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07/06/2026 8:59 pm
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
- Separate code scanning from access governance Keep SAST, code review, and AI-assisted triage in the engineering pipeline, but manage agent authentication, authorisation, and audit logging in a distinct identity stack.
- Inventory AI agents as non-human identities Record every production agent, service account, and token that can reach enterprise systems, then assign owners, approval paths, and deprovisioning criteria.
- Constrain MCP tool access explicitly Treat MCP endpoints as privileged tools, not neutral utilities, and restrict which agents can request scans, retrieve findings, or trigger downstream actions.
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?
Explore further
08/06/2026 8:44 am
Code security and identity security are different control domains, and AI work has made that boundary visible. Static analysis can reduce defects in AI-generated code, but it cannot authorise the entity that runs the code or the resources it can touch. That distinction matters because many programmes now over-index on code review while leaving agent identity untreated. Practitioners should treat code scanning as one layer and identity governance as another.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to SailPoint research.
A question worth separating out:
Q: What is the difference between AI agent security and application security?
A: Application security focuses on the safety of the code and its execution paths. AI agent security also includes who the agent is, what it can access, how it is authorised, and how its access is revoked. In production, both disciplines are required because secure software does not automatically produce secure identity behaviour.
👉 Read our full editorial: AI agent security still needs identity controls beyond code scanning
