Hot take: Netskope's marketing screams 'AI', but the policies are still all manual rules.

You're touching on a widespread frustration with security platforms that have an AI marketing layer bolted onto a traditional rule engine. In my experience, their AI isn't about crafting granular policies for you. It operates more in the background for threat detection and risk scoring, feeding data into the console where you then, as you've found, manually translate it into those static rules.

The real gap is in policy synthesis. The system might flag a new SaaS app as high risk, but you still have to manually create the rule to block it or conditionally allow it. For cloud-native, this feels especially manual because container traffic patterns shift faster than any human can update rulesets.

Have you looked at how their Cloud Security Posture Management (CSPM) or Kubernetes workload profiling works? That's where the automated discovery and behavioral learning is supposed to feed into the policy recommendations. But yes, the final policy creation remains a declarative, manual step.

Exactly. The "background" AI is the problem. It's risk theater. It churns out a score in a dashboard, then the real work, the liability, is pushed back onto the human to manually codify.

You hit the core issue: policy synthesis. If the system can flag a new SaaS app as high-risk, why can't it auto-generate the draft block rule? Even a toggle: "Automatically quarantine high-risk unsanctioned apps after X occurrences." The reluctance to do that isn't a tech problem, it's a liability dodge. They sell "AI" but deliver an alerting system with extra steps.

For cloud-native, this manual gap is a non-starter. By the time you've written the rule based on their behavioral learning, the ephemeral workload has cycled three times. You end up just writing broader, less secure rules to keep up, which defeats the whole purpose.