The AI Security Framework
1. Governance
Know which AI tools are approved, who owns them, what data they can use and how higher-risk use cases are reviewed.
Read AI governance guidance2. Data Protection
Reduce accidental disclosure by defining acceptable AI use and controlling where confidential information can be sent.
Read about shadow AI and data leakage3. Identity and Access
Treat AI agents and integrations as identities with owners, permissions, credentials and auditability.
Read AI agent identity guidance4. Microsoft Copilot
Review Microsoft 365 identity, permissions and oversharing before expanding AI access across collaboration data.
Read the Copilot security guide5. AI Agents
Control tools that can take actions across business systems with least privilege, approval points and monitoring.
Read how to secure AI agents6. AI Automation
Keep workflows narrow, test failure modes and monitor integrations that connect multiple business applications.
Read the SMB automation security guideWhat businesses should review before deploying AI
Start with the systems AI will touch. Review identity and administrative accounts, application permissions, data classification, cloud sharing, API access, third-party vendors, logging, incident-response procedures and employee guidance.
A useful AI security review should answer practical questions: Who owns the tool? What can it access? Can it write or delete data? What happens if credentials are compromised? Can actions be traced? How quickly can access be revoked?
Common AI security risks
AI-related incidents often begin with familiar security weaknesses rather than a completely new class of threat. Weak identities, overshared files, exposed API keys, excessive OAuth permissions, poor vendor review and unclear data-handling rules become more consequential when AI increases the speed and scale of access.
For a broader overview, see AI Security Risks for Canadian Businesses.
AI Security Content Cluster
AI Security Risks for Canadian Businesses
Understand the main security questions that appear when AI is introduced into normal business workflows.
Read articleAI Governance and Cybersecurity
Build an approved-use process covering ownership, vendors, permissions, data and incident response.
Read articleMicrosoft Copilot Security
Prepare Microsoft 365 permissions and information governance before broad Copilot adoption.
Read articleShadow AI and Data Leakage
Control unapproved AI use without driving employees further outside the organization's visibility.
Read articleSecuring AI Agents
Protect agents that can access business systems and perform actions on behalf of users.
Read articleAI Agent Identity and Access
Use dedicated identities, least privilege, credential protection and strong offboarding.
Read articleSecuring AI Automation for SMBs
Apply practical security controls to workflows that connect multiple applications and data sources.
Read articleHow The Cyber Arm approaches AI security
AI security should fit into the broader cybersecurity program rather than become a separate collection of tools. Identity, endpoint security, cloud configuration, vulnerability management, monitoring, incident response and employee awareness still matter. AI changes how those controls are applied and how quickly weaknesses can be amplified.
If your main question is how to select, design and implement AI use cases rather than how to secure them, see AI Capital Advisory for AI strategy and implementation guidance.
Explore the Cybersecurity Knowledge Center, Managed Detection and Response, Managed SIEM, and Vulnerability Assessment.
Need an AI security review?
Start with your current identities, applications, data and AI integrations. We can help identify practical gaps before AI workflows are expanded further.
Book a Cybersecurity Gap Assessment or contact The Cyber Arm.