By Glen Day
As businesses move closer to unlocking the full potential of Microsoft Copilot, data security is essential for a successful deployment. As I previously mentioned in my last post,“Adopt Copilot with Confidence: Where to Start and How to Ensure a Successful Deployment,” Microsoft Copilot is changing the way businesses use AI by integrating advanced automation and intelligence into day-to-day operations. It also has the potential to change workflows, provide intelligent insights, automate tasks, and enhance collaboration. However, the promise of generative AI (GenAI) can only be realized when organizations are confident their data remains secure and compliant.
In this post, I hope to explain how a successful Copilot rollout requires not only understanding your data but also proactively securing it. This leads us to our second step—fortifying your data— which focuses on access and protection controls to manage risks, prevent unauthorized access, and build trust in Copilot’s functionality.
Why Data Security is Essential for Copilot Success
Securing your data isn’t just a box to check in your organization—it’s now foundational for every AI initiative. Security is the biggest concern for companies adopting widespread use of generative AI tools. Organizations with security measures in place are 75% more likely to adopt AI, highlighting the critical role security plays in AI implementation (KPMG, Trust in Artificial Intelligence, 2023). Leaders are right to be cautious; data breaches, oversharing, and policy non-compliance can derail an AI project before it even starts.
While Microsoft designed Copilot with robust security controls, companies must implement their own complementary measures to align with organizational policies and regulatory compliance requirements. Here’s how NVISIONx delivers cohesive and actionable views of access risks to sensitive data:
Step 1: Conduct a Comprehensive Data Risk Assessment
With a comprehensive data inventory, contextual classifications, and directory services integration already in place (see Blog 1, “Adopting Copilot with Confidence”), your organization is well-positioned to perform a targeted data risk assessment. Then, you can address and manage access risks associated with sensitive data in this way:
- Detect Over-Privileged Access: Identify over-privileged access by comparing user roles against their actual access permissions for sensitive data. For example, an HR specialist may need access to employee records but should not have access to legal case files. Regularly identifying and removing excess permissions ensures that confidential information is accessible only to those with a legitimate need, strengthening data security.
- Monitor for Suspicious Data Sharing and Unauthorized Applications: Leverage SharePoint and OneDrive activity logs to detect unapproved data sharing and storage. Left unaddressed before Copilot deployment, these issues pose significant access risks, as sensitive data could be inadvertently exposed or misused by AI. By analyzing activity logs, you can identify cases where sensitive files, such as financial reports or employee records, are shared with users who do not meet “need-to-know” criteria. For instance, if an employee sends confidential HR files to a personal email, this unregulated sharing creates privacy risks that Copilot could unintentionally propagate.
GenAI’s design draws from various files and contexts, which makes tight access controls essential. If these controls aren’t established before deploying GenAI, the tool could inadvertently amplify these oversights, creating widespread exposure of restricted information.
Step 2: Collaborate on Remediating Data Risks
For a secure and efficient Copilot deployment, implement a remediation process that prioritizes collaboration with data owners. Begin by identifying high-risk files, such as those containing financial, HR, or legal information, and analyze activity logs for inappropriate sharing and access patterns. Work closely with data owners to validate access needs, refine permissions, and enforce “need-to-know” principles.
Although Copilot integrates with Microsoft Purview for access control, effective remediation may still be challenging. Consider using specialized tools that enhance risk detection and automate remediation workflows for more efficient coordination across business and technical stakeholders. This collaborative, prioritized approach helps reduce access risks while reinforcing compliance and data governance as Copilot becomes embedded in organizational operations.
Step 3: Update Policies with GenAI-Specific Risk Considerations
With GenAI in use, policies should adapt to address unique risks related to AI processing, focusing on how sensitive information is handled and safeguarded. Here’s how to enhance policy updates with GenAI in mind:
- GenAI-Specific Data Handling Policies: Develop policies that specify which data GenAI tools can process and restrict certain data categories. For example, highly sensitive information, such as PII or legal details, should be excluded from GenAI interactions. Implement tagging and labeling protocols so GenAI tools can easily identify and bypass restricted data. These policies should evolve alongside GenAI use cases and regulatory guidance.
- Automated Compliance Checks for GenAI: Set up automated compliance checks to ensure GenAI interactions adhere to policy. For instance, conduct periodic scans to monitor GenAI data usage and flag interactions with restricted data. If flagged, automated alerts can prompt a review and, if necessary, policy adjustments to safeguard sensitive information.
- Policy Evolution with Regulatory Changes: Regularly review and update policies based on new AI regulations or best practices. As privacy laws evolve, particularly around AI’s handling of PII, biometric data, or financial information, adjust GenAI data handling protocols accordingly. Policies should trigger reviews if regulatory updates impact GenAI processing, ensuring compliance.
Step 4: Continuous Monitoring for Compliance and Security Drift
Once Copilot is deployed, continuous monitoring ensures security controls remain aligned with policies and standards as they evolve. Effective monitoring practices include:
- Configuration Modification Alerts: Set up automated alerts to monitor configuration settings within Copilot, detecting potential shifts from your security standards. For example, if settings are changed to grant broader access or enable external sharing, an alert can flag these as potential risks, initiating corrective actions before they result in data exposure.
- Policy Adaptation and Updates: Regularly review policies to ensure compliance with new regulatory standards or industry best practices. If new privacy regulations emerge that restrict access to PII, update policies to ensure Copilot’s access to such data is aligned, maintaining compliance.
Continuous monitoring prevents “security drift,” where security controls diverge from policy requirements, creating unseen risks. By actively assessing, detecting anomalies, and adapting policies, helps to ensure Copilot’s deployment remains secure, compliant, and resilient.
Building Trust Through Data Security
Securing your data builds trust—not only in your Copilot deployment but also among stakeholders, customers, and regulators who expect responsible AI adoption. Data security requires a continuous, coordinated effort between business and technology teams to manage risk and ensure compliance. As organizations further adopt Copilot, maintaining rigorous data security practices will be essential to harnessing the full potential of GenAI with confidence and trust.
In the final installment of this series, I’ll share my insights on the third critical step: Optimizing Your Data for sustained AI success. We’ll look at proven practices and strategies for managing the data lifecycle; removing redundant and surplus data; and driving operational efficiency to optimally extract the full value from Microsoft Copilot and other GenAI solutions.
What are your Copilot or GenAI adoption experiences? If you have useful feedback, constructive critique or new insights, I welcome your thoughts!
Please join me as I share more insights surrounding this journey for ensuring a successful and secure GenAI adoption!
#Copilot #GenAI #DataRiskAssessment #Cybersecurity #Privacy #NVISIONx