AI threat modeling must include supply chains, agents, and human risk | CyberScoop<\/title> <meta name=\"description\" content=\"AI security requires more than cloud hardening. Learn why attackers target AI supply chains, agent permissions, and human gatekeepers\u2014and how to build defenses that address the full system.\"> <link rel=\"canonical\" href=\"https:\/\/cyberscoop.com\/ai-threat-modeling-beyond-cloud-infrastructure-op-ed\/\"> <meta property=\"og:locale\" content=\"en_US\"> <meta property=\"og:type\" content=\"article\"> <meta property=\"og:title\" content=\"AI security's \u2018Great Wall\u2019 problem\"> <meta property=\"og:description\" content=\"AI security requires more than cloud hardening. 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<\/p>\n<div class=\"ad ad--top ad--top-mobile\">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<section id=\"stickybar\" class=\"stickybar stickybar--newsletter js-stickybar\" readability=\"0.82\"> <button class=\"stickybar__close js-stickybar-close\" aria-controls=\"stickybar\"> <svg class=\"icon icon--close\" width=\"21\" height=\"22\" viewBox=\"0 0 21 22\" fill=\"none\"><path d=\"m.822.518-.805.805L9.695 11 .017 20.678l.805.805 9.678-9.678 9.677 9.678.806-.805L11.305 11l9.678-9.677-.806-.805-9.677 9.677L.822.518Z\" fill=\"currentColor\" \/><\/svg> <span class=\"visually-hidden\">Close<\/span> <\/button> <\/section>\n<article class=\"single-article content\">\n<div class=\"single-article__container js-single-article-content\">\n<header class=\"single-article__header \" readability=\"26.201716738197\">\n<div class=\"single-article__header-content\" readability=\"35.990384615385\">\n<ul class=\"single-article__eyebrow\">\n<li class=\"single-article__category\"> <a class=\"single-article__category-link\" href=\"https:\/\/cyberscoop.com\/ai-threat-modeling-beyond-cloud-infrastructure-op-ed\/\"> <span>Commentary<\/span> <\/a> <\/li>\n<\/ul>\n<p> AI security requires more than cloud hardening. The real attack surface isn’t your infrastructure\u2014it’s the supply chains, agents, and humans that make up the system around it. <\/p>\n<p>  <br \/>\n<audio id=\"audio-player\" src=\"https:\/\/wp-tts-cdn.api.scpnewsgrp.com\/cyberscoop\/87707\/english.openai.mp3\"><\/audio> <\/p>\n<div readability=\"11\">\n<div>\n<p>Listen to this article<\/p>\n<p>  <\/p>\n<p>0:00<\/p>\n<\/p><\/div>\n<p>  <\/p>\n<p> <span id=\"tts-tooltip\">Learn more.<\/span> <span> This feature uses an automated voice, which may result in occasional errors in pronunciation, tone, or sentiment. <\/span> <\/p>\n<\/div>\n<p>  <\/div>\n<div class=\"single-article__cover-wrap\">\n<figure class=\"single-article__cover\"> <img data-recalc-dims=\"1\" fetchpriority=\"high\" width=\"640\" height=\"426\" src=\"https:\/\/i0.wp.com\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem.jpg?resize=640%2C426&ssl=1\" class=\"single-article__cover-image wp-post-image\" alt decoding=\"async\" fetchpriority=\"high\" srcset=\"https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg 5400w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=300,200 300w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=768,512 768w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=1024,683 1024w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=1536,1024 1536w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=2048,1365 2048w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=600,400 600w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=252,168 252w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=506,337 506w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=1013,675 1013w, https:\/\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-2.jpg?resize=1265,843 1265w\" sizes=\"(max-width: 1013px) 100vw, 1013px\"><figcaption> The great wall of China at sunset. (Getty Images) <\/figcaption><\/figure>\n<\/p><\/div>\n<\/header>\n<div class=\"single-article__content\">\n<div class=\"single-article__content-inner has-drop-cap\"> <html readability=\"76.142092316597\"><body readability=\"153.3023255814\"><\/p>\n<p>The Great Wall of China was built to slow northern raiders and prevent <a href=\"https:\/\/www.britannica.com\/place\/the-Steppe\/Military-and-political-developments-among-the-steppe-peoples-to-100-bce\">steppe armies<\/a> from riding straight into the empire\u2019s heart. Yet in 1644, its most impregnable fortress fell without a siege.<\/p>\n<p>At Shanhai Pass, where the wall meets the Bohai Sea, General Wu Sangui commanded the eastern gate. Behind him: a rebel army had just taken Beijing, the emperor was dead, and the Ming Dynasty was buckling under internal crisis. Ahead: Manchu forces who had spent decades probing for weakness. Wu faced the oldest dilemma in fortress warfare: who is the greater threat?<\/p>\n<p>He opened the gate. The Manchus poured through, defeated the rebels, and never left. They founded the Qing Dynasty and ruled China for the next 268 years, the last imperial dynasty before the republic.<\/p>\n<p>The wall didn\u2019t fail. The stone held. What broke was the human system it depended on.<\/p>\n<div class=\"ad ad--inline_1 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<p>Walls do not fail because the bricks are weak. They fail because the system around the wall is weak. Underpaid guards get bribed, gate procedures degrade, supply lines break. The attacker does not need to knock the wall down when they can walk through the gate.<\/p>\n<p>That is why I disagree with the increasingly popular framing that AI security is fundamentally a cloud infrastructure problem. Cloud security matters. Identity, telemetry, and incident response are table stakes. But treating AI risk as something you can solve primarily by hardening the hosting layer is a comforting simplification, not a complete threat model.<\/p>\n<p>Palo Alto Networks <a href=\"https:\/\/www.paloaltonetworks.com\/state-of-cloud-native-security\">recently reported<\/a> that 99% of organizations experienced at least one attack on an AI system in the past year. If nearly everyone is getting hit, the right conclusion is not \u201cbuild a higher wall.\u201d It is \u201cwe are defending the wrong boundaries.\u201d<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-the-fortress-fallacy\">The fortress fallacy<\/h4>\n<p>A fortress mindset starts with an implicit assumption: secure the infrastructure and you secure the system. That mental model can work when the system boundary is clean and the workload is deterministic, but AI breaks both assumptions. Modern AI stacks are ecosystems that depend on components sitting outside the neat perimeter even when a model runs inside your cloud tenant: open-source libraries, data pipelines, evaluation tools, plug-ins, agent frameworks, and the humans who can change any of the above. If your security plan begins and ends with cloud controls, you will build excellent defenses around a system that attackers are not planning to assault head-on. Attackers route around strength and target weakness. Why scale the wall when someone at the gate is underpaid, overworked, or facing an impossible choice?<\/p>\n<div class=\"ad ad--inline_1 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<p>Consider the trust problem. Most organizations are consuming models trained elsewhere, fine-tuning them, and shipping them into production with limited internal expertise. Even if you self-host, you did not write the model or curate the training data. Cloud hardening does not change that reliance; it just makes the box look more secure. The most dangerous failures are not always intrusions but permissioned outcomes. AI agents turn mixed-trust inputs into execution, and when an agent can read internal content, open tickets, or trigger workflows, the attack surface moves to whatever the agent is allowed to do. If an attacker can influence what the agent consumes, they can influence what it executes, often without malware or exploit chains. Real-world AI incidents frequently involve the glue: telemetry, orchestration, plug-ins, and vendor services beside the model.<\/p>\n<p>Humans remain the soft underbelly. AI security discussions obsess over architecture while threat actors obsess over access paths. Cloud controls cannot prevent coercion, social engineering, or insider risk. If a small set of people can approve changes to an AI system\u2019s tools or policies, that is where the attacker will focus. <\/p>\n<p>History teaches this lesson clearly: the Great Wall\u2019s garrison soldiers took payments to allow traders and smugglers to pass, to skip patrols, or to neglect watch duties. Irregular pay and delayed wages made them susceptible. Today, the \u201cbribe\u201d often isn\u2019t cash. It might be a phishing link, a fake vendor request, or pressure to cut corners during a production crisis. The strongest wall doesn\u2019t matter if the person guarding the gate is persuaded to open it.<\/p>\n<h4 class=\"wp-block-heading\" id=\"h-security-from-within\">Security from within <\/h4>\n<p>Begin with a premise security leaders should already accept: everything can be breached. Your job is to reduce the odds, detect threats quickly, and limit the damage when prevention fails.<\/p>\n<div class=\"ad ad--inline_1 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<p>Threat model the entire AI system, not just where it\u2019s hosted. Include data supply chains, tooling, evaluation pipelines, plug-ins, agents, and the people who can change them. If your threat model leaves out upstream and downstream dependencies, it\u2019s incomplete.<\/p>\n<p>Treat non-human identities as serious risks. Agents, service accounts, and tool credentials should be managed like privileged user accounts. Apply zero standing privileges (no always-on access), just-in-time access, contextual approvals for high-impact actions, and monitoring for unusual tool behavior. The moment you give an agent broad permissions \u201cfor productivity,\u201d you\u2019ve created a new control plane that attackers can hijack.<\/p>\n<p>Build audit-grade change control. You should be able to answer: who changed agent permissions, retrieval sources, tool bindings, or policy gates? If those controls can be modified quietly, you\u2019re running a system that\u2019s one rushed change away from becoming a security incident.<\/p>\n<p>Invest in detection that assumes manipulation, not just compromise. The hard part of AI misuse is that malicious actions can look legitimate in traditional logs. You need traceability from input to outcome: what context was fed in, what tool calls happened, what policy was active, and what boundary was crossed.<\/p>\n<p>Cloud security is necessary\u2014but not sufficient. When vendors say AI security is mainly a cloud infrastructure problem, I hear the modern version of \u201cthe wall is tall, so we\u2019re safe.\u201d Wu Sangui\u2019s wall was tall too, stretching to the sea at the empire\u2019s edge. None of that mattered when the system around it collapsed. Empires fall from within.<\/p>\n<div class=\"ad ad--inline_1 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<p>AI security isn\u2019t solved by building a better fortress. It\u2019s solved by governing delegated authority, hardening the supply chain, and building systems that can prove what happened when\u2014not if\u2014something goes wrong.<\/p>\n<p><em>David Schwed is the CEO of SVRN,<\/em> <em>an AI infrastructure company focused on building decentralized, sovereign intelligence systems that empower users with data privacy and autonomous computational power.<\/em><\/p>\n<p><\/body> <\/p>\n<footer class=\"single-article__footer\" readability=\"2.5104166666667\">\n<div class=\"author-card\" readability=\"11\">\n<div class=\"author-card__avatar\">\n<figure class=\"author-card__image-wrap\"> <img data-recalc-dims=\"1\" decoding=\"async\" class=\"author-card__image\" src=\"https:\/\/i0.wp.com\/ddi.mohflo.net\/wp-content\/uploads\/2026\/02\/ai-securitys-great-wall-problem-1.jpg?w=640&ssl=1\" alt=\"David Schwed\"> <\/figure>\n<\/p><\/div>\n<p><h4 class=\"author-card__name\">Written by David Schwed<\/h4>\n<p> David Schwed is the CEO of SVRN, an AI infrastructure company focused on building decentralized, sovereign intelligence systems that empower users with data privacy and autonomous computational power. <\/p>\n<\/p><\/div>\n<div class=\"single-article__tags-container\">\n<h4 class=\"single-article__tags-title\">In This Story<\/h4>\n<\/p><\/div>\n<\/footer>\n<p> <\/html><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"single-article__ads js-single-article-sidebar\">\n<div class=\"ad ad--sidebar js-single-article-sidebar-5 ad--rightrail_1 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<div class=\"ad ad--sidebar js-single-article-sidebar-4 ad--rightrail_2 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div>\n<div class=\"ad ad--sidebar js-single-article-sidebar-3 ad--rightrail_3 \">\n<div class=\"ad__inner\"> <span class=\"screen-reader-text\">Advertisement<\/span> <\/div>\n<\/div><\/div>\n<\/article>\n<div 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{"id":8334,"date":"2026-02-09T05:00:00","date_gmt":"2026-02-09T11:00:00","guid":{"rendered":"https:\/\/cyberscoop.com\/?p=87707"},"modified":"2026-02-09T05:00:00","modified_gmt":"2026-02-09T11:00:00","slug":"ai-securitys-great-wall-problem","status":"publish","type":"post","link":"https:\/\/ddi.mohflo.net\/index.php\/2026\/02\/09\/ai-securitys-great-wall-problem\/","title":{"rendered":"AI security\u2019s \u2018Great Wall\u2019 problem"},"content":{"rendered":"