A SASE model is failing when teams need growing bypass lists to keep applications usable, when encrypted AI traffic looks indistinguishable from normal API traffic, and when agents operating in SaaS or on the endpoint never reach the proxy at all. Those are signs the architecture cannot observe or govern the real workflow.
When SASE starts losing visibility into how work actually happens
The clearest warning sign is that enforcement is being bypassed to preserve usability. When security teams keep adding exceptions for apps, endpoints, regions, or workflows, the model is no longer shaping behaviour, it is reacting to it. At that point, SASE is operating as a partial control plane, not a reliable policy boundary.
That usually shows up in three places. First, traffic patterns change faster than policy can keep up, especially when users move between browsers, SaaS, copilots, and local agents. Second, encrypted sessions become harder to distinguish by intent, so inspection based on destination or protocol stops adding much value. Third, the proxy or gateway is no longer in the middle of the real transaction path, which means the control may still log activity but cannot consistently govern it.
For AI-driven work, the important shift is not simply more traffic volume. It is that the workflow fragments across browser sessions, SaaS integrations, embedded assistants, and endpoint-executed actions. If your control only sees part of that chain, you can still report on network access while missing the decision points where data moves, prompts are sent, or actions are triggered.
Why bypass lists and encrypted AI traffic expose the control gap
A growing bypass list is not just an operational inconvenience. It is evidence that the policy model no longer matches the application model. Every exception creates a narrower slice of enforceable traffic, and over time the architecture starts to protect only the paths that were easiest to centralise, not the paths employees actually use.
Encrypted AI traffic creates a different problem. Many AI and API exchanges look similar on the wire, so packet-level inspection alone may not reveal whether a session is a user query, an agent-to-service call, or a data-intensive automation step. When the control cannot separate those cases, it becomes difficult to apply differentiated policy, logging, or data handling rules with confidence.
When agents operate directly in SaaS or on the endpoint, the control gap becomes structural. Those actions may originate outside the proxy model entirely, which means the boundary you are relying on is no longer the place where work is happening. In practice, that is where SASE begins to drift from a governable access layer into a monitoring layer.
What to look for in mature AI-era network governance
Mature environments do not depend on a single choke point to understand and control work. They correlate network controls with application telemetry, SaaS audit logs, endpoint events, and identity signals so they can see the full workflow, not just the last hop. That matters because AI-driven work often shifts the control question from “who reached the site” to “what action was taken, by which workflow, with which data.”
If a SASE deployment is still useful, it should be able to enforce a small number of high-value decisions consistently, such as known risky destinations, sanctioned versus unsanctioned applications, and sensitive data movement. If those decisions require a growing exception catalog, or if every new AI workflow needs a manual bypass before rollout, the architecture is lagging the business model.
NIST AI Risk Management Framework is useful here because it pushes organizations to measure whether controls still support trustworthy operation as AI use expands. For the same reason, NIST Cybersecurity Framework 2.0 helps teams judge whether governance, protection, detection, response, and recovery still line up with how work is really being delivered. Where network enforcement is part of the answer, NIST SP 800-207 Zero Trust Architecture reinforces the principle that access should be continuously evaluated rather than assumed from location or perimeter position.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF, NIST CSF 2.0 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | Govern | AI-driven work changes control trust and oversight requirements. |
| Recommendation — Assess whether controls still support trustworthy AI use as workflows and data paths expand. | ||
| NIST CSF 2.0 | GV.OV-01 — Cybersecurity Oversight | SASE drift is a governance and oversight problem over changing control coverage. |
| PR.AA-05 — Least Privilege | Bypass sprawl often reflects overly broad access paths and weak policy enforcement. | |
| Recommendation — Review whether network controls still govern the actual workflow paths used by the business. Tighten access paths so exceptions do not become standing policy. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | The question is about whether perimeter-style enforcement still matches real access paths. |
| Recommendation — Apply continuous evaluation so access decisions do not depend on being inside the proxy boundary. | ||
Practitioner Guidance
What to verify: Check whether the controls you are calling SASE still see the majority of user-initiated, SaaS-initiated, and endpoint-initiated actions that matter to the business. If the answer is no, the gap is architectural, not just tuning-related.
What to prioritise: Start with the workflows that carry sensitive data or automated action, because those are the ones most likely to break the proxy model first. Use that review to separate controls that still govern behaviour from controls that only document it.
Common mistake: Treating a larger bypass list as proof of operational maturity. In most cases it is proof that the policy boundary has lost generality and is being preserved by exceptions.
Practitioner takeaway: A SASE model is falling behind when it can still observe traffic, but can no longer reliably shape the real work path. The key question is whether it governs the workflow where decisions occur, not whether it logs the network hop.
Related resources from NHI Mgmt Group
- What are the signs that human-led security operations are no longer keeping pace with AI-driven attacks?
- How do you know if your identity governance model is keeping up with AI agents?
- How can teams tell whether AI-driven fraud controls are keeping up?
- How do organisations know whether privileged access controls are keeping up with AI-driven change?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 25, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
