Price depends on scope, risk, and cadence. Most small companies start with a short assessment, a policy pack, and a use case register. Ongoing help covers reviews, tests, and training. Spend less by keeping scope focused on the few use cases that matter.

Choose a light baseline, publish a short policy pack, create a simple use case register, and add minimum monitoring. Keep it to what your team will use every week. Expand only when risk grows.

Ethics are principles such as fairness and transparency. Governance turns those ideas into policies, controls, tests, and audits. Ethics asks what is right. Governance makes the right behavior repeatable, testable, and provable.

Keep it simple and focused on outcomes. Name one owner, list AI use cases with a risk tier, publish clear guardrails, and add short reviews for high risk cases. Bake checks into your existing product and security processes. Scale the program as risk grows.

Poorly governed AI leads to privacy violations, biased outcomes, leaks of secrets and intellectual property, and weak explainability. It can also be abused by users or attackers. These problems cause complaints, takedowns, fines, and lost deals. Add guardrails, testing, and logs so you can show your work.

Map data flows for each AI use case. Choose a lawful basis. Minimize and pseudonymize training data. Explain when AI is in the loop. Honor data rights, secure training sets, and run DPIAs when risk is high or data is sensitive.

AI governance is how you decide, document, and defend the way AI is designed, deployed, and monitored. Good governance reduces legal and brand risk, speeds enterprise deals, and keeps models aligned with your goals.