Security inspection that happens while an AI coding agent is generating code rather than only after the code is committed. It is designed to catch risky output before it enters the review queue or expands into a larger remediation backlog.
Expanded Definition
Agent-time scanning is a security control pattern for agentic software development workflows, where code is inspected while an AI coding agent is still producing output rather than after the work is committed. The goal is to intercept insecure dependencies, secret exposure, policy violations, and unsafe patterns before they become part of a pull request or downstream build artifact. That timing matters because an autonomous agent can generate many lines quickly, and post-commit review alone often turns a preventable issue into a larger remediation task.
In NHI Management Group’s view, the concept sits at the intersection of secure software delivery and agent governance. It is distinct from ordinary pre-merge scanning because the inspection is synchronized with the agent’s execution loop, not just with developer handoff. Industry usage is still evolving, and definitions vary across vendors, but the security intent is consistent: reduce the blast radius of agent-generated code before humans spend review effort on avoidable defects. The term aligns closely with guidance emerging in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework.
The most common misapplication is treating agent-time scanning as a cosmetic CI gate, which occurs when teams run it only after the agent has finished and the code is already queued for review.
Examples and Use Cases
Implementing agent-time scanning rigorously often introduces latency and workflow complexity, requiring organisations to weigh faster remediation against potential friction in the agent’s generation loop.
- An AI coding agent proposes a new API client, and the scanner blocks the output when it detects a hard-coded token or an unsafe logging pattern before the snippet is saved.
- During test generation, the scanner flags a dependency suggestion that would introduce a known vulnerable package, allowing the agent to regenerate a safer alternative.
- A code assistant creates infrastructure-as-code resources, and the scanner intercepts overly broad cloud permissions before the draft reaches a pull request.
- In an agentic pair-programming workflow, the scanner checks each file chunk for secrets, insecure deserialisation, and dangerous shell execution as the agent writes it, not after checkout.
- Security teams use the concept alongside the OWASP Top 10 for Agentic Applications 2026 to prioritise what the scanner should catch first, especially where agent output can reach production quickly.
Why It Matters for Security Teams
Agent-time scanning reduces the chance that AI-generated defects, secrets, or policy violations will survive long enough to be reviewed as normal code. That matters because once an autonomous coding agent has emitted unsafe material, the problem is no longer just code quality. It becomes a governance issue involving approval paths, auditability, and how much trust is placed in agent output. Security teams also need to account for adversarial behaviour, prompt manipulation, and tool misuse, which are increasingly addressed in sources such as the MITRE ATLAS adversarial AI threat matrix and the CSA MAESTRO agentic AI threat modeling framework.
For identity and access teams, the concept also reinforces the need to control which agents can generate code, access repositories, or invoke deployment tools. That makes the practice relevant to NHI governance when the agent itself operates as a non-human identity with credentials and delegated authority. Organisations typically encounter the operational cost of weak agent-time scanning only after unsafe code, leaked secrets, or excessive permissions reach the review queue, at which point the control becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Defines agentic AI risks and controls relevant to scanning code during agent execution. | |
| NIST AI RMF | Frames governance and risk management for AI systems that produce code and tool actions. | |
| NIST CSF 2.0 | PR.DS-6 | Supports data protection and integrity controls that agent-time scanning helps enforce. |
| OWASP Non-Human Identity Top 10 | Covers non-human identity governance where coding agents act with credentials and access. | |
| CSA MAESTRO | Addresses threat modeling for agentic systems that need runtime inspection and guardrails. |
Assign ownership and monitor AI code generation risk across the full lifecycle, not only at commit time.
Related resources from NHI Mgmt Group
- What is the difference between scanning AI-generated code and governing AI agent identity?
- How should security teams combine agentless and agent-based Kubernetes scanning?
- When should secret scanning happen in an AI agent workflow?
- What is the difference between secret scanning and agent runtime control?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
