I'm looking into AI-driven policy management solutions as we revamp our SASE stack. The idea of using AI to suggest rule cleanups and group alerts seems promising, but I have some concerns about its reliability, the potential for mistakenly blocking traffic, and how to manage changes on a larger scale. We're a mid-sized organization with hybrid staff and cloud workloads. Currently, our main issues include: too many conflicting firewall rules, our security operations center getting overwhelmed with low-quality alerts, and slow change approval processes. I'd love to hear from anyone who has deployed AI policy management in a SASE platform. Did it really help reduce alert noise and improve response times?
6 Answers
I actually code my firewall rules using CoPilot, and it's a game changer! Super intuitive.
AI can definitely help reduce the noise by grouping alerts and highlighting redundant rules. The real benefit comes when it combines identity and traffic context effectively. I've seen platforms like Cato handle this well. Ideally, the AI suggestions should be actionable rather than random, so it can make a real difference.
The effectiveness of AI depends on having robust identity and device data in play. Without that, it’s just fancy log parsing. It’s good to let AI highlight outdated rules, but I believe we should still review and approve any changes made.
If your rule set is chaotic, AI might just make it sound nicer while pointing out the same issues. It might be best to clean up the rule naming conventions and establish clear baselines before expecting the AI to be truly beneficial.
AI solutions are generally similar across different vendors, but the real distinction is how well they integrate data into a single view. SASE providers like Cato and Cloudflare excel in this area, making AI suggestions easier to trust.
Using AI in SASE is primarily about getting suggestions, but I recommend treating these as proposals. Always maintain a rollback plan and document each change for auditing purposes.
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