Just as fast as more enterprises are adopting AI in multiple areas of their operations, the guidelines for doing so are also evolving. AI governance must focus on ensuring responsible, ethical, and compliant use of AI technologies by setting up clear policies, risk controls, and stakeholder oversight. To help you get started in building your AI governance plan, Wipro offers these best practices learned from our global experience.
Six Steps to a Powerful Governance Plan
1. Data Classification at Every Stage – classify public, internal, confidential, and restricted data across all stages – from input and reasoning to output and memory – to apply the right security and privacy rules.
2. Human Oversight Triggers – design thresholds where the agent must pause or seek human intervention, such as when confidence is low or decisions impact customers directly.
3. Explainability Framework – integrate tools or logs that help trace why an agent chooses a specific action or tool, helping build trust and aid audits.
4. Dynamic Access Controls – modify what data or systems the agent can access based on real-time context, such as user role, task sensitivity, or risk level.
5. Red Teaming Exercises – test agent behaviors under adversarial scenarios like trick questions or conflicting commands to identify failure points before deployment.
6. Incident Logging and Alerting – ensure detailed logs are maintained and monitored for suspicious patterns or unexpected agent behavior, triggering real-time alerts.
Helpful Resources
There are several influential organizations shaping AI governance policies globally. These include governmental bodies, international institutions, and industry-let initiatives. By aligning your own policies to guidelines defined by these organizations, you can build a plan that reduces risks, enhances privacy and safety, and delivers the strongest ethical and compliant standards.
Here are a few of the leading groups to consult:
Governmental and Intergovernmental Bodies
Industry and Nonprofit Initiatives
These organizations are not only setting the tone for ethical AI but also influencing legislation, corporate policy, and public trust. Aligning with their frameworks helps you avoid regulatory penalties, build stakeholder trust, and ensure fairness, transparency, and accountability in your AI systems.
Examples You Can Follow
While you may be leading your company or even your industry, you aren’t the first to attempt creating an AI governance plan. A number of good examples exist in multiple sectors, including healthcare where organizations use governance to ensure diagnostic models are validated across diverse populations. Another is financial services where AI is used for auditing credit scoring models for fairness and explainability.
Some examples that Wipro recommends you review are:
GitHub Copilot Agents: These use context-limited memory and code-safety filters to avoid leaking client IP.
Salesforce Einstein GPT Agents: These allow enterprises to control memory settings, audit logs, and customize tools agents can call.
Wipro HR Agent: This is deployed with daily log review, memory wipe every 24 hours, and integrated escalation workflows for sensitive queries.
These are all practical resources you can rely upon as you keep pace with the speeding technology train that is racing business forward. When you need help, contact Wipro and let us put our experience to work for you.
About the Author
Ivana Bartoletti
Ivana Bartoletti is the Global Chief Privacy and AI Governance Officer at Wipro. A leading voice in responsible AI, she advises governments and enterprises on privacy, ethics, and regulation.
