As regulators worldwide race to define rules for artificial intelligence, companies are grappling with how to govern AI responsibly. For those of us who have navigated decades of evolving privacy regulations, this moment feels familiar: a fragmented global landscape, rising stakeholder expectations, and the pressure to act before all the rules are clear.
At RadarFirst, we’ve spent years helping enterprises manage privacy and compliance across hundreds of jurisdictions. That experience taught us that compliance isn’t about chasing checklists; it’s about aligning purpose, accountability, and trust. As AI becomes a new frontier of risk and opportunity, the same principles apply.
Don’t Start with a Framework—Start with a Question
The rush to implement AI governance has prompted a wave of checklists, tools, and templates. For Chief Compliance Officers, this is understandable. But proper governance doesn’t begin with controls. It starts with a question: Why are we governing AI in the first place?
This deceptively simple question is foundational. It demands that we root our AI governance efforts in the organization’s core mission, values, and purpose. Are we seeking to build trust with customers? To safeguard against bias? To preserve human dignity? To ensure regulatory compliance and operational resilience? Whatever the answer, it must come first—before defining risks, before issuing policies, and before standing up a governance committee.
Without this clarity, governance becomes performative: policies are adopted without teeth, risk frameworks grow detached from real-world impacts, and oversight bodies drift into ceremonial roles. Worse, AI governance devolves into a compliance theater—checked boxes without meaningful accountability.
Purpose Dictates Policy
Just as privacy frameworks rely on purpose limitation, AI governance must be grounded in how and why AI is being used. A chatbot supporting customer service introduces vastly different risks than an internal workflow tool. Yet too often, companies apply the same governance to both.
Start by clarifying intent: Are you enhancing customer experiences? Automating manual tasks? Exploring new business models? These answers define the scope of governance and determine what risks matter most.
Identify Stakeholders and Assign Accountability
In privacy, we learned that data touches every corner of the business. AI is no different. Governance must be co-created by those who develop, deploy, and are affected by AI systems—from engineering and legal to HR and customer experience.
Just as crucial as stakeholder inclusion is accountability. Who owns the risk? Who decides when to pause or retire an AI system? Governance must define not only who is involved, but also who has the authority to act.
Tailor Governance to the Audience
Effective governance isn’t just about controls—it’s about communication. Policies aimed at boards, regulators, employees, and customers all require different framing.
Regulators need documentation. Boards want assurance of oversight. Customers expect transparency. Internal teams need practical, actionable guidance. If your policies aren’t aligned with your audience, they risk failing where it matters most in application.
Translate Purpose into Policy (Not Just Performance)
Once you’ve clarified your intent and aligned your stakeholders, policy development begins. But resist the urge to build policy by copying frameworks. Without purpose, tools like model registries or risk checklists become busywork.
Instead, let intent guide your governance pillars:
- If your goal is trust, prioritize transparency and data integrity.
- If you rely on third-party AI vendors, ensure that you have contractual protections and accountability.
- If AI impacts human rights, define where human intervention is required.
And remember: AI doesn’t stop learning once deployed. Lifecycle governance, monitoring, retraining, and auditing are essential.
Governance as a Competitive Advantage
When approached intentionally, governance becomes a strategic asset rather than a constraint. It builds confidence with regulators and customers, empowers teams, and accelerates innovation by reducing ambiguity.
We’ve seen this evolution in privacy. Companies that once treated compliance as a checkbox now compete on data ethics and trust. The same will happen with AI.
The best AI policies aren’t the most complex. They’re the most aligned with business goals, user expectations, and ethical standards.
Start with a Conversation
If you’re at the beginning of your AI journey, don’t start with templates. Start with three questions:
- Why are we using AI?
- Who does it affect?
- Who needs to trust or approve what we’re doing?
These questions reveal the foundation your governance program needs and keep you focused on outcomes, not just appearances.
At RadarFirst, we’ve helped organizations master privacy governance in a complex global landscape. The AI era calls for similar precision, empathy, and strategic alignment. Let’s not reinvent the wheel; let’s build on what we’ve learned.
