Generative AI safety: are policy and filtering keeping up?

TL;DR: Generative AI safety breaks down when employees use approved and unapproved AI tools through unverified domains, creating blind spots for data exposure and unauthorized access, according to DNSFilter. Policy alone is not enough because enforcement has to happen at the network layer where access, visibility, and review can be controlled.

Editorial analysis by NHI Mgmt Group, based on content published by DNSFilter: “Generative AI Safety: How Policy and Filtering Keep AI Use Secure”.

Key questions

Q: What breaks when generative AI policy is not enforced technically?

A: A policy that is not enforced technically leaves users free to reach unapproved AI services, which means shadow AI, uncontrolled data sharing, and inconsistent compliance become operational realities.

Q: Why do unapproved AI tools create more risk than traditional software use?

A: Unapproved AI tools can process user input in ways traditional applications do not, including storing prompts, retaining outputs, or reusing data to improve services.

Q: How do security teams know whether AI access is actually working safely?

A: Look for three signals: complete discovery of the AI estate, clear mapping of source data to each system, and logs that prove what was accessed and why.

Practitioner guidance

• Define an approved AI service list Document which generative AI tools are permitted, which data classes may be shared, and which use cases are prohibited so users have an explicit boundary for daily work.
• Enforce access with DNS filtering Block resolution to unapproved, high-risk, or spoofed AI domains so policy decisions are enforced before a connection is established.
• Extend controls to remote users Apply the same AI access rules on managed and off-network devices so hybrid work does not create a policy gap.

Bottom line: Generative AI safety depends on controlling which services users can reach, not only on telling them what they may use.

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