Zscaler alternatives for AI security beyond the browser

At a glance

What this is: NHI Mgmt Group's analysis, drawing on WitnessAI's blog post: Browser-first security models miss important AI interactions because enterprise AI now operates across native apps, IDEs, embedded assistants, and autonomous workflows.

Why it matters: IAM and security teams need governance that follows the actual AI footprint, or they will overestimate visibility, miss shadow AI, and leave unmanaged agent and developer pathways exposed.

Context

Browser-based inspection was designed for a narrower world than the one enterprises now operate in. When AI activity moves into desktop software, developer tools, embedded copilots, and autonomous workflows, proxy controls can still work on the traffic that passes through them, but they no longer describe the full identity and data path.

That is the core governance gap in this article. The question is not whether a proxy can inspect web sessions, but whether the security programme can see and control AI interactions that happen outside the browser, where discovery, policy enforcement, and runtime oversight are harder to maintain.

For identity teams, this shifts the problem from perimeter coverage to footprint coverage. The practical issue is aligning control planes to user, developer, and agent behaviour across the systems where AI is actually used, rather than assuming one inspection layer can cover every surface.

Key questions

Q: What breaks when AI security stops at the edge?

A: Edge filtering can reduce exposure, but it cannot reliably see inside the container where the model, plugin chain, and orchestration logic operate. If the attacker arrives through a poisoned component or manipulates runtime behaviour, edge controls are already too far away to explain or contain the action path.

Q: Why do native AI apps and agents create governance risk for security teams?

A: They shift execution outside the browser-centric path that many controls were built to inspect. That matters because prompts, responses, and tool use can still involve sensitive data even when no web proxy sees the full interaction. Governance has to follow the workflow, not the application label.

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. If any of those are missing, the control environment is incomplete. Safe AI access is evidenced, not assumed.

Q: What is the difference between browser-based AI control and runtime AI governance?

A: Browser-based control focuses on traffic that passes through a web inspection layer, while runtime governance follows the AI interaction wherever it executes, including native apps and agent workflows. Runtime governance is broader because it must preserve attribution and policy enforcement even when the browser is not part of the path.

Technical breakdown

Why proxy-first AI security loses visibility outside the browser

Proxy-based security works when traffic is forced through an inline path that can be inspected and policy-checked. The limitation appears when AI interactions originate in native desktop applications, IDE extensions, or embedded assistants that use local clients, background services, or non-browser API calls. In those cases, the control plane may still exist, but the inspection point no longer sits on the full path. That creates partial telemetry, incomplete attribution, and policy gaps that are easy to miss if teams assume all AI usage behaves like web browsing.

Practical implication: validate whether each AI workflow actually traverses the control path before treating browser inspection as coverage.

What changes when AI appears in desktop apps, IDEs, and agents

AI in desktop apps and developer environments behaves differently from browser-based SaaS usage because the identity and execution context often live closer to the endpoint or development workstation. That matters for governance because prompt content, model calls, and tool use may be distributed across local clients, plug-ins, and service interactions rather than one browser session. Autonomous or agentic workflows add another layer, because the system may select tools and sequence actions dynamically. Once that happens, visibility has to follow both the human identity and the non-human execution path.

Practical implication: inventory AI by execution surface, not by application category, so policy can follow the real workflow.

Why AI governance now depends on runtime visibility and attribution

AI governance becomes more than DLP or content inspection when organisations need to understand who triggered an interaction, what model or agent acted, and what data moved in both directions. Runtime visibility matters because AI systems can expose sensitive content in prompts and responses, while agentic systems can also expand the blast radius through chained actions. In that model, attribution is not a reporting nice-to-have. It is the control that ties behaviour back to a human or machine identity, which is essential for auditability and response.

Practical implication: require audit trails that connect AI actions to the initiating identity and the affected data path.

Threat narrative

Attacker objective: The objective is to exploit unmanaged AI interaction surfaces so sensitive prompts, data, and agent actions occur beyond effective detection or governance.

1. Entry occurs through legitimate AI use in browser, desktop, or developer workflows that security teams may not monitor consistently.
2. Credential or data exposure follows when prompts, responses, or embedded tool interactions move outside the visibility boundary of the proxy layer.
3. Escalation happens when autonomous or embedded systems continue acting without a corresponding control path for attribution and policy enforcement.
4. Impact is the accumulation of blind spots across shadow AI, unmanaged agent activity, and incomplete evidence for investigation or compliance.

Breaches seen in the wild

• DeepSeek database exposure 2025: An unauthenticated DeepSeek ClickHouse database exposed over a million log lines with plaintext chat history and API keys in 2025.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Browser-only AI security creates a visibility illusion: Proxy inspection still has value, but it cannot define the entire AI control plane once usage moves into desktop applications, IDEs, and agents. That means many programmes are reporting security over a narrower slice of behaviour than they actually need to govern. The practitioner conclusion is simple: coverage claims must be tested against real execution surfaces, not assumed from web traffic.

AI security is becoming an identity-routing problem as much as a data problem: Once AI spans users, developers, and agents, the question is no longer just what content passed through a filter. It becomes which identity initiated the action, which surface executed it, and whether the control system preserved that chain of accountability. The practitioner conclusion is that governance must bind interaction records to the identities behind them.

Shadow AI is no longer just unsanctioned browser usage: Native apps, IDE extensions, and autonomous workflows expand the unmanaged zone beyond what conventional discovery tools were built to find. That broadens the discovery problem from application inventory to behavioural inventory. The practitioner conclusion is to treat AI discovery as a continuous control, not a one-time onboarding task.

Agentic workflows force organisations to separate visibility from attribution: Seeing that an agent acted is not the same as knowing who authorized the underlying capability, what policy constrained it, or where it sent data. In practice, many governance stacks can log one of those dimensions but not all three. The practitioner conclusion is that agent oversight requires traceability across human intent, machine action, and data movement.

AI governance now needs architecture-aware segmentation of controls: Desktop apps, browsers, IDEs, and autonomous systems cannot all be governed with the same inspection assumptions. That does not mean every surface needs a different programme, but it does mean the control architecture must be matched to execution reality. The practitioner conclusion is to segment AI governance by surface, risk, and identity type rather than by vendor category.

From our research library:

• Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
• Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• Read next: Threat Modelling AI Agents

What this signals

AI security visibility now has to be surface-aware: Browser inspection remains useful, but it no longer maps cleanly to the way enterprise AI is actually deployed. Programmes that do not distinguish between web traffic, desktop clients, developer tools, and agent frameworks will keep undercounting where governance is needed.

Shadow AI discovery becomes a standing identity task: If unmanaged copilots, IDE extensions, and autonomous workflows are appearing outside standard onboarding paths, discovery must operate continuously rather than as a one-off audit. That is especially true when access and activity can shift between human and non-human identity layers in the same workflow.

For practitioners

• Map AI controls to execution surfaces Inventory where AI actually runs across browser sessions, desktop apps, IDEs, copilots, and agent frameworks before deciding which controls apply.
• Separate browser visibility from endpoint visibility Test whether prompt, response, and tool activity remain observable when the same workflow moves from web apps into native clients or developer tools.
• Require identity-linked AI audit trails Confirm that logs tie each AI action back to the initiating human or non-human identity, not just to the application or proxy event.
• Validate agent coverage with real workflows Run proof-of-value tests using autonomous and embedded assistant scenarios to verify discovery, policy enforcement, and runtime protections on the paths you actually use.
• Reassess shadow AI discovery continuously Treat discovery as an ongoing control because new copilots, extensions, and agentic tools can appear outside standard onboarding and network change processes.

Key takeaways

• Browser-centric AI controls do not fully describe enterprise AI risk once desktop apps, IDEs, copilots, and agents enter the picture.
• Governance failures here are architectural because the security stack is being asked to inspect interactions that never reliably enter the browser path.
• The practical response is to align discovery, attribution, and runtime controls to the actual AI footprint rather than to a single inspection layer.

Key terms

• Browser-based AI usage control: Browser-based AI usage control is the practice of governing how people use AI tools inside web browsers. It limits what data can be entered, which sites or apps can interact with AI, and what actions AI outputs may trigger. Technically, it combines policy enforcement, content inspection, session controls, and audit logging at the browser layer.
• Runtime AI Governance: Runtime AI governance is control applied while the interaction is happening, rather than before deployment or after an incident. It combines discovery, policy enforcement, output inspection, and audit logging so that AI use can be managed in live enterprise conditions.
• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• Agentic workflow: An agentic workflow is a sequence of tasks executed by an AI agent with some level of tool access and decision authority. In security terms, the workflow matters because it can span multiple systems, identities, and permissions, which makes attribution and revocation harder than with ordinary automation.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.