If 2023 and 2024 were the years of AI experimentation, 2025 is the year of formal governance.
New regulatory milestones, fresh guidance from standards bodies, and maturing internal controls have made AI governance best practices a board-level priority.
In the EU, the Artificial Intelligence Act began phasing in requirements earlier this year and added additional obligations for general-purpose AI starting August 2, 2025, with guidance aimed at models that present systemic risk; non-compliance can carry fines up to 7% of global turnover.
This article lays out a practical blueprint you can use right now: A clear definition of AI governance; AI governance best practices you can adopt across industries; deep dives on policies, risk controls, and team alignments; and, finally, why DataGalaxy is the best-value platform to bring it all together at scale.
What is AI governance?
AI governance is the set of policies, processes, roles, and controls that ensure AI is trustworthy, lawful, and aligned with business objectives across its lifecycle from idea intake and data sourcing to deployment, monitoring, and retirement.
This framework ensures organizations remain aligned with ethical standards, focusing on transparency, fairness, accountability, privacy, and security.
AI governance encompasses every stage of the AI lifecycle, from design and training to validation, deployment, monitoring, and decommissioning. It involves both technical measures, like model documentation, explainability, and performance monitoring, and organizational steps, such as role assignment, audit procedures, and stakeholder alignment.
AI governance best practices at a glance
Below are the AI governance best practices successful organizations are adopting:
Start with strategy
Treat AI as a portfolio of business-aligned use cases. Evaluate value vs. feasibility, set guardrails early, and define what “Good” looks like for your company before any model is trained
Make data trustworthy by design
Establish lineage, ownership, and quality monitoring so model inputs are explainable and auditable
Create actionable policies
Document acceptable uses, data sourcing rules, model validation gates, human-in-the-loop requirements, and incident processes
Implement risk controls end-to-end
Use structured risk assessments, pre-deployment evaluations, red-teaming, and ongoing monitoring for drift, bias, privacy, and security
Implement risk controls end-to-end
Use structured risk assessments, pre-deployment evaluations, red-teaming, and ongoing monitoring for drift, bias, privacy, and security
The common thread: Good governance is operational, not ornamental.
It lives where people work and is reinforced by the same platforms that manage data, metadata, and models.
Designing data & AI products that deliver business value
To truly derive value from AI, it’s not enough to just have the technology.
- Clear strategy
- Reasonable rules for managing data
- Focus on building useful data products
AI governance policies
Make rules real & traceable
The following are best practices for various areas of AI governance policies and regulations:
1. Acceptable use & purpose policies
- Define what AI can and cannot be used for in your organization, including prohibited use cases and sensitive domains
- Link every AI initiative back to a stated business purpose and legal basis. Under risk-based regimes like the EU AI Act, documenting intended purpose and risk classification is foundational to downstream obligations
2. Data sourcing, IP, and consent
- Specify approved datasets and data collection methods
- For generative systems, keep a record of how training data is sourced and filtered to meet copyright and transparency requirements
3. Human oversight
- Describe where and how humans can intervene, approve, or override model decisions (especially in high-impact contexts)
- Set escalation and “kill switch” procedures for suspected harm or drift. These elements map directly to high-risk governance expectations under the EU AI Act.
4. Documentation & transparency
- Mandate model cards, evaluation summaries, known limitations, and change logs.
- Ensure evidence is audit-ready and tied back to policies and approvals so you can demonstrate “due care” to regulators and customers
AI governance risk controls
Measure what matters
Here are a few recommendations on steps to take to ensure your AI risk is managed and monitored in the following ways:
1. Risk assessment & classification
- Start each AI effort with a structured risk assessment covering potential impacts to safety, privacy, fairness, IP, security, and reputation
- Classify risk levels and required controls. Ensuring you mirror the risk-based approach in the EU AI Act.
2. Data & model lineage
- Maintain end-to-end traceability between datasets, features, models, and outputs
- Lineage is essential to explainability and to remediating incidents quickly
- A strong governance platform will automate lineage capture and make it visible to both technical and non-technical audiences
3. Monitoring & drift management
- Once live, monitor data quality, model performance, fairness metrics, and feedback loops
- Trigger alerts when input distribution shifts or outputs degrade
- Pair monitoring with a change-management workflow and rollback plan
4. Security & access control
- Apply least privilege to training and inference environments, protect model artifacts, and log access
- Combine traditional security controls with AI-specific checks (e.g., prompt injection defenses, supply-chain integrity)
- Regulatory guidance for systemic-risk models underscores the need for adversarial testing and cybersecurity protections
5. Reporting & auditability
- Design dashboards and evidence repositories for policy adherence, risk posture, and control effectiveness
- Ensure you can produce documentation for regulators on short notice and that your records link decisions to data and people
The 3 KPIs for driving real data governance value
KPIs only matter if you track them.
Move from governance in theory to governance that delivers.
Download the free guideAI governance team alignments
Governance as a team sport
Be sure to include the following considerations when creating your governance teams:
1. Executive sponsorship
- An accountable executive should own the AI governance strategy and scorecard, ensuring trade-offs are explicit and value is measured, not just risk avoided
2. Cross-functional teams
- Operationalize a multifaceted team for each AI product:
- Business owner: Defines outcomes
- Product owner: Delivery & lifecycle
- Data steward: Quality & lineage
- Risk & compliance: Controls & approvals
- Security: Threats & access)
- Legal: Lawful basis & contracts
3. Portfolio & intake rituals
- Run structured intake and quarterly portfolio reviews to prioritize high-value, compliant initiatives and to retire low-value experiments
- Tools that tie business goals to models and to the underlying data help avoid “model sprawl”
4. Enablement & literacy
- Invest in playbooks, policy templates, and hands-on sessions so teams understand not only what to do but how to do it in your stack
- Governance adoption rises when people can see the policies directly in the context of their assets and pipelines
5. Governance in the flow of work
- Adoption is highest when governance happens where people already work, like in your data catalog, lineage views, model registry, and collaboration tools
DataGalaxy: The best-value governance platform for your AI program
Delivering on AI governance best practices requires a platform that unites strategy, data, and AI—not a patchwork of disconnected tools.
DataGalaxy is designed to bring policy, lineage, ownership, portfolio management, and marketplace-style access together in one experience.
Policy management made actionable
With DataGalaxy, you can document business policies and turn rules into monitored controls—then link those to specific datasets, models, and use cases.
Built-in monitoring and governance campaigns help drive adoption across domains, making governance a shared responsibility rather than a compliance bottleneck.
End-to-end lineage & context
Automated lineage and a collaborative catalog give every stakeholder, from data engineers to auditors, visibility into sources, transformations, and downstream consumption. That traceability is critical for explainability and incident response.
Blink, your AI Copilot
Ask questions. Get answers. Drive action.
Blink helps every user explore, understand, and use data in their daily work. No tickets, no filters, no delays. Ask anything in your native language and get trusted, contextual answers and recommendations.
Meet Blink!Portfolio & value management
DataGalaxy connects use-case intake, portfolio tracking, and governance so you can prioritize the right AI work, measure impact, and prove value to the business while keeping compliance tight.
Designed for responsible AI frameworks
DataGalaxy explicitly supports alignment with frameworks such as the EU AI Act and ISO/IEC 42001, letting you adapt rules, define custom risk scores, and create workflows that mirror your standards. That means you’re not governing in the abstract—you’re governing against the exact controls regulators expect.
Ecosystem-ready with 70+ connectors
From modern cloud warehouses to BI tools, DataGalaxy integrates across your stack so governance lives alongside delivery—no exporting spreadsheets or copying metadata back and forth.
Built for collaboration and adoption
A business glossary, role-based ownership, and a data/AI product marketplace make it simple for teams to find, understand, and request the right assets, accelerating safe reuse and reducing operational load on data teams. This is operational governance that scales.
An AI governance 90-day starter plan
Here, you’ll find a plan to help you begin your AI governance journey, from day 1 to day 91.
Weeks 1 – 2
- Define the scope and policy set
- Select 3–5 priority AI use cases
- Draft or refresh policies for acceptable use, data sourcing, lifecycle gates, oversight, and incidents, and map them to AI RMF functions and ISO 42001 clauses for traceability
Weeks 3 – 6
- Instrument data and models
- Catalog datasets, assign owners, enable lineage, and set up quality monitors
- Create model documentation templates and evaluation plans aligned to NIST GenAI controls
Weeks 7 – 10
- Operationalize risk controls
- Run risk assessments, red-team high-impact use cases, and configure alerts for drift and misuse
- Draft a simple audit-evidence playbook for the EU AI Act timelines that apply to your portfolio
Weeks 11 – 13
- Launch governance campaigns
- Train stewards and owners, publish the glossary, and kick off a campaign to drive policy adoption and close gaps discovered during evaluations
Final thoughts
AI’s potential is undeniable, but so are its risks and responsibilities.
The organizations that win treat AI governance best practices as a value engine, not just a compliance tax. They are aligning use cases to strategy, making data trustworthy by design, codifying policies that people actually use, and measuring risk continuously.
DataGalaxy gives you the connective tissue to make all of this real: Policies that map to assets, lineage that speeds audits and fixes, a portfolio view that keeps work aligned to outcomes, and collaboration features that turn governance into a shared habit.
FAQ
- How do you improve data quality?
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Improving data quality starts with clear standards for accuracy, completeness, consistency, and timeliness. It involves profiling, fixing anomalies, and setting up controls to prevent future issues. Ongoing collaboration across teams ensures reliable data at scale.
- How do I start a data governance program?
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To launch a data governance program, identify key stakeholders, set clear goals, and define ownership and policies. Align business and IT to ensure data quality, compliance, and value. Research best practices and frameworks to build a strong, effective governance structure.
- How does DataGalaxy support data governance and AI readiness?
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DataGalaxy combines active metadata, lineage, policy management, and business context — all in one place. This helps organizations enforce governance and prepare data responsibly for AI use cases. You get transparency, traceability, and collaboration built in — key pillars for AI and regulatory trust.
- How does a data catalog integrate with my existing tools?
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Modern catalogs integrate with your full data ecosystem — from Snowflake to Power BI. DataGalaxy includes prebuilt connectors, APIs, and automation tools that make syncing metadata seamless and scalable.
👉 See supported integrations - How does the integration support data quality management?
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The integration leverages Snowflake’s Data Metric Functions and DataGalaxy’s semantic layer to monitor, surface, and manage data quality issues. This enables teams to quickly detect anomalies, ensure data reliability, and deliver AI-ready datasets with full business context.
At a glance
- AI governance is now a board-level priority, requiring clear policies, risk controls, and team alignment to ensure AI systems remain trustworthy, compliant, and business-aligned.
- Best practices include embedding governance into workflows, making data trustworthy by design, enforcing security and oversight, and continuously monitoring for drift, bias, and risk.
- DataGalaxy enables organizations to operationalize AI governance at scale with integrated policy management, lineage, portfolio tracking, and collaboration tools aligned to global regulations like the EU AI Act.
About the author
With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.
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