Notifications
Clear all
Topic starter
09/06/2026 11:07 pm
TL;DR: Enterprises comparing Netskope alternatives are increasingly separating SSE consolidation from AI governance, because traditional traffic-layer controls do not fully govern employee or autonomous-agent interactions with AI, according to WitnessAI. That split is now operational, not theoretical: intent, identity, and action attribution require a different control layer than network inspection alone.
NHIMG editorial — based on content published by WitnessAI comparing Netskope alternatives and AI governance approaches
Questions worth separating out
Q: How should security teams evaluate Netskope alternatives for AI governance?
A: Security teams should separate SSE requirements from AI governance requirements.
Q: Why do traditional SSE platforms fall short for autonomous agents?
A: Traditional SSE platforms are built to move and inspect traffic, not to govern decision-making by autonomous actors.
Q: What breaks when AI activity is logged without identity attribution?
A: Without identity attribution, audit trails cannot distinguish a human prompt from an agent-initiated action or a delegated workflow.
Practitioner guidance
- Separate SSE decisions from AI governance decisions Use Netskope-style evaluation criteria for traffic enforcement, then run a separate control assessment for intent-based AI policy, identity attribution, and runtime defense.
- Map AI interactions to actor identity Require controls that distinguish employee, workload, and autonomous-agent activity across model, app, and tool interactions.
- Test runtime controls against agent tool use Build test cases where an agent attempts to call tools, access sensitive data, or chain actions without a human approval gate.
What's in the full article
WitnessAI's full blog post covers the operational detail this post intentionally leaves for the source:
- Side-by-side evaluation criteria for WitnessAI, Palo Alto Networks, Zscaler, Cisco, and Fortinet across AI governance and SSE fit.
- Deployment specifics for proxy chaining, API integration, and agentless options in enterprise environments.
- Detailed product differences in intent-based classification, audit trails, and runtime defense for AI interactions.
- Pricing and packaging notes that matter when teams are deciding whether to add a dedicated AI governance layer or stay inside SSE consolidation.
👉 Read WitnessAI's comparison of Netskope alternatives for AI governance →
Netskope alternatives: what the AI governance gap means for IAM?
Explore further
10/06/2026 1:21 am
SSE consolidation and AI governance are now separate buying motions. The article makes clear that organizations are choosing between broader network-security consolidation and deeper control over AI interaction behaviour. Those are related, but not interchangeable, and the difference shows up in whether the platform governs traffic paths or the intent and identity behind an AI action. Practitioners should treat AI governance as its own architectural requirement, not a side effect of SSE modernization.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to the same research.
A question worth separating out:
Q: Who should own AI governance when existing security tools already cover traffic control?
A: AI governance should be owned jointly by IAM, security architecture, and risk teams, because traffic control alone does not establish accountable use. The ownership model must cover identity lifecycle, policy enforcement, and audit evidence for both human and non-human actors. If no one owns delegated AI authority, no one can enforce it consistently.
👉 Read our full editorial: Netskope alternatives expose the gap between SSE and AI governance
