The checklist translates key findings from Akamai’s State of the Internet (SOTI) Security report, Enterprise AI Usage Risk Report 2026.
Key Takeaways
-
Unmonitored AI use creates critical enterprise visibility gaps.
-
Unsanctioned tools, shadow AI, and personal account crossovers expand attack surfaces and expose proprietary enterprise data.
-
Data leaks through AI interactions threaten regulatory compliance.
-
Sensitive information exposed via prompts, file uploads, and copy/paste actions requires direct monitoring and dedicated DLP controls.
-
Autonomous AI agents and extensions introduce unmanaged risk.
-
Uninventoried agents and risky extension permissions can grant excessive access to sensitive corporate resources.
-
Dynamic risk management enables safe enterprise AI adoption.
-
Enforcing real-time visibility and strict identity controls balances continuous oversight with business innovation.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
Blanket restrictions hinder innovation, whereas securing AI adoption effectively requires a dynamic, continuous approach to risk management.
Security teams should monitor AI application use across browsers, AI browsers, desktop apps, IDEs, and extensions.
Organizations can detect unsanctioned AI activity, maintain approved and restricted lists, identify personal account crossover, and establish governance policies.
Security teams must monitor prompts, file uploads, copy/paste activity, and general interactions involving sensitive or proprietary data.
Teams must inventory these assets, monitor the data that agents can access, govern autonomous actions, and detect risky extension permissions.
Organizations should enforce SSO and MFA, track personal-to-corporate account crossover, detect session hijacking, and continuously monitor access for anomalous behavior.
The goal is to establish proactive visibility and governance over AI applications to balance safe innovation with comprehensive oversight.