AI governance for tribes: A practical guide for tribal leaders

Tribal leaders are increasingly integrating AI more deeply into their organizational operations. But leaders who don’t first establish AI governance expose their tribes to risks like data security issues, poor operational outcomes and financial losses.

Keep reading to learn how AI governance helps tribes mitigate those risks, plus how to create an effective governance structure and AI policy to protect your tribe.

Why do tribes need stronger AI governance?

Without governance, tribes face the same risks as their non-tribal peers, like shadow AI, data privacy, bias or inaccurate outputs. But tribal leaders also carry the additional responsibility of navigating sovereignty issues and protecting tribal culture, as well as compliance challenges that are only likely to grow over time.

Key AI risk management areas for tribes include:

• Sovereignty and culture: AI use could expose your tribe’s data, including member, cultural and historical data, to AI models without tribal consent, which risks not only member privacy but culturally disrespectful AI-produced content.
• Shadow AI: Unsanctioned AI use within tribal organizations can create issues with data privacy, costs and inaccurate information flowing up to leaders or your tribal council.
• Bias and poor output quality: AI trained on limited or inaccurate data may exhibit biases or output wrong answers, and even well-trained models still hallucinate.
• Cost overruns: As AI models increasingly move to usage-based pricing, it’s become possible to run up a large monthly AI bill without realizing it until it’s too late.
• Limited regulations: There is still no comprehensive federal AI law in the U.S., but states are enacting their own rules, and the EU AI Act is already in force, with obligations phasing in through 2027.
• Lack of ROI: AI initiatives that are poorly designed or implemented will cost money but lack impact (for example, a Gartner survey found 84% of finance organizations have implemented or plan to implement AI, but only 7% report high impact).

Many tribal organizations are also currently lagging behind the private sector in AI adoption, which, if not corrected, will make it harder to catch up.

How does effective governance help tribes mitigate AI risks and improve outcomes?

AI governance helps manage AI risks and improve outcomes by setting ground rules for how your tribal organizations use AI. Effective governance chooses AI tools, defines use cases and builds guardrails to protect your data and mitigate the risk of bad outcomes.

Establishing governance typically starts with creating a governance team. Once formed, your governance team will choose AI governance pillars and create a formal AI policy that will help limit risky AI use and deliver better organizational results from your AI investments.

AI governance should be an ongoing effort, with your governance team continuing to evaluate AI-related risks and opportunities.

Guide your AI governance using policy pillars like these

Before you can write your formal AI policy, you need to define key policy pillars. Each pillar covers a critical aspect of AI governance, including areas like fairness, reliability, privacy and vendor risk. Once you have a list of pillars, you can use it to help write your AI policy.

Here are common policy pillars that may make sense for your tribe to build into your own AI policy:

Fairness and bias management

AI models can show bias towards individuals or groups based on certain characteristics like culture, ethnicity or gender. Assess how your AI tools are trained, as bias is especially likely for models that rely on smaller datasets.

Reliability and validity

With AI, input quality plays a major role in output quality. Evaluate whether you are giving your AI tools the clean, organized data they need, and regularly check AI outputs for errors.

Sovereignty, privacy and security

Sharing private tribal data with public AI models risks exposing that data to users outside your tribe. Set up controls to help ensure that tribal data is only shared with enterprise AI tools that are contractually bound to protect it. In vendor contracts, ask for explicit terms: No training on tribal data, no benchmarking and a full data export if you leave.

Transparency and explainability

Leaders should understand how AI models are being used within tribal entities. Conduct regular AI transparency and explainability reviews to make sure that’s the case.

Accountability and oversight

AI needs human oversight to manage risks, protect against bias and avoid damaging mistakes. Make sure your tribe has clear guardrails and defined oversight structures in place. As vendors embed AI agents directly into finance and ERP systems, govern each agent like an employee: Define what it can reach, who approved it and how to shut it off.

Inclusiveness

AI tools built on broad, diverse datasets are more likely to deliver outputs that reflect the broad range of human experience. This can help you not just avoid bias but actively promote inclusiveness.

Legal and regulatory compliance

Legal and regulatory compliance rules for AI are still in their infancy. However, tribes, specifically, should evaluate how AI could affect their existing compliance obligations and watch for future developments in this space.

Vendor and third-party risk

Your vendors are likely using AI too, which means that any tribal data you share with them could be at risk of being fed to public AI models. Regularly discuss this risk with your vendors and make sure they have their own responsible AI governance policies in place.

Acceptable IP use

AI outputs that draw on trademarked or copyrighted IP could create liability for your tribe. Be aware of this risk and train your employees to flag potential problem areas.

Sustainability

AI relies on resource-intensive, energy-hungry data centers to function. If your tribe has sustainability goals, you may want to restrict unnecessary AI use or buy carbon offsets.

Again, this list is just a starting point. You may wish to include some or all of these pillars when writing your own AI policy, but you may also determine you need others not mentioned here.

AI governance checklist: Follow these action steps to establish your tribe’s own AI governance process

To establish an AI governance process for your tribe, you should create a governance team or working group. This group should define AI dos and don’ts, choose governance pillars and consider how AI will affect your tribe, its data and its operations. At that point, you can write your tribe’s formal AI policy.

Here are key steps to follow when establishing AI governance:

1. Choose your AI governance team

Establish a diverse group of tribal leaders, organizational managers and other stakeholders. This group should include a range of perspectives to help the group consider how AI affects your tribe, its businesses and its people.

2. Level set for AI knowledge

Your whole governance team needs to have an active working knowledge of AI. Make sure everyone on the team understands the different types of AI, like agents or generative AI, key risks and how technology is evolving today.

3. Decide on AI dos and don’ts

Define your AI dos and don’ts. This is essentially deciding how you want your tribal organizations to use AI, and what sort of usage is off-limits. For example, using an AI tool to help with grant writing or assist in conducting fraud checks may be a do, but delegating those tasks entirely to AI would be a don’t.

4. Select AI governance pillars that fit your needs

Based on your do’s and don’ts, along with other considerations like compliance requirements or tribal culture, choose your AI governance policy pillars. Use the policy pillars list in the previous section as a starting point here and then adapt it to meet your tribe’s specific needs.

5. Collaborate with other stakeholders

Your AI governance team needs to be able to make crucial governance decisions without getting bogged down, but it should also consult a wide range of stakeholders when possible. Open communication between your governance team, tribal leaders and employees within tribal entities will help ensure both more effective policies and stronger policy adoption.

6. Talk about any new oversight processes you may need

Your governance team should create specific oversight processes for areas like AI monitoring or approving a new AI tool. These can then be included in your formal AI policy.

7. Clarify which AI tools are acceptable to use

Clearly define which AI tools your tribal organizations are allowed to use. For example, you may want to state that tribal employees can only use enterprise AI tools your tribe has specifically contracted with, as those will protect your data.

8. Write your formal AI policy

Turn your dos and don’ts, policy pillars, oversight ideas and acceptable tools list into a formal written AI policy. You can write your own policy from scratch, but you may also find it simpler to download a sample AI policy and adapt it to fit your needs.

Read more

• AI for Tribes: Managing risk and maximizing impact
• AI ROI: How to get more business value from your AI spending
• AI risk management: Business strategy, governance and more