Why do GenAI tools create governance gaps that traditional network security controls often miss?

Why This Matters for Security Teams

GenAI tools are not just another application category. They can accept untrusted prompts, retrieve external content, call tools, and return outputs that influence downstream decisions, all within traffic patterns that look ordinary to firewalls, proxies, and DLP engines. That creates a governance gap: the network can see packets, but it cannot reliably see intent, model output risk, or whether a user has routed sensitive data into an unmanaged AI service.

This is why NHIMG treats GenAI as an identity and control problem as much as a transport problem. Traditional controls may still matter, but they are insufficient when the real exposure is prompt injection, shadow AI use, or hidden data exfiltration through model interactions. NIST’s NIST AI 600-1 GenAI Profile and NIST Cybersecurity Framework 2.0 both reinforce the need to govern AI risk with context, not just perimeter enforcement. In practice, many security teams encounter GenAI misuse only after data has already been copied into an external model, rather than through intentional approval workflows.

How It Works in Practice

Effective genai governance starts by treating the interaction as the control point. The question is not only “what host made the connection,” but “what was the user or agent trying to do, what data was sent, what model was used, and what came back.” That is why current guidance suggests combining inline inspection, policy enforcement, and identity-aware access decisions. Network controls can still block destinations, but they need help from application context and user context to identify risky model usage.

Practitioners usually build this in layers. First, they classify sanctioned and unsanctioned GenAI services so security teams can distinguish approved business use from shadow AI. Second, they inspect prompts and responses for sensitive content, malicious instructions, and policy violations. Third, they connect access decisions to identity, posture, and data classification so a higher-risk request can be challenged or denied in real time. This is where NIST AI 600-1 GenAI Profile and NIST SP 800-207 Zero Trust Architecture are useful: they push teams toward continuous verification and contextual policy, not static trust.

NHIMG research on The State of Non-Human Identity Security shows how visibility gaps and over-privileged access continue to weaken controls around machine access. That same pattern appears in GenAI when model credentials, API keys, or connected tools are not tightly governed. The LLMjacking research also underscores how quickly exposed credentials can be abused once attackers find them. These controls tend to break down in BYOD and browser-based AI use because the traffic exits through unmanaged endpoints, making policy enforcement inconsistent.

Common Variations and Edge Cases

Tighter GenAI inspection often increases latency, privacy review burden, and operational friction, so organisations have to balance visibility against developer speed and user adoption. There is no universal standard for this yet, especially for how deeply prompts and outputs should be inspected in regulated or multilingual environments.

One common edge case is embedded AI inside SaaS products. In those cases, the network team may never see a distinct “AI app” at all, even though users are sending regulated data to a model behind the scenes. Another is agentic workflows, where an AI system can chain tool calls, retrieve documents, and post results without a human reading every step. That makes simple allowlists less effective, because risk emerges from the sequence of actions rather than a single destination. NHIMG’s Top 10 NHI Issues and Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs are useful here because GenAI services often inherit the same lifecycle failures as other machine identities, especially around secret rotation, scope control, and revocation.

The practical takeaway is that traditional network security still provides a baseline, but GenAI governance needs context-aware controls, stronger identity signals, and policy decisions made at the moment of use. Where organisations rely only on transport-layer inspection, they usually discover the gap after a prompt leak, a policy violation, or an unsafe model output has already reached production users.

Related resources from NHI Mgmt Group

• Why do AI and LLM applications create security risks that traditional tools often miss?
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• Why do ITSM tools often create identity governance gaps?
• Why do SaaS tools create governance gaps that traditional SAM misses?