[{"data":1,"prerenderedAt":100},["ShallowReactive",2],{"glossary:ai-native":3},{"term":4,"published":90,"modified":90,"mentions":91,"related":99},{"slug":5,"title":6,"lede":7,"blocks":8,"faqs":74},"ai-native","AI-Native Security","AI-native security is a platform where AI is the core detection and decision engine, designed in from day one, not a feature added on top of a rule-based product.",[9,12,15,21,23,54,56,58,60,62,64,70,72],{"t":10,"text":11},"p","AI-native security is a security architecture where artificial intelligence is the core detection and decision engine of the platform, designed in from day one, rather than a feature added on top of an existing rule-based product. The distinction matters because the two approaches behave very differently against modern threats.",{"t":13,"text":14},"h2","Key facts",{"t":16,"items":17},"ul",[18,19,20],"AI-native platforms analyze every signal with machine learning models as the primary engine; add-on approaches run AI as a secondary filter after static rules.","The difference shows most clearly on novel threats: zero-day phishing, AI-generated business email compromise and attacks with no known signature.","\"AI-powered\" in marketing can mean either architecture. The question that separates them: what happens if you remove the AI?",{"t":13,"text":22},"AI-native vs AI add-on",{"t":24,"rows":25},"table",[26,30,34,38,42,46,50],[27,28,29],"","AI-native","AI add-on",[31,32,33],"Detection engine","Models are the primary engine, run on every signal","Static rules first, AI as a secondary filter",[35,36,37],"Novel threats","Judged on behavior and intent, no signature needed","Missed until a rule or signature exists",[39,40,41],"Payload-free attacks (BEC)","Scored on relationship and request","Nothing to scan, so it usually passes",[43,44,45],"Learning","Continuous, per tenant, from live traffic","Periodic rule and model releases",[47,48,49],"Explainability","Every verdict carries its reasoning","A score with little context",[51,52,53],"Deployment","API connection inside the tenant","Gateway or appliance in front of it",{"t":13,"text":55},"Why the difference matters",{"t":10,"text":57},"Attackers industrialized personalization. Generative AI writes fluent, error-free spear phishing in any language, and each message can be unique, which defeats signature matching by design. A rule-based engine with an AI layer bolted on still makes its first decision with rules; everything the rules pass through unexamined becomes the AI layer's problem, often with reduced context. An AI-native engine makes its first decision with models that evaluate sender behavior, relationship history, content intent and technical signals together.",{"t":10,"text":59},"This is the same architectural shift that happened in other security categories: antivirus moved from signatures to behavioral models, and network security moved from port rules to traffic analysis. Email security is going through it now, accelerated by AI-generated attacks and the rise of shadow AI inside organizations.",{"t":13,"text":61},"How to tell if a product is AI-native",{"t":10,"text":63},"Four questions cut through the marketing:",{"t":16,"items":65},[66,67,68,69],"What happens if you remove the AI? If a functioning product remains, AI is a feature. If nothing remains, it is the engine.","What makes the block decision on a never-seen-before attack? Ask for the decision path, not the feature list.","When was the detection core built? Platforms architected before the ML era usually retrofit.","Does detection improve without rule updates? AI-native systems learn from the environment they protect: who mails whom, what normal looks like, what employees report.",{"t":13,"text":71},"How Sentaro applies this",{"t":10,"text":73},"Sentaro was built AI-native from the start: one engine, Vord, whose Message, App and Behavioral Defense vectors do the detecting, not a filter behind one. Every message and OAuth event is evaluated by models that learn each organization's normal, which is what catches zero-day phishing and AI-written business email compromise that signature-based layers pass through. See the Sentaro product overview for how the layers fit together.",[75,78,81,84,87],{"q":76,"a":77},"What does AI-native mean in cybersecurity?","It means the platform's core detection and decision-making is built on AI models from the ground up, rather than adding AI features to an existing rule-based product. Remove the AI from an AI-native product and nothing functional remains.",{"q":79,"a":80},"Is AI-native the same as AI-powered?","No. \"AI-powered\" is a marketing term that covers both architectures. Many AI-powered products are legacy engines with an ML scoring layer added. AI-native refers specifically to products where AI is the primary engine.",{"q":82,"a":83},"Why does AI-native matter for email security?","Because modern phishing and BEC are increasingly AI-generated and unique per message, signature and rule matching fails by design. Detection has to be based on behavior, relationships and intent, which requires models at the core.",{"q":85,"a":86},"What are the drawbacks of AI-native security?","Model quality depends on data quality, decisions need to be explainable enough to audit, and false positives must be managed. Mature AI-native platforms address this with per-organization learning and transparent verdict reasoning.",{"q":88,"a":89},"Is a secure email gateway (SEG) AI-native?","Traditional SEGs predate the ML era and are rule-and-signature engines at the core, even when AI features have been added. API-based platforms built in the last several years are more often AI-native, but verify with the four questions above rather than the label.","2026-09-08",[92,93,94,95,96,97,98],"business-email-compromise","phishing","secure-email-gateway","shadow-ai","spear-phishing","vendor-email-compromise","zero-day-attacks",[94,98,95],1789658070041]