Agentic Radar (Agentic Workflow Security Scanner) is a scanner for agentic AI workflows that looks for security weaknesses in how agents are connected, instructed and allowed to act. It helps identify overbroad tool access, unsafe handoffs and exposed secrets before deployment. Use it to review workflows, then validate findings against actual permissions and runtime behaviour.
What Agentic Radar Checks in an Agentic Workflow
Agentic Radar is best understood as a pre-deployment scanner for the security posture of an agentic workflow. It focuses on how agents are wired together, what instructions they receive, and what actions they are permitted to take, because those design choices shape the workflow’s real attack surface.
The tool is most useful when a workflow looks fine on paper but hides risky combinations in practice, such as broad tool reach, brittle delegation chains, or secrets that can be exposed through prompts, logs, context, or connected systems. Its value is not simply finding flaws, but surfacing where autonomy and authority have been assembled in ways that could later be abused.
Why Agentic Workflow Scanning Matters
Agentic workflows introduce a different security problem from ordinary application flows: the system may be technically functional while still being unsafe to operate. A scanner in this category helps identify where connected agents can overreach, where handoffs can be manipulated, and where instructions can push an agent toward unintended execution paths.
This matters because workflow design can create hidden trust chains. An agent that can call tools, pass context, or accept instructions from other components may inherit more effective power than the owner intended. That is especially important when the workflow spans multiple models, orchestrators, plugins, or external services.
Security review should therefore treat the workflow as an authorization surface, not just an automation diagram. The point is to understand where access, instruction, and execution authority converge, and whether that combination matches the intended operating model.
What Findings Usually Mean Operationally
Findings from an agentic workflow scanner usually fall into a few practical buckets: excessive tool scope, weak separation between steps, unsafe prompt or handoff handling, and secret exposure risk. Each one points to a different failure mode, but all of them can expand blast radius if the workflow is later compromised or misused.
Overbroad tool access means an agent can do more than the task actually requires. Unsafe handoffs mean one agent can pass malformed, misleading, or sensitive context to another agent or tool without sufficient validation. Exposed secrets indicate that credentials, tokens, or other sensitive material may be reachable where they should not be, especially in prompts, memory, logs, or connector configuration.
Use the scanner as a design review aid, then compare its output with the actual permissions granted at runtime. A workflow is only as safe as the live controls around it, not the intended controls in the architecture diagram.
How to Read Scanner Results in Context
Scanner output should be interpreted as a candidate finding set, not a final verdict. Some issues will be structural, such as a workflow that is too permissive by design. Others will depend on deployment context, such as whether a tool is reachable from production, whether a secret is real or stubbed, or whether a handoff is protected by an approval gate.
The most important question is whether the reported weakness can actually be exercised in the deployed environment. If a finding maps to a permission, secret, or action path that exists in production, it deserves immediate attention. If it only exists in a lab or template workflow, it still signals a pattern to prevent from spreading into live systems.
For deeper context on workflow-level threat modelling, see the Agentic AI Security Guide, which maps common agent threats to controls across inputs, memory, tools, orchestration and identity. For a broader view of how autonomy changes the security model, the AI Agents vs Agentic AI guide helps frame where risk rises as systems move from simple assistance to delegated action.
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 addresses the attack and risk surface, while NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agentic workflow scanners look for overbroad authority and unsafe delegated action. |
| ASI02 — Tool Misuse | The term centers on unsafe tool access and risky tool invocation paths in agent workflows. | |
| ASI07 — Insecure Inter-Agent Communication | Unsafe handoffs between agents are a core weakness this scanner is meant to surface. | |
| Recommendation — Enforce per-action authorization and strip unused agent privileges. Constrain tool access to task-scoped, policy-approved actions. Validate and bound inter-agent messages before they can trigger actions. | ||
| NIST AI RMF | AI Risk Management Framework | The term concerns governance and risk evaluation of agentic AI workflows before deployment. |
| Recommendation — Apply AI risk governance to assess autonomy, authority and operational controls. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Overbroad tool access is fundamentally a least-privilege problem in agent workflows. |
| Recommendation — Limit each agent to the minimum permissions needed for its task. | ||
Practitioner Guidance
Why practitioners should care: This kind of scanner is most valuable when it is tied to a real review process, not used as a generic green-light badge. It should help you decide whether the workflow’s authority, tool reach, and secret handling are acceptable for the task the agent is meant to perform.
What to watch for: The highest-signal results are usually the ones that combine broad permissions with weak control boundaries, because those conditions can turn a small prompt or handoff issue into a material security event. If the scanner repeatedly points to the same access pattern, treat that as a design problem rather than a one-off finding.
Practitioner takeaway: Validate scanner findings against live permissions and runtime behaviour before deployment, then reduce authority wherever the workflow depends on trust that it does not strictly need.
For implementation guidance on least privilege and delegated action, AI Agent Authorisation Guide is the most direct internal reference. If you need operational visibility after deployment, the AI Agent Observability, Audit and Incident Response Guide covers the logging and response signals that help confirm whether a risky workflow has actually been abused.
Related resources from NHI Mgmt Group
- Guide: AI Red Teaming Guide
- How do I know if an agentic security workflow is actually safe?
- Who is accountable when an agentic security workflow closes the wrong case?
- What should security teams review first after an agentic workflow compromise?
- How should security teams govern AI-assisted investigations when connecting a SIEM to an external agentic workflow platform?