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11/07/2026 1:53 pm
TL;DR: AI agents move laterally, create shadow connections, and abuse service-account or API-key access across enterprise networks faster than endpoint tools can reliably contain, according to Elisity. Identity-based microsegmentation shifts enforcement into the network data plane, but it still leaves authorised-channel abuse and discovery gaps that security teams must plan around.
NHIMG editorial — based on content published by Elisity: AI Agent Network Security, Why Microsegmentation Is the Missing Layer
By the numbers:
- 48% of security professionals rank agentic AI as the #1 attack vector.
- 89% year-over-year surge in AI-enabled attacks.
- Only 14.4% of AI agents deploy with full security approval.
Questions worth separating out
Q: How should security teams contain AI agents that can move laterally across the network?
A: Security teams should treat AI agents as network-reachable identities and constrain them with identity-based microsegmentation.
Q: Why do AI agents complicate existing IAM and zero trust assumptions?
A: AI agents complicate IAM and zero trust because they can hold legitimate credentials while still behaving unpredictably at runtime.
Q: What breaks when shadow AI is not discovered before policy design?
A: When shadow AI is not discovered first, policy is built on an incomplete inventory and cannot reliably enforce least privilege.
Practitioner guidance
- Inventory all sanctioned and shadow AI agents Build a living register of agents, the identities they use, and the network paths they open.
- Attach network policy to agent identity Replace broad zone-based assumptions with identity-aware rules that define exact destinations, protocols, and peer relationships.
- Separate enforcement from the endpoint Prefer agentless controls in the network data plane so the AI system cannot disable the mechanism governing its movement.
What's in the full article
Elisity's full post covers the operational detail this analysis intentionally leaves for the source:
- Network-level comparison of agentless microsegmentation versus endpoint-based enforcement for AI agent containment
- The five threat vectors the vendor maps to agentic AI, including lateral movement, shadow AI, and NHI proliferation
- Implementation guidance for identity-aware policy design across sanctioned and shadow agents
- The article's own view of how microsegmentation fits alongside Zero Trust and NIST-aligned controls
👉 Read Elisity's analysis of AI agent network security and microsegmentation →
AI agent containment at the network layer: is microsegmentation enough?
Explore further
11/07/2026 2:05 pm
Identity-based network containment is becoming a first-class control for AI agent governance. Endpoint and IAM controls still matter, but they do not solve the problem of agent-driven east-west movement once an AI system is active on the network. Microsegmentation moves enforcement to a place the agent cannot easily observe or alter, which changes the containment model for both sanctioned and shadow deployments. Practitioners should treat network reachability as a governance object, not just a transport concern.
A few things that frame the scale:
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, which keeps the human-side control gap wide open.
A question worth separating out:
Q: Who is accountable when an AI agent causes network exposure or data movement?
A: Accountability should sit with the team that approved the identity, scope, and containment model for the agent, not with the agent itself. That means IAM, security architecture, and platform owners all need a shared control definition for reachability, lifecycle, and response. Without clear ownership, containment gaps persist even when the tooling exists.
👉 Read our full editorial: AI agent network security needs identity-based microsegmentation
