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06/09/2026 5:18 pm
TL;DR: Shadow AI creates three distinct risks: unauthorized AI services can exfiltrate sensitive data, employees can paste confidential material into public tools, and malware can use AI-enabled command-and-control channels, according to CyberFOX. DNS filtering adds visibility and enforcement at the connection layer, but governance still needs approved-tool inventory, policy, and monitoring.
NHIMG editorial — based on content published by CyberFOX: DNS filters can stop AI threats before they spread
By the numbers:
- 38% of employees say they share sensitive work info with AI tools without permission.
Questions worth separating out
Q: What breaks when shadow AI is not included in identity governance?
A: When shadow AI is excluded, the organisation loses discovery, ownership, and enforcement at the same time.
Q: Why do AI agents increase non-human identity risk?
A: AI agents increase non-human identity risk because they can execute many actions quickly once they inherit a credential or tool permission.
Q: How can security teams decide whether DNS filtering is enough for AI governance?
A: DNS filtering is enough only as an enforcement and visibility layer, not as a complete governance model.
Practitioner guidance
- Inventory every AI service and plugin Build a live register of approved and unapproved AI tools, including browser-based services, embedded copilots, and developer assistants, so policy starts from observed use rather than procurement records.
- Block unauthorized AI destinations at DNS Use DNS policy to stop connections to unknown or high-risk AI services, while allowing only destinations that have been reviewed for data handling, geography, and logging expectations.
- Treat AI integrations as NHI-bearing pathways Review tokens, API keys, and delegated access used by AI tools with the same scrutiny applied to service accounts, including scope, revocation, and downstream trust chains.
What's in the full article
CyberFOX's full article covers the operational detail this post intentionally leaves for the source:
- Specific DNS filtering workflows for identifying shadow AI destinations and blocking unknown services.
- Practical examples of how AI tools leak data through unsanctioned prompts, plugins, and external connections.
- The article's guidance on balancing user productivity with policy enforcement and approved-tool access.
- Operational examples of why AI-assisted malware still depends on external command-and-control lookups.
👉 Read CyberFOX's analysis of DNS filtering for shadow AI risk →
Shadow AI and DNS filtering: are your controls keeping up?
Explore further
06/09/2026 5:19 pm
Shadow AI is becoming an identity governance problem, not just a content filtering problem. When users and teams adopt AI services without review, the organisation is really allowing unmanaged non-human identities to reach external systems. That means secret handling, delegation scope, and outbound trust all become part of the governance question. The control lesson is straightforward: if the AI connection is not inventoried, it is not governed.
A question worth separating out:
Q: What do organisations get wrong about employee use of public AI tools?
A: The most common mistake is assuming the risk begins and ends with the app itself. In reality, the exposure occurs when employees paste data into prompts, so the real control point is the combination of user behaviour, approved tool access, and data classification.
👉 Read our full editorial: DNS filtering for shadow AI: stopping data leaks and command links
