamentally misaligned. Risk assessments are frequently manual, evaluation tools are scattered, governance requirements are unclear, and policies rarely map cleanly to real-world implementation. The result? Bottlenecks and delays gaps that frustrate both governance teams and developers.

Effective AI governance demands a new balance—one that enforces oversight without impeding innovation. It also requires multiple stakeholders collaborating effectively with each other. Compliance officers and chief AI officers must determine what needs to be assessed to comply with company policies and regulations, while AI developers need to operationalize these requirements by generating the right qualitative and quantitative evidence. Unfortunately, the handshake between these personas is often not smooth can create friction in the governance process. This challenge is precisely why we formed these strategic partnerships—to bridge the gap between governance requirements and technical implementation.

Traditional methods tend to create friction, slowing down deployment or leading to incomplete compliance. It’s a trade-off most organizations can no longer afford.  That’s where Azure AI Foundry steps in.

Turning Governance into a Native Part of AI Development

Azure AI Foundry aims to simplify and accelerate the path to responsible AI by integrating evaluation tooling directly with leading governance platforms. Foundry allows you to define, execute, and monitor AI risk and compliance workflows, without breaking the developer experience or disrupting their velocity.

Whether you’re running LLMs or agent-based systems, Foundry lets governance and dev teams work in sync. Evaluation plans are typically defined within your chosen governance platform—based on factors like risk profiles, compliance needs, or business context. These plans are then executed using Azure AI Foundry, with results seamlessly synced back to the governance layer. This enables both governance and development teams to remain aligned throughout the evaluation process. This approach offers several key advantages:

• Reduced time-to-compliance for AI systems
• Improved collaboration between governance teams and developers
• Continuous compliance monitoring throughout the AI lifecycle
• Standardized evaluation methodologies across the organization
• Comprehensive documentation for regulatory audits

Think of it as DevOps for AI governance—integrated, automated, and transparent.

Why This Matters Now

AI governance isn’t just about ticking boxes—it’s about building trust at scale. As scrutiny intensifies, businesses need a sustainable way to implement evaluations for fairness, robustness, safety, security, and explainability. Iterate fast without introducing risk.

Azure AI Foundry solves this by making governance programmable, repeatable, and aligned with development workflows, instead of something that happens after deployment.

Prebuilt Integrations That Power Your Governance Stack

Azure AI Foundry now offers several prebuilt integrations with key AI governance platforms (Microsoft Purview Compliance Manager, CredoAI, and Saidot), each addressing different aspects of the AI governance challenge. What makes these integrations truly powerful is how seamlessly evaluation plans flow from governance platforms into Azure AI Foundry.

Each of these AI governance solutions—Credo AI, Saidot, and Microsoft Purview Compliance Manager—offers a distinct approach to determining risks and controls. For instance, Saidot’s governance platform enables teams to generate evaluation plans specifically tailored to risk profiles, while other partners emphasize different mechanisms such as control mappings or assessment workflows. These platforms define the ‘what’ and ‘why’ of AI evaluation, which are then operationalized through Azure AI Foundry.

From there, Azure AI Foundry automatically maps these plans to the appropriate evaluators in the Azure AI Evaluation SDK. No more guessing which metric applies to your model or how to run the test correctly. We provide ready-to-use sample notebooks and automated execution flows, so developers can focus on building, not interpreting policy.

Once evaluations are executed in Foundry:

• Results are stored in the Azure AI Foundry Evaluation tab, giving AI developers full visibility into what passed, what failed, and where to improve.
• Simultaneously, results flow back into the governance platform, enabling compliance officers to track, audit, and collaborate on next steps—whether that’s approving a deployment, requiring model changes, or triggering further reviews.

This creates a closed-loop system: governance teams define the “what” and “why,” Foundry handles the “how,” and both sides stay aligned throughout the process. Let’s explore these key integrations that are transforming how organizations approach AI compliance.

Microsoft Purview Compliance Manager: Turn regulatory requirements into technical actions.

Microsoft Purview Compliance Manager helps users translate regulations like the EU AI Act into actionable tasks that can be implemented using Azure AI Foundry. This integration facilitates the operationalization of AI policies, meeting regulatory requirements, and demonstrating compliance continuously without affecting development speed.

Customers can utilize AI Foundry’s security features, such as bias detection, hallucination, and safety measures, to help comply with AI regulations. This integration also includes evidence attachment capabilities where AI Application Evidence can be tagged with regulatory controls to support compliance from the application development phase. With evidence and regulatory control assessment consolidated in one place, the governance team can efficiently manage AI application audits.

Credo AI: Bridge the gap between policy and implementation

Credo AI is powering trust by aligning and bridging the gap between AI governance team and AI developers. Through seamless integrations with Azure AI foundry, Credo AI ensures trust, compliance and governance are embedded throughout the AI lifecycle – minimizing compliance risks, streamlining AI developer efficiency with ready to use governance-compliant code and accelerating trusted AI development.

The Credo AI Platform enables governance teams to define clear evaluation requirements in the Credo AI platform based on specific use case context and compliance frameworks like the EU AI Act and NIST AI RMF.

Using the integration with Azure AI Foundry, requirements are automatically translated into actionable instructions and ready-to-use code for developers in Azure AI Foundry. Evaluations for bias, hallucination, safety, and more can be triggered directly in the dev environment, with results flowing back to Credo AI as artifacts for governance teams to review and maintain a system of record

This governance-first approach helps users build every model with clear requirements in mind—accelerating approvals while protecting from noncompliance risk. This tangible value enables organizations to scale their AI solutions faster, more safely, and with higher confidence in meeting regulatory and organizational standards.

Saidot: Govern your Azure AI models in a streamlined, risk-aware workflow.

The Saidot + Azure AI Foundry integration enables organizations to connect their Azure model registry to Saidot’s governance platform, access pre-identified model risks and generate risk-based evaluation plans tailored to the specific context and use case of each AI system. This approach—developed by Saidot and further refined through our collaboration—helps identify relevant risks and align evaluation activities accordingly. Teams can then execute these plans directly in Azure, review results in Saidot, and engage risk and compliance stakeholders in a streamlined feedback loop.

This unified workflow can make it easier to operationalize AI policies, meet regulatory requirements, and demonstrate compliance continuously without compromising development velocity.

Building a Sustainable AI Governance Strategy

While these integrations provide powerful tools for AI governance, organizations must also develop a comprehensive strategy that encompasses people, processes, and technology. Here are some key considerations:

1. Establish clear governance roles and responsibilities: Define who is accountable for different aspects of AI governance, from technical evaluation to risk assessment and compliance reporting.
2. Implement a risk-based approach: Not all AI systems require the same level of governance burden. Develop a framework for categorizing AI applications based on their potential impact and risk profile and identify these factors in the AI governance platform.
3. Foster a culture of responsible AI: Implement processes where trustworthy AI considerations and governance requirements are embedded in the organizational culture, not just in technical processes.
4. Continuously monitor and improve: AI governance is not a one-time exercise. Implement mechanisms for ongoing monitoring and improvement of governance practices.

The Bottom Line

With Azure AI Foundry, governance is no longer a blocker; it’s a strategic enabler. By uniting evaluation tooling with leading AI governance platforms, Foundry empowers teams to align AI development with regulations from day one, automate evaluations and simplify audit trails, and move fast with confidence, knowing governance is built in.

Each partner brings a unique perspective to operationalizing AI governance: Saidot focuses on risk-profile-driven planning, Credo AI emphasizes policy-to-scorecard mappings and impact assessments, while Microsoft Purview Compliance Manager integrates governance into broader enterprise compliance frameworks. These approaches complement Azure AI Foundry’s flexible evaluation execution layer.