Demystifying AI Governance: Ensuring ethical and compliant AI in the public sector

AI is no longer a distant possibility in government—it’s here. From automating paperwork to analyzing satellite imagery, public sector agencies are exploring ways to use AI to improve services, boost efficiency and deliver better outcomes for citizens. But as the adoption curve accelerates, so does the pressure to make sure AI is deployed responsibly.

The challenge? AI raises as many questions as it answers. Can we trust its recommendations? How do we stay compliant with fast-changing privacy and AI regulations? Without clear guardrails, the risks of missteps—ethical, legal and reputational—are high.

That’s why AI governance has become the new cornerstone of responsible innovation in government.

See how Collibra Public Sector helps US federal agencies.

Why ethical AI frameworks matter for government

For federal agencies, the stakes around AI aren’t just technical. They’re societal. When citizens interact with AI in public services—whether applying for benefits, contesting a parking ticket or accessing healthcare—trust is on the line.

Recent surveys underscore the urgency. Seventy-eight percent of CIOs said scaling AI and ML use cases to create value is their top priority over the next three years.1 And nearly every state in the U.S. now has or is drafting comprehensive privacy or AI laws, with the FTC signaling tougher enforcement.

This new regulatory reality means government agencies must balance innovation with accountability. Citizens don’t just expect fast, digital-first services. They expect those services to be fair, transparent and compliant.

What AI governance is—and why it’s different

AI governance is the framework of rules, processes and responsibilities that ensure AI is developed, deployed and monitored in an ethical and compliant way.

It’s more than risk management. Done right, governance empowers agencies to trust AI outputs by knowing exactly what data trained a model and how it was used. It helps them comply with regulations like GDPR, CCPA or state-level AI acts by enforcing policies consistently. And it lets them consume AI confidently with transparency and safeguards that protect citizens and strengthen public trust.

At Collibra Public Sector, we define AI governance as applying visibility, control and accountability across the full AI lifecycle—from data input to model output. That means cataloging datasets, tracing models, linking them to policies and continuously monitoring results.

The federal government’s unique challenges

U.S. federal agencies face some of the toughest hurdles when it comes to responsible AI. Regulatory complexity is one. In 2025 alone, eight new state privacy laws are coming into effect, adding to an already fragmented compliance landscape. Agencies operating across states or sharing data federally must reconcile overlapping rules while maintaining transparency and public accountability.

At the same time, AI governance has moved to the top of the national agenda. The last two administrations—via the 2023 White House Executive Order on Safe, Secure, and Trustworthy AI and the 2025 directive by the current administration—have established AI as a government-wide priority. Together, these Executive Orders direct agencies to build stronger safeguards for privacy and transparency while accelerating AI innovation across missions. They also emphasize the need for common frameworks—like the NIST AI Risk Management Framework (RMF)—which defines how agencies map, measure and manage AI risk that aligns with their commitment to sustaining public trust.

These directives signal a lasting shift. That responsible AI is no longer optional. Agencies are now required to demonstrate oversight of the data, models and systems that inform their AI operations. Knowing what data trained a model, where it came from, and how it’s being used across systems is now table-stakes.

Sensitive data compounds the challenge. Government AI projects often involve highly sensitive information such as tax records, health data and citizen identifiers. Misuse or breaches can erode public trust instantly.

Fragmented governance adds yet another layer of risk. Most agencies still manage data in silos across separate apps, clouds and legacy systems. This makes it nearly impossible to get a full picture of how data flows into AI models.

These challenges underscore why governance isn’t just an administrative checkbox. Agencies need to automate visibility and control across the entire AI lifecycle, creating the accountability and assurance citizens expect from the government.

The solution: Unified AI governance.

A proven framework for AI governance

Collibra’s AI Governance framework gives agencies a repeatable process to build responsible AI programs. It’s designed to align with federal mandates such as the 2023 and 2025 Executive Orders on AI and the NIST AI RMF, ensuring agencies can operationalize national principles for safe, secure, trustworthy AI.

The RMF’s core functions—map, measure, manage and govern—are reflected in our four-step approach to AI governance, helping agencies translate policy requirements into measurable, auditable practice.

1. The first step is to define the use case. Agencies must clarify what problem they are solving, what data will be used, who owns the model and what risks are involved. Defining the use case upfront helps ensure relevance, feasibility and accountability.
2. The second step is to identify and understand the data. AI is only as good as the data behind it, so agencies must catalog datasets, evaluate quality and check compliance before training models. Active metadata and data lineage tools help trace where data comes from and how it changes.
3. The third step is to document models and results. Transparency is key, and documenting models—including inputs, outputs and assumptions—creates an audit trail. This allows teams to trace results back to datasets and policies, ensuring explainability for regulators and the public.
4. The fourth step is to verify and monitor continuously. Governance doesn’t end at launch. Agencies must monitor models in production for drift and accuracy, and retrain as needed. Ongoing oversight ensures AI stays compliant and reliable over time.

The truth is it’s your blueprint for accelerating innovation responsibly.

Turning compliance into acceleration

Some see governance as a brake on progress. In reality, it’s the opposite. Unified governance accelerates every AI use case safely.

When data is cataloged, policies are automated and context is built in, teams spend less time hunting for datasets or reinventing compliance processes. Models trained on high-quality, well-understood data deliver more accurate and trusted results. With governance in place, agencies can expand AI use cases with confidence, knowing safeguards apply consistently across the ecosystem.

This is what we call Data Confidence™—the ability to innovate boldly while knowing your people are using the right data safely.

Practical steps for agencies

If your agency is exploring or already implementing AI, there are three priorities to focus on now.

1. First, unify governance across silos by moving to a model that spans every system, source and user.
2. Second, automate visibility and compliance since manual documentation won’t scale.
3. Third, create active links between policies and AI use cases to ensure rules and regulations aren’t just written on paper but embedded in how AI operates day to day.

The road ahead: trusted AI in the public sector

AI has the potential to revolutionize public services — from accelerating benefits processing to strengthening fraud detection and improving citizen engagement. But achieving these goals requires trust. Without clear governance, these same systems could just as easily undermine trust,violate privacy and erode public confidence.

Federal mandates now make that trust non-negotiable. The 2023 and 2025 Executive Orders on AI call for stronger oversight, transparency and accountability across the federal landscape. The NIST AI RMF provides the playbook. Together, they set the direction. But agencies still need a way to operationalize these directives.

That’s where Collibra Public Sector can help your agency.

Collibra provides agencies with a unified data and AI governance platform that provides a practical foundation to apply these national principles. Automatically tracing datasets. Linking them to models and policies. And creating continuous visibility across the AI lifecycle. When you know what data trained your models, how it's being used, and can confirm compliance, your agency can innovate with confidence.

That’s Data Confidence™. And it’s key to trusted AI in government.

The AI race is already on. Agencies that invest in governance today will be first to reap the rewards tomorrow—delivering better services, stronger compliance and deeper public trust.

1 Databricks, CIO Vision 2025

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  Collibra Public Sector

  Unified governance for data and AI