Feeling the pressure to roll out Microsoft Copilot fast, but unsure if your data estate is ready?
This webinar focused on unlocking the full potential of Microsoft 365 Copilot through robust data governance and security practices.
Eoin Fahy, Microsoft Security Specialist, provided a deep dive into Microsoft’s approach to securing generative AI tools like Copilot. He emphasised the importance of the Shared Responsibility Model, where Microsoft ensures infrastructure security and data privacy, while organisations must manage access, protect data, and enforce responsible use. He showcased how Microsoft Purview and Intune can prevent data leakage, enforce sensitivity labels and monitor AI usage through tools like Data Security Posture Management (DSPM) and Defender for Cloud Apps.
Key takeaways:
- Enable sensitivity labels to ensure Copilot inherits protection from source data.
- Deploy DLP policies to block sensitive data from being shared with third-party AI tools.
- Use DSPM to audit AI usage and identify risky behavior.
- Block shadow AI apps with Defender for Cloud Apps.
Nivasha Sanilal, Cloud Essentials’ Compliance Lead, followed with a practical framework for implementing AI governance. She introduced Cloud Essentials’ five-step methodology, which integrates compliance, risk management, and technology controls. She stressed the need for stakeholder education, ethical AI policies and a governance panel to align business and regulatory priorities. Her message is clear: successful AI deployment requires more than technology—it demands a holistic strategy involving people and process.
Key Takeaways:
- Run a Copilot Maturity Assessment to benchmark readiness and risk.
- Establish a governance panel to align compliance and deployment priorities.
- Educate users on AI risks, bias and responsible use.
- Review and update policies to reflect AI-specific risks and controls.
Johann van Schalkwyk, Cloud Essentials’ Head of Solutions, concluded the expert presentations by outlining Cloud Essentials’ Data Governance as a Service. He detailed our crawl-walk-run approach to deploying Microsoft Purview, including pilot testing, policy simulation and ongoing support. Johann emphasised the importance of continuous improvement, incident response and reporting to maintain governance maturity.
Key Takeaways:
- Leverage advisory support to mature compliance and governance over time.
- Start small with manual labeling and simulate policies before full rollout.
- Use pilot deployments to refine controls and engage champions.
- Monitor adoption metrics to drive continuous improvement.
In conclusion, AI readiness is not a one-size-fits-all journey. Each organisation must tailor its deployment strategy to its unique maturity level, infrastructure and risk appetite. Success in this space hinges on collaboration—data governance is a team sport that requires alignment between IT, compliance and business stakeholders.