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AI agent security and identity controls: what teams are missing


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TL;DR: Semgrep’s AI-focused SAST and MCP workflow help teams catch and triage vulnerabilities in generated code faster, but the article draws a hard line: code scanning does not authenticate agents, enforce authorization, or provide enterprise identity infrastructure. That boundary matters because production AI agents need identity controls as well as secure code review.

NHIMG editorial — based on content published by WorkOS: Semgrep for AI Agent Security: Features, Pricing, and Alternatives

Questions worth separating out

Q: How should security teams govern AI agents that can access enterprise systems?

A: Treat production AI agents as non-human identities, not just applications.

Q: Why is code scanning not enough for AI agent security?

A: Code scanning finds vulnerabilities in the software artefact, but it does not establish identity, privilege, or accountability for the runtime actor.

Q: When should organisations add identity controls to AI development pipelines?

A: They should add identity controls as soon as an AI system can authenticate to internal tools, customer environments, or third-party APIs.

Practitioner guidance

What's in the full article

WorkOS's full article covers the operational detail this post intentionally leaves for the source:

  • Semgrep feature breakdowns for AI-powered triage, automated fix suggestions, and the MCP server workflow
  • Pricing and packaging details for Community, Teams, and Enterprise tiers
  • WorkOS capability list for SSO, MFA, directory sync, RBAC, and audit logging in enterprise deployments
  • Direct comparison points between code scanning workflows and production identity requirements

👉 Read WorkOS's analysis of Semgrep for AI agent security and enterprise identity →

AI agent security and identity controls: what teams are missing?

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