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Convequity on X

$PANW and $DDOG both put something real inside the customer's systems that an AI lab cannot recreate by calling SaaS APIs. Palo Alto's install is still harder to replace with AI. 1. Datadog's agents mostly sit outside the traffic and watch production. A bad agent version rarely takes the business down the way a bad firewall rule can. 2. Palo Alto's firewalls and SASE cloud gateways sit inside the traffic and decide what to allow, deny, decrypt or inspect. A wrong allow or deny hits live traffic and live machines. 3. Many Datadog users start the day in a coding agent and open Datadog when something looks wrong. Many Palo Alto users start in its console and stay there through alerts, triage and response. For them, an outside AI agent is an add-on. Cybersecurity work splits two ways on AI risk. Work done before code ships ("shift-left") or from outside the traffic, such as scanning code, checking cloud settings and sorting collected alerts, gives answers that are easy to check. AI agents are already taking over more of it. Work in live traffic and on live machines ("shift-right") means judging whether an attacker is still inside and whether blocking them is safe. That is more ambiguous and mistakes cost more, so enterprises move slower and demand earned trust. Palo Alto is not risk-free. In our view its exposure is the easily checked detection and vulnerability work inside its platform, which AI agents can take over sooner. The threat is not an AI-native firewall startup.

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