SASECompare
Home/Comparisons/AI/ML Traffic Security & Controls

AI/ML Traffic Security & Controls

LIVE

Can the platform detect, classify, and control AI/ML application traffic?

Gartner's #1 cybersecurity trend for 2025-2026 is AI security. CISOs must control which AI tools employees use, prevent data leakage to AI services, and secure agentic AI workflows. We tested 5 specific AI security capabilities across all 8 SASE vendors.

5checks
8vendors
Information sourced from publicly available documentation. Vendor capabilities change frequently. Always verify with the vendor before making purchasing decisions. Not affiliated with any vendor. See our terms & disclaimer. Vendors: to report inaccuracies, email info@sasecompare.com.
Cato Networks
Cato

5/5

Check Point
Check Point

4/5

Cisco
Cisco

5/5

Cloudflare
Cloudflare

4/5

Fortinet
Fortinet

5/5

Netskope
Netskope

5/5

Palo Alto Networks
Palo Alto

5/5

Zscaler
Zscaler

5/5

YESSupported
PARTIALLimited
NONot supported
TBDResearch pending
01

AI/ML application traffic classification?

02

Granular per-app AI policies?

03

Real-time content inspection for AI uploads?

04

Shadow AI usage detection?

05

Sanctioned vs. unsanctioned AI differentiation?

Share
Did we get something wrong?Let us know

Need this analysis tailored to your environment?

Get a custom report with deeper analysis, weighted scoring based on your priorities, and vendor recommendations specific to your deployment.

Request Custom Report

Get notified when we publish new comparisons

No spam. Just new research drops and major updates.

Frequently Asked Questions

Which SASE vendor is best for ai/ml traffic security & controls?
Based on 5 checks across 8 vendors, Cato Networks and Cisco and Fortinet and Netskope and Palo Alto Networks and Zscaler lead with 5 out of 5 capabilities fully supported (YES). Cloudflare scored lowest with 4 YES answers. Results are based on publicly available documentation. Always verify with the vendor before purchasing.
Does the platform detect shadow AI usage - unauthorized AI tools and agentic AI workflows across the organization?
Cato Networks, Check Point, Cisco, Fortinet, Netskope, Palo Alto Networks, Zscaler fully support this. Cloudflare offers partial support. Employees are using AI tools IT doesn't know about. Shadow AI is the new shadow IT, but with far higher data exposure risk
Can it differentiate between sanctioned AI tools (corporate Copilot with enterprise agreements) and unsanctioned consumer AI services?
Cato Networks, Cisco, Cloudflare, Fortinet, Netskope, Palo Alto Networks, Zscaler fully support this. Check Point offers partial support. Enterprise AI tools have data processing agreements. Consumer versions don't. Treating them the same is a compliance gap
Can the platform identify and classify AI/ML application traffic (ChatGPT, Copilot, Gemini, Claude, etc.) as a distinct category?
Cato Networks, Check Point, Cisco, Cloudflare, Fortinet, Netskope, Palo Alto Networks, Zscaler fully support this. You can't control what you can't see. If AI traffic blends into 'web browsing', you have zero visibility into your AI exposure
Does it support granular policies to allow/block/limit specific AI applications per user group?
Cato Networks, Check Point, Cisco, Cloudflare, Fortinet, Netskope, Palo Alto Networks, Zscaler fully support this. Marketing may need ChatGPT while finance shouldn't use it. Blanket allow/deny is not good enough
Can it inspect content being uploaded to AI services and block sensitive data (PII, source code, financials) in real-time?
Cato Networks, Check Point, Cisco, Cloudflare, Fortinet, Netskope, Palo Alto Networks, Zscaler fully support this. A developer pasting proprietary code into ChatGPT is a data breach. Inline DLP for AI prompts is non-negotiable
How is the AI/ML Traffic Security & Controls comparison tested?
We test 5 specific scenarios across Cato Networks, Check Point, Cisco, Cloudflare, Fortinet, Netskope, Palo Alto Networks, Zscaler. All answers are sourced from publicly available vendor documentation, knowledge base articles, and verified user reports. YES means confirmed working with documentation, PARTIAL means it works with significant limitations, NO means confirmed not supported.

Methodology

All answers are sourced from publicly available vendor documentation, knowledge base articles, press releases, and verified user reports. We do not rely on vendor marketing claims.

YES means the feature is confirmed working with documentation. PARTIAL means it works with significant caveats or limitations. NO means it is confirmed not supported. TBD means research is still in progress.

Click any cell in the matrix to see the detailed evidence and source link.

Feedback

Help me make this better

This is a one-person project. Your input directly shapes what gets added, fixed, or prioritized next.