AI Security Vendor Risk Report
AI adoption is accelerating faster than enterprise controls, exposing data, models, and autonomous workflows to unmanaged risk.
By Cato Networks | Executive Guide | Source: AI Security Vendor Checklist

Traditional security platforms were not built for GenAI tools, AI agents, or model-driven applications.

Security leaders now need measurable controls across AI use, AI development, and runtime operations.

The next security gap is not network visibility—it is uncontrolled AI activity across users, apps, and enterprise data flows.

Boards expect AI productivity gains, while regulators expect governance, auditability, and policy enforcement.

⚠ Within the next 12–24 months, enterprises without AI controls may face data leakage, failed audits, and disruption across cloud and business systems.

Leading programs evaluate vendors against architecture, enforcement, analytics, and compliance readiness.

  • Discover shadow AI usage
  • Block prompt injection attempts
  • Protect sensitive data flows
  • Enable real-time policy enforcement

This creates stronger resilience, faster adoption, and clearer accountability for enterprise teams.

Enterprise AI Security Exposure Assessment
Benchmark your AI security posture before vendor selection or large-scale rollout.

✔ Vendor evaluation framework
✔ AI risk gap analysis
✔ Governance readiness score
✔ Executive action roadmap
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