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07/09/2026 4:38 pm
TL;DR: AI adoption has pushed enterprise risk beyond model quality into access governance, with attackers able to exploit overprivileged agents, shadow AI, and exposed model infrastructure, according to Zero Networks. The underlying problem is that existing network and identity controls were not built to contain AI tools, agents, and workflows by design.
NHIMG editorial — based on content published by Zero Networks: AI Security Best Practices: How to Close AI Vulnerabilities with a Containment Architecture
By the numbers:
- AI-driven attacks have increased 56% in the last year, according to Zero Networks.
- Security breaches involving AI tools are surging even faster, rising 61% year-over-year, according to Zero Networks.
- Nearly 90% of organizations use AI in at least one business function, according to Zero Networks.
Questions worth separating out
Q: How should security teams implement AI containment without blocking business use?
A: Start by treating AI tools and agents as governed identities, not just applications.
Q: Why do autonomous agents create more lateral movement risk?
A: Autonomous agents often need broad, chained access across APIs, data stores, and external services, so a compromised identity can move through multiple systems quickly.
Q: What are the signs that shadow AI is creating governance and compliance gaps?
A: Common signs include limited visibility into who is using AI tools, no reliable audit trail for prompts and outputs, unclear data residency, and inconsistent handling of sensitive information.
Practitioner guidance
- Map AI identities and connections in real time Build a live inventory of AI tools, agents, SaaS destinations, and model infrastructure so you can see what is active, what it connects to, and what sits outside IT oversight.
- Constrain AI agents to least agency Apply identity-based access controls so each AI agent can only reach explicitly authorized systems, approved resources, and task-scoped connections.
- Isolate model infrastructure with segmentation Segment model environments so only authorized workloads and identities can reach them, reducing the chance of tampering, extraction, or prompt abuse.
What's in the full article
Zero Networks' full article covers the operational detail this post intentionally leaves for the source:
- The specific AI segmentation pattern used to constrain lateral movement across tools, agents, and model infrastructure.
- The article's breakdown of least agency enforcement for AI agents and how it maps to identity-based access controls.
- The deterministic guardrail approach for policy creation and enforcement as AI adoption scales.
- The practical network-visibility questions teams should use to inventory SaaS AI destinations and embedded workflows.
👉 Read Zero Networks' analysis of AI security best practices and containment architecture →
AI agent access, shadow AI, and model exposure: are controls keeping up?
Explore further
07/09/2026 4:38 pm
AI security is becoming an identity containment problem, not a model quality problem. The article’s strongest insight is that AI risk expands when tools, agents, and model connections are allowed to behave like loosely governed internal identities. That shifts the centre of gravity from model tuning to access boundaries, blast radius, and lifecycle governance. For practitioners, the control question is no longer only what the AI can produce, but what it can reach.
A few things that frame the scale:
- 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared with nearly 1 in 4 for securing human identities.
A question worth separating out:
Q: Should organisations prioritise microsegmentation or visibility first for AI security?
A: They should treat them as linked controls, but visibility comes first operationally because you cannot segment what you have not identified. Once the AI inventory is current, microsegmentation becomes the enforcement layer that limits reach and contains compromise. Discovery without containment is incomplete, but containment without discovery is blind.
👉 Read our full editorial: AI adoption is widening the attack surface around agents and models
