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SiliconANGLE AI · 2026/7/30 14:49:33

How attack path mapping helps AI security agents prioritize risk
AI 中文解读
核心亮点:安全团队终于不再像瞎子摸象,而是用地图思维看清攻击路径,让AI优先处理最危险的风险。
通俗解读:过去安全人员面对一堆警报就像看购物清单,每个警报孤零零的,不知道哪个先处理。现在有了攻击路径映射技术,把云服务器、员工账号、设备等信息全部连成一张网,AI安全代理就能像看地图一样,一眼看出黑客可能从哪个漏洞摸进系统、怎样一步步偷走敏感数据。比如一个看似普通的服务器,背后关联了哪些权限、谁最近动过它,全都一目了然。这样AI就可以优先修补最关键的入口,而不是被大量无关紧要的警报拖住手脚。
实际影响:对普通人来说,企业数据保护会更靠谱,信息泄露、账号被盗的风险降低。对安全团队来说,他们不再需要熬夜排查海量警报,工作效率大幅提升。未来AI安全代理会更聪明,能自主判断风险优先级,你的银行、电商、社交平台后台的防护系统会变得更智能、更主动。
Coverage from SiliconANGLE's livestreaming video studiohelp_outline
UPDATED 10:49 EDT / JULY 30 2026
AI
How attack path mapping helps AI security agents prioritize risk
by
Valentina Villamil
Security teams have spent years chasing alerts in isolation while attackers move fluidly across cloud, identity and device boundaries. That mismatch is pushing more practitioners toward attack path mapping — using graph databases to show AI agents exactly how a threat could reach sensitive data, and where to act first.
Alex Chantavy (pictured), co-founder and CEO of SubImage Inc., a cybersecurity startup that maps enterprise cloud, identity and device data into a single graph, has spent more than a decade working with graph databases. Chantavy previously worked in government cybersecurity and on Microsoft Corp.’s Azure Red Team before founding SubImage to bring an attacker’s map-based instincts to enterprise defense.
“There’s a saying that attackers think in graphs, defenders think in lists,” Chantavy said. “As long as that is true, the attackers will always win.”
Chantavy spoke with theCUBE’s John Furrier at the Neo4j GraphTalk event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed why attack path mapping is critical for reliable AI security agents and how graph databases help prioritize risk. (* Disclosure below.)
Attack path mapping for AI security agents
SubImage traces its roots to Cartography, an open-source mapping tool Chantavy helped build at Lyft Inc. before donating it to the Cloud Native Computing Foundation. The startup finished Y Combinator’s winter 2025 batch, raised $4.2 million in seed funding, and counts Neo4j Inc. among its earliest customers.
“One of the key properties of graphs is locality,” Chantavy said. “I need to know: Is this instance open to the internet? What permissions does it operate as? Who touched it last?”
The stakes are rising as enterprises race to define who governs AI agent identity, a question SiliconANGLE has tracked as security teams confront agents with broad, unsupervised access. Chantavy compared the moment to cloud-native computing’s early days, when Lyft and its peers built their own open-source tooling because no vendor category yet existed.
“You want to represent those many-to-many relationships faithfully,” Chantavy said. “You just can’t really do that in relational land.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Neo4j GraphTalk event:
(* Disclosure: TheCUBE is a paid media partner for the Neo4j GraphTalk event. Neither Neo4j, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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