State of the Internet: Enterprise AI Usage Risk Report 2026

Join Akamai security experts as they analyze enterprise AI adoption and security risks using real-world enterprise telemetry rather than traditional survey data. The panel highlights how AI power users and personal identity usage—which accounts for 47% of enterprise AI conversations—create significant shadow AI blind spots as employees connect personal accounts, personal clouds, and unmanaged tenants to corporate workflows. The discussion also details emerging risks like "vibe hacking" and autonomous AI agents, demonstrating why legacy security frameworks such as Data Loss Prevention (DLP) and User and Entity Behavior Analytics (UEBA) fall short against high-velocity agentic behaviors. To address these challenges without blocking productivity, the experts recommend moving security enforcement directly to the device edge, controlling tenant access, and embedding zero trust guardrails at the point of interaction. What you'll learn: -How enterprise AI power users and personal identity usage generate massive shadow AI blind spots for security teams. -Why traditional DLP and UEBA controls fail to protect against autonomous agents and local file modifications. -Practical mitigation strategies for CISOs to implement zero trust controls at the prompt level and device edge without compromising employee productivity.