SASE backhauling assumptions are breaking in AI-era work

At a glance

What this is: This analysis argues that SASE’s original backhauling model no longer maps to how AI-era work actually happens.

Why it matters: It matters because IAM, PAM, and security teams increasingly need enforcement at the point of interaction, not just at the network edge, to govern humans, contractors, and AI agents safely.

By the numbers:

• 52% of remote-capable professionals work in hybrid arrangements, and another 26% work exclusively remote.

👉 Read Island's analysis of why SASE backhauling assumptions are breaking in AI-era work

Context

SASE was built to solve a real problem: the old perimeter could not follow users, devices, and cloud applications. The challenge now is that AI-era workflows do not behave like conventional network traffic, so the control point has shifted from routing and inspection to the moment of interaction. For identity and access teams, that means the governance question is no longer only who or what may connect, but what the user or agent is allowed to do inside the session.

The article’s core claim is that backhauling and packet inspection were always partial answers, and they are increasingly insufficient where contractors, unmanaged devices, SaaS apps, and agentic AI overlap. That is a genuine identity intersection because the enforcement gap is no longer just network posture. It is also session context, delegated access, and machine-speed action that traditional controls struggle to interpret.

Key questions

Q: How should security teams govern AI workflows when network inspection is not enough?

A: They should move beyond traffic-only controls and govern the session where the work occurs. That means combining identity, application context, tool-use auditing, and policy enforcement at the point of interaction. If a control can only tell you where traffic went, it is not sufficient for AI-era workflows that make decisions inside the app and then act immediately.

Q: Why do SASE and backhauling models struggle with modern identity governance?

A: Because they assume the network path is the best place to understand risk, while many high-value actions now happen before or inside the application session. Users, contractors, and agents can all behave legitimately at the edge and still create governance gaps later. Identity teams should treat network controls as one signal, not the governing source of truth.

Q: What do security teams get wrong about unified SASE enforcement?

A: They often assume one console means one decision model. In practice, SWG, CASB, ZTNA, and SD-WAN may each apply policy differently and at different times. That fragmentation weakens context continuity, so teams should test whether the same session is evaluated consistently as it moves across controls.

Q: Who is accountable when AI or contractor activity bypasses network-level controls?

A: Accountability sits with the programme owner who defined the control boundary, not just the operator who ran the platform. If AI workflows, contractors, or third parties can act inside applications without session-level governance, then the architecture has failed to carry policy to the point of action. That is an identity and access design issue, not only a networking issue.

Technical breakdown

Why network inspection misses agentic AI intent

Network inspection can confirm that an agent connected to an LLM or SaaS endpoint, but it cannot reliably explain what the agent read, why it acted, or how output was reshaped inside the application. Agentic workflows often include local reasoning, tool execution, and in-session application events that never become useful network signals. That creates a visibility gap between connectivity and decision-making. The technical limit is not encryption alone. It is that the most relevant security signal exists before or after the packet, inside the workflow boundary rather than on the wire.

Practical implication: Security teams need session-aware control points that can observe tool use and application context, not just traffic metadata.

Why backhauling creates a false sense of unified enforcement

Traditional SASE often centralises delivery through cloud points of presence, but that does not mean policy is enforced once, consistently, or in the same context. SWG, CASB, ZTNA, and SD-WAN can each evaluate a session separately, which fragments the decision trail and makes policy carryover unreliable. The result is a distributed inspection chain, not a single enforcement plane. In practice, context gathered at login does not always govern what happens later in the session, especially when users move between applications, devices, and trust states.

Practical implication: Architects should map where policy is evaluated versus where behaviour actually occurs, then remove redundant decision points.

What changes when work happens at the point of interaction

Modern work increasingly originates at the application layer, where users paste code into AI tools, move files between tenants, or delegate actions to agents and SaaS workflows. That means the operative control boundary is no longer the data-centre edge or a distant proxy. It is the interaction surface where the action starts. This is also why zero trust guidance remains relevant: the control should sit as close to the resource and relationship as possible, continuously. Network transit alone cannot carry enough context to govern that model well.

Practical implication: Identity, access, and security teams should push enforcement closer to the session, the app, and the delegated action itself.

NHI Mgmt Group analysis

Point-of-interaction enforcement is now the governing model that matters. SASE-style backhauling can still contribute transport security, but it no longer answers the hardest question in AI-era operations: what is permitted inside the session after the connection is established. That creates a control gap between authentication and action, which is where modern abuse often lives. For identity programmes, this is the same shift seen in NHI governance and agentic AI control design. Practitioners should treat the interaction layer as a first-class enforcement surface, not a logging afterthought.

Network inspection is not a governance strategy for agentic AI. The article is right to separate visibility of traffic from visibility of behaviour. AI agents, like service accounts and other NHIs, can complete meaningful actions without generating a clear human-readable network story. That means the security model needs identity, context, and delegated authority rather than packet depth alone. The named concept here is interaction-layer governance gap: the mismatch between where work is decided and where legacy controls look for evidence. Practitioners should design for session truth, not traffic volume.

Unified delivery is not the same as unified control. Many programmes still equate consolidation with governance maturity, but fragmented enforcement points can preserve complexity even when the console looks simpler. That matters across IAM, PAM, and cloud access because inconsistent context handling produces policy drift. The NIST Zero Trust Architecture principle of continuous verification becomes much harder to execute when controls sit sequentially in the data path rather than near the action. Practitioners should re-evaluate whether their architecture actually enforces one decision model or merely presents one.

The identity boundary is widening from users to contractors, devices, and agents. The article’s examples show that risk now emerges from mixed trust states, not just compromised logins. A contractor, a personal device, a SaaS workflow, and an AI assistant can all participate in the same business action. That makes identity governance a cross-domain problem spanning human identity, NHI, and delegated access. Practitioners should assume the next control failure will come from a session that looked legitimate at the edge but behaved differently inside the application.

What this signals

Interaction-layer governance gap: the next enforcement problem is not transport security but decision quality inside the application session. As AI tools, SaaS workflows, and delegated access merge, teams will need controls that can interpret intent, context, and identity together. NIST SP 800-207 Zero Trust Architecture remains relevant, but only if practitioners apply it closer to the action than many SASE deployments currently do.

The practical signal for identity and security programmes is that visibility stacks and enforcement stacks can no longer be designed separately. If the control cannot see the session state that produced the action, it will miss the part of the workflow that matters most. That pushes IAM, PAM, and NHI governance toward unified identity-aware enforcement, not just better inspection.

For practitioners, the near-term planning question is whether their current architecture can govern humans, contractors, and AI agents inside the same workflow without relying on backhaul as the primary trust boundary. If not, the programme will need more session-level policy, stronger delegated-access controls, and better auditability of application actions.

For practitioners

• Map enforcement to the point of interaction Inventory where policy is actually decided for SaaS, AI tools, and delegated workflows, then compare that to where users and agents perform actions. Prioritise controls that can evaluate session context at the application layer rather than only at the network boundary.
• Separate visibility from enforceability Review whether your inspection stack can prove what happened after a prompt, file upload, or tool call, not just that traffic reached a known endpoint. If it cannot, treat it as partial telemetry and pair it with application-layer control and audit data.
• Reassess zero trust for AI-era workflows Use NIST SP 800-207 Zero Trust Architecture to test whether continuous verification still holds when identity, device state, and application context change mid-session. Where enforcement is sequential and proxy-heavy, redesign toward nearer-to-action policy checks.
• Extend identity governance to contractors and agents Include third parties, unmanaged devices, and AI agents in the same governance review because they now participate in the same business workflow. Apply least privilege, session controls, and explicit delegated access boundaries to each.

Key takeaways

• SASE backhauling still has value, but it no longer matches where AI-era risk originates: inside the application session.
• Network visibility alone cannot govern agentic workflows, contractor activity, or delegated access when the decisive action happens before traffic inspection.
• Practitioners should shift control design toward point-of-interaction enforcement, with identity and session context driving policy.

Key terms

• Point-of-interaction enforcement: A control model that applies policy where the user, agent, or workflow actually performs the action inside the application session. It matters because network routing and inspection can confirm transit, but they often cannot govern the decision itself or preserve enough context to explain it later.
• Interaction-layer governance gap: The mismatch between where modern digital work is decided and where legacy security tools are able to observe it. In AI, SaaS, and delegated-access workflows, the highest-value actions can happen before any useful network signal appears, leaving a blind spot in identity and policy enforcement.
• Delivery unification: A security architecture in which multiple controls are delivered through a common cloud service or vendor platform. It can simplify operations, but it does not automatically create a single enforcement model, because policy may still be evaluated at several separate points with different context and outcomes.
• Continuous Verification: A Zero Trust practice that re-evaluates trust during the session instead of relying on a single successful login. The control is stronger when context signals are available in real time and when the identity programme can act on those signals without creating excessive exceptions.

What's in the full article

Island's full blog post covers the architectural detail this post intentionally leaves for the source:

• The step-by-step argument for why PoP-based backhauling no longer maps cleanly to AI-era work patterns.
• Specific examples of how prompt activity, local tool execution, and application-layer interactions evade network-only inspection.
• The article's comparison between delivery unification and enforcement unification in SASE architectures.
• Island's recommended direction for moving enforcement closer to the point of work.

👉 Island's full post expands the AI workflow examples, inspection limits, and enforcement implications.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, secrets management, and workload identity. It helps identity and security practitioners build the control thinking needed for modern delegated access and agentic workflows.