Map and govern what AI agents can access across SaaS systems

Enterprises increasingly deploy autonomous AI agents that stitch together multiple SaaS products and internal services, creating cross-system access patterns that are invisible to traditional IAM, DLP, and SIEM tools; 400,000 enterprise IT organizations face this new surface area and the attendant compliance, data exfiltration, and operational-resilience risks. Security, compliance, platform engineering, and developer productivity teams are the primary stakeholders because they must both enable productive agent behavior and prevent agents from creating lateral movement, privacy violations, or costly outages. You could build a platform that discovers and maps agent identities, capabilities, and the SaaS endpoints they touch, enforces policy-as-code at runtime, and simulates agent runs to verify least-privilege before deployment; the product would include a library of connectors (Okta, Google Workspace, Slack, Salesforce, AWS), an agent-aware policy language, realtime enforcement hooks, and an audit trail suitable for SOC workflows. Offer deployment options (SaaS control plane plus on-prem enforcement) and a developer-first SDK with pre-built templates to target the $120K ACV segment of mid-to-large enterprises. This is a $48.0B addressable market with high momentum because autonomous agents, SaaS composability, and the shift from post-hoc audits to live enforcement are converging—buyers and investors are prioritizing runtime governance now (market score 92, revenue potential 88). To stand out in a medium-competition field you must be agent-first rather than treating agents as another identity type, deliver low-latency real-time enforcement and realistic simulations, and build deep, maintainable connectors; strengths are clear ROI and a timely value proposition, while challenges include complex integrations, evolving SaaS APIs, and long enterprise sales cycles.