Topic starter
25/10/2026 11:00 am
TL;DR: Generative AI safety depends on controlling which services users can reach, DNSFilter argues, not just telling them what they may use. Access controls and visibility at the network layer are what turn policy into something security teams can validate and govern.
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: 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.
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.
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.
Glossary terms: DNS Filtering, Generative AI Security Risk, Shadow AI, Protective DNS
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:14 pm
Policy is only governance theatre until it has an enforcement plane. Generative AI use becomes risky the moment employees can reach unapproved services through ordinary web access while policy lives only in documents. DNS-level controls turn policy into an operational control by making allow or block decisions at the point of resolution, which is where many shadow AI behaviours actually begin. Practitioners should treat this as an access-governance problem, not just a user-awareness problem.
A few things that frame the scale:
- According to Gartner, worldwide spending on generative AI is set to reach $644 billion in 2025, a nearly 77% year-over-year increase.
A question worth separating out:
Q: How should organisations govern generative AI use across hybrid workforces?
A: They should apply the same approval rules, filtering controls, and monitoring standards on office and remote networks so location does not change the security posture. Hybrid work increases the chance that users will bypass informal controls, so governance has to travel with the user. Consistent enforcement is the only reliable way to keep policy aligned with real usage.
👉 Read our full editorial: Policy and DNS filtering are central to safer generative AI use
This post was modified 7 days ago by NHI Mgmt Group