AI Governance: What You Need To Know in 2026

It’s no surprise: The workplace adoption of AI (artificial intelligence) continues to rise, with 68% of fully remote companies and 62% of midsize companies embracing the technology.

But introducing AI into your business ecosystem isn’t quite the same as, say, switching from Microsoft Office to Google Drive. Generative AI is a powerful new technology that has the ability to speed up some workflows … and bring about mistakes, errors, or data leaks at the same time.

This is where AI governance comes into play—important on both a broad industry level and within your organization.

What is AI governance?

AI governance is a collection of policies, regulatory frameworks, and even government regulations, all meant to ensure the ethical use of AI technologies and mitigate potential risks.

Note: Artificial intelligence is a broad term that encompasses a number of different tools and machine learning methods. Much of the AI governance conversation, though, focuses on generative AI and large language models (LLMs)—i.e. the technology behind widely available tools like ChatGPT—so that’s what we’ll be focusing on in this overview.

Why is AI governance important?

AI governance is important because it helps us mitigate the potential risk found in AI applications. These risks can include: 

• Bias toward specific groups or situations based on the algorithm’s training data
• Ethical concerns, which again may be related to training data
• Data leaks and loss of privacy through use of publicly available AI models
• Misuse such as deepfakes (i.e. a video that purports to be of a public figure but is actually a computer-generated fake)

The level of risk present in an AI tool varies: every machine learning algorithm is a little different. Yes, machine learning engineers do tend to use some standard methods to train their AI models. But the very nature of AI means that these algorithms change based on their data and outputs.

When a user feeds data into an AI tool, this information may be used to train and change the model further—a practice that has led to growing concerns about data security at a corporate and government level.

And even if you know that your data won’t be used to train a generative AI model, there’s still the risk of the AI creating an output that’s biased or factually incorrect.

By setting standards that guide how all AI applications are developed and used, we as a society can ideally better protect personal information, shore up corporate cybersecurity, and reduce the likelihood of misinformation being spread via AI outputs.

How does AI governance work?

The AI governance process requires a governing body or group of individuals who can make decisions about (and create frameworks for) appropriate AI use.

This could include:

• Establishing baseline levels of acceptable AI risk‍
• Setting data quality standards that guide the training of AI systems
• Creating guidelines for reducing or mitigating bias in AI outputs
• Establishing benchmarks to evaluate how accurate and ethical AI outputs are
• Developing an AI auditing process for assessment of new models and systems
• Documenting mandatory guardrails for the development and use of trustworthy AI
• Conducting ongoing risk assessments throughout the AI model development life cycle

AI governance isn’t about setting one-time rules, though. Because AI technology continues to develop and become more complex, so must our AI governance measures. AI governance will probably look quite different in the next five years—it must adapt to the changing nature of machine learning.

And implementing AI governance frameworks is only effective if everyone gets on board, from the machine learning engineers making new AI algorithms to policymakers, business leaders, and software users.

The core pillars of effective AI governance

AI governance shares several key concepts with existing data privacy and human rights regulations, including:

• Privacy: How will data fed into an AI model be used?
• Fairness: Does the AI display bias or discrimination in its outputs? 
• Ethics: Was the AI developed with the right data for a specific use case? Are its effects clear?
• Safety: Does the AI produce outputs that can be harmful to people? 
• Reliability: Can you tell that something is AI generated? Can you trust its accuracy?

Two more key elements of AI governance are specific to machine learning:

• Explainability. The extent to which human engineers can understand how an AI model produced a specific output
• Reproducibility. How well AI engineers and researchers can replicate a model’s output

All of these AI governance pillars build on each other. For example, by improving a large language model’s explainability and reproducibility, we can create systems that are less prone to hallucinations. This, in turn, supports better AI ethics, safety, and reliability.

Who’s responsible for AI governance?

In the U.S., companies and individuals are often the ones establishing their own AI governance frameworks when purchasing or using a new tool.

Interestingly, some AI experts and founders (including Sam Altman of OpenAI) have called for the U.S. government to step in and oversee AI regulation. And while the U.S. government has yet to pass specific laws at the time of this writing, we’re starting to see more federal attention to the topic.

In October 2024, the Biden-Harris administration released a memorandum that outlined a proposed U.S. approach to AI governance. The administration issued the memorandum with a goal of “appropriately harnessing AI models and AI-enabled technologies in the U.S. government” all while protecting:

• National security
• Human rights
• Civil rights
• Civil liberties
• Privacy
• Safety

This announcement wasn’t a bill or executive order, though it did lay out some steps that the government should take in the six months after its publication.

Meanwhile, the European Union is already tackling AI governance at a government level. In August 2024, the E.U. passed its Artificial Intelligence Act, which does four key things:

1. Establishes levels of risk in AI systems
2. Places responsibility on the developers of risky AI systems
3. Anyone using high-risk AI in a professional context may have some responsibility as well
4. Requires that the creators of general purpose AI models provide their users with technical documentation, usage instructions, and information about training data

The E.U. AI Act bans several kinds of AI systems, too, including ones that:

• Use deceptive techniques
• Compile facial recognition databases with internet or CCTV images
• Use biometric categorization systems to sort and group individuals

The World Economic Forum expects that the E.U. AI Act will have wide-ranging global effects—not unlike Europe’s General Data Protection Regulation (GDPR)—over time. 

How can you implement AI governance in the workplace?

Implementing AI governance in the private sector is a complex process—especially when an organization is just starting to explore AI for the first time. You need to find the right tool for your uses, evaluate its decision-making processes, compare it against your existing ethical standards, train your team on best practices … the list goes on.

The National Institute of Standards and Technology (NIST) has an AI Risk Management Framework that can help. It outlines a useful, business-focused overview of AI risks—and provides clear suggested actions and steps to use AI more safely.

Some of the NIST’s suggestions include:

• Align AI development with existing data privacy and copyright laws
• Assess how many dependencies will exist between a new AI system and existing IT infrastructure
• Ask stakeholders what they consider to be unacceptable AI use at work
• Create transparent acceptable AI use policies
• Establish AI incident response tasks
• Detail ways to continually improve AI risk measurement

Anyone building their own large language model for internal use may also benefit from using Inspect, an LLM evaluation framework from the UK AI Safety Institute. The institute also publishes regular reports that provide more insight into how different language models work.

Learn more about responsible AI governance practices

Because AI governance is a new and evolving field, it’s important to stay up to date on new recommendations and best practices for AI adoption.

Professionals around the world can benefit from the insights contained in the NIST framework and EU AI Act, as many of the concepts are broadly applicable across borders. And in the U.S., business leaders can refer to a growing list of state AI governance guidelines.

Further professional education and reading is a great option, too. A growing number of books and online courses break down key AI governance concepts in a way that’s applicable to various industries. If you’re interested in learning more, start by checking out our short list of favorites:

• The Oxford University Handbook of AI Governance
• The University of Pennsylvania’s AI Strategy and Governance course on Coursera
• Georgetown University’s Online Certificate in AI Governance and Compliance

We’re starting to see industry certifications pop up, too, like the IAPP Artificial Intelligence Governance Professional (AIGP) designation and the NICCS Certified AI Ethics and Governance Professional course.

Go further. Consult with AI professionals

You can also opt to consult with an AI professional to learn more about how the technology works for improving safe AI use at your organization and adopting ways to leverage AI automation in your own business. Sign up for Upwork to connect with skilled consultants offering support for your most pressing AI questions.

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This article is intended for educational purposes and should not be viewed as legal or tax advice. Please consult a professional to find the solution that best fits your situation.