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14/09/2026 9:44 am
TL;DR: Shadow AI is increasingly embedded in endpoint processes, extensions, local models, and MCP-connected tools, so firewalls and EDR can see fragments of activity without identifying what is actually installed or in use, according to Straikerai. The governance problem is inventory and classification, not traffic alone, because security teams need confidence-weighted discovery before they can enforce policy.
NHIMG editorial — based on content published by Straikerai: Why Firewalls and EDR Still Miss Shadow AI
By the numbers:
- While 71% of IT teams have been advised on AI agent data access, only 47% of compliance teams, 39% of legal teams, and 34% of executives have the same visibility.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams govern shadow AI when firewalls and EDR only show fragments?
A: Teams should treat shadow AI as an endpoint inventory problem first and a policy problem second.
Q: Why does shadow AI create risk even when there is no obvious external traffic?
A: Because many AI tools now run locally, inside IDE extensions, containers, or MCP-connected workflows that do not depend on a visible outbound session.
Q: What are the signs that shadow AI controls are failing in practice?
A: Shadow AI controls are failing when sensitive data moves through copy-paste, uploads, or API calls outside sanctioned workflows, yet the organisation has no alerting or enforcement at the point of transfer.
Practitioner guidance
- Implement endpoint-first AI discovery Correlate applications, extensions, packages, processes, local services, containers, and signing data so the inventory captures the AI tool itself, not just its network traffic.
- Preserve confidence levels in detection outputs Separate strong evidence such as a signed application bundle from weak evidence such as a leftover configuration directory, and use those distinctions in triage and approval workflows.
- Inventory local models and MCP servers Add locally hosted models, MCP servers, and AI agents running inside containers or IDEs to the same governance register used for other machine-access paths.
What's in the full article
Straikerai's full blog covers the operational detail this post intentionally leaves for the source:
- Endpoint scanning logic for AI applications, extensions, and local model artefacts
- How confidence-weighted detection distinguishes active tools from leftover files and generic processes
- Examples of local signal processing for usage and credential-pattern detection without exporting prompts
- Operational discussion of how Discover AI, Ascend AI, and Defend AI fit into the broader AI security stack
👉 Read Straikerai's analysis of why firewalls and EDR miss shadow AI →
Shadow AI discovery: what firewall and EDR visibility still miss?
Explore further
14/09/2026 9:45 am
Shadow AI discovery is now an endpoint inventory discipline, not a firewall problem. The article shows that perimeter tools can only confirm that AI traffic exists, not which tool, extension, runtime, or local model created it. That distinction matters because governance decisions depend on ownership, approval status, and runtime context. In identity terms, you cannot govern what you cannot name, and AI tooling increasingly behaves like a distributed set of non-human identities. Practitioners should treat discovery as a classification problem before they treat it as a blocking problem.
A question worth separating out:
Q: Should organisations monitor AI usage by collecting employee prompts?
A: Usually not as a default control. Prompt collection can create privacy, legal, and security exposure because prompts often contain source code, customer data, credentials, or personal information. A better pattern is to process sensitive signals locally and report only the risk indicator, such as a detected credential pattern or usage count.
👉 Read our full editorial: Shadow AI is an endpoint inventory problem firewalls cannot solve
