AI Copilot Security
Security considerations for AI-powered copilot tools — coding assistants, productivity AI, and enterprise AI assistants — that are integrated into sensitive business workflows.
What Is an AI Copilot?
An AI copilot is an AI assistant integrated into a tool or workflow to help users complete tasks faster. Microsoft Copilot (in Office 365), GitHub Copilot (for coding), Salesforce Einstein, and Google Gemini in Workspace are all examples. They share a common characteristic: they have access to organisational data — emails, documents, code, calendars — to generate contextually relevant responses.
This data access is what makes copilots powerful — and what makes their security posture critical.
Key Security Risks of AI Copilots
Data oversharing: An AI copilot with broad permissions can surface sensitive documents in response to general queries. If your M365 Copilot can access all SharePoint sites, any user query may inadvertently expose confidential HR records, financial data, or IP.
Prompt injection via documents: If a copilot processes documents or emails, attackers can embed instructions in those documents to manipulate the AI's behaviour — redirecting sensitive outputs or exfiltrating data.
Over-privileged access: Many copilots are provisioned with broad access to maximise functionality. This violates the principle of least privilege.
Code quality and security: AI-generated code often contains vulnerabilities — hardcoded credentials, SQL injection risks, insecure dependencies. Code generated by copilots must be reviewed.
Data exfiltration via AI outputs: AI-summarised content can condense large amounts of sensitive information and make it easier to exfiltrate in a single query.
Securing Microsoft Copilot for M365
Before deploying Microsoft Copilot:
- Audit SharePoint permissions: Remove broad sharing, apply sensitivity labels, ensure users only have access to what they need
- Review OneDrive sharing: Legacy "anyone with the link" shares become discoverable by Copilot
- Apply Microsoft Purview information protection: Label and protect sensitive content before Copilot is enabled
- Use restricted search and role scoping: Limit which data sources Copilot can index per user role
- Monitor Copilot audit logs: Review what data is being accessed and summarised
Securing GitHub Copilot
- Enable code secret scanning to prevent Copilot from generating or completing code with hardcoded secrets
- Review AI-generated code for security vulnerabilities before committing
- Use Copilot in organisations (not personal accounts) to maintain audit trails
- Disable Copilot on repositories containing highly sensitive IP if risk warrants it