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17/05/2026 9:32 am
TL;DR: AI agents often leave one trace in network traffic and another in the credentials they use, so most tools still miss the full picture of risk, according to Astrix Security. Continuous discovery only becomes useful when security teams can tie connections to the non-human identities, permissions, and owners behind them.
NHIMG editorial — what this means for NHI practitioners
Questions worth separating out
Q: How should security teams govern AI agents that use non-human identities?
A: Start by tying each agent to a named owner, a credential type, and a permission scope.
Q: What is the difference between network detection and identity-based discovery for AI agents?
A: Network detection tells you that something connected and exchanged traffic.
Q: When does shadow AI become an access governance problem?
A: Shadow AI becomes an access governance problem as soon as the agent can authenticate to enterprise systems with real credentials.
Practitioner guidance
- Implement network-to-identity correlation for agent traffic Join firewall or proxy logs to identity records so each AI agent session can be tied to an owner, credential type, and permission scope.
- Inventory unmanaged agents and MCP servers continuously Use outbound connection telemetry to detect locally run agents and undocumented MCP servers, then classify them as sanctioned, unsanctioned, or deprecated.
- Rank agent risk by reachable blast radius Prioritise agents with write access, production access, or broad SaaS permissions before reviewing low-impact read-only integrations.
Programmes that cannot connect activity, identity, and ownership will struggle to separate sanctioned automation from shadow AI?
👉 Read Astrix Security's analysis of AI agent discovery across network and identity layers →
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17/05/2026 9:33 am
Network-to-identity correlation is becoming the baseline control for AI agent governance. Security teams can no longer rely on endpoint visibility or identity inventory alone because agent activity is distributed across transport, cloud, SaaS, and DevOps layers. A control model that cannot bind traffic to the NHI behind it leaves ownership and privilege unresolved. The practical conclusion is straightforward: discovery must be correlation-first, not tool-silo-first.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), 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.
A question worth separating out:
Q: Why do AI agents complicate zero trust architecture assumptions?
A: Zero trust assumes every access request is continuously verified, but AI agents can make frequent, autonomous requests using credentials that appear legitimate. That makes ownership, context, and privilege scope just as important as authentication. Teams need continuous identity validation and least privilege for the agent itself.
👉 Read our full editorial: AI agent discovery needs both network and identity context
