{"id":26714,"date":"2026-07-05T10:30:48","date_gmt":"2026-07-05T09:30:48","guid":{"rendered":"https:\/\/neocoeurintelligence.com\/?p=26714"},"modified":"2026-07-13T14:22:26","modified_gmt":"2026-07-13T13:22:26","slug":"ai-risk-assessment-adversarial-ai-the-methodology-to-map-your-exposure","status":"publish","type":"post","link":"https:\/\/neocoeurintelligence.com\/fr\/ai-risk-assessment-adversarial-ai-the-methodology-to-map-your-exposure\/","title":{"rendered":"\u00c9valuation des risques IA \u2013 IA adversariale : la m\u00e9thodologie pour cartographier votre exposition"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"26714\" class=\"elementor elementor-26714\">\n\t\t\t\t<div class=\"elementor-element elementor-element-342cb2d e-flex e-con-boxed sc_layouts_column_icons_position_left e-con e-parent\" data-id=\"342cb2d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div 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a{color:#1F7A8C}\n.stat-row{display:grid;grid-template-columns:repeat(3,1fr);gap:16px;margin:16px 0}\n.stat-card{background:#0A1A2F;border:1px solid #1F4863;border-radius:10px;padding:20px 16px;text-align:center}\n.stat-val{font-size:36px;font-weight:700;color:#22D3EE;line-height:1;margin-bottom:4px}\n.stat-label{font-size:11px;color:#A8BCCD;line-height:1.5}\n@media(max-width:720px){\n.risk-matrix{grid-template-columns:1fr}\n.attack-grid{grid-template-columns:1fr}\n.pillar-grid{grid-template-columns:repeat(3,1fr)}\n.process5{grid-template-columns:1fr}\n.proc-arrow{display:none}\n.stat-row{grid-template-columns:1fr}\n}\n<\/style>\n\n<div class=\"aiact-art\">\n\n<div class=\"aiact-wrap\">\n<div class=\"aiact-meta\"><strong>ART-AI-RISK<\/strong> \u00b7 Neo Coeur Intelligence \u00b7 Published: July 2026 \u00b7 Based on MITRE ATLAS v4 \u00b7 Gartner 2025<\/div>\n<\/div>\n\n<div class=\"aiact-toc\">\n<h3>\ud83d\udccb TABLE OF CONTENTS<\/h3>\n<div class=\"aiact-toc-item\"><span class=\"aiact-toc-num\">00.<\/span>Introduction<\/div>\n<div class=\"aiact-toc-item\"><span class=\"aiact-toc-num\">I.<\/span>General Overview: AI Risk Assessment and Adversarial AI<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">1.1<\/span>Definition and foundations<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">1.2<\/span>The scale of the problem: key figures<\/div>\n<div class=\"aiact-toc-item\"><span class=\"aiact-toc-num\">II.<\/span>Detailed Analysis: Threat Landscape<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">2.1<\/span>The 5 C-I-D-T-P pillars of AI risk<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">2.2<\/span>MITRE ATLAS: the 80+ TTPs you need to know<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">2.3<\/span>Four critical attack vectors in production<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">2.4<\/span>Concrete sector cases<\/div>\n<div class=\"aiact-toc-item\"><span class=\"aiact-toc-num\">III.<\/span>Methodology: Mapping Your AI Exposure<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">3.1<\/span>Step 1. Define scope and governance<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">3.2<\/span>Step 2. Inventory production AI models<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">3.3<\/span>Step 3. Classify vulnerabilities by C-I-D-T-P pillar<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">3.4<\/span>Step 4. Conduct AI red teaming<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">3.5<\/span>Step 5. Document, remediate and maintain<\/div>\n<div class=\"aiact-toc-item indent\"><span class=\"aiact-toc-num\">3.6<\/span>AI risk audit checklist<\/div>\n<div class=\"aiact-toc-item\"><span class=\"aiact-toc-num\">IV.<\/span>Conclusion<\/div>\n<div class=\"aiact-toc-item\"><span class=\"aiact-toc-num\">V.<\/span>References<\/div>\n<\/div>\n\n<div class=\"aiact-section\">\n<div class=\"aiact-wrap\">\n\n<h1>Introduction<\/h1>\n<div class=\"aiact-divider\"><\/div>\n\n<p>Your AI models are probably vulnerable to FGSM, PGD or prompt injection. And you don&#8217;t know it. This is not a provocative claim, it is a statistical reality confirmed by Gartner in 2025: <strong>80% of AI models deployed in production have never been subjected to adversarial security testing<\/strong>. In an era where machine learning systems make decisions that directly affect credit access, medical diagnoses, fraud detection and hiring processes, this blind spot constitutes a systemic organizational risk.<\/p>\n\n<p>The question this article addresses is both methodological and operational: how can an organization rigorously identify, classify and prioritize the vulnerabilities of its production AI models, before a security incident, a regulatory audit, or a coordinated adversarial attack forces it to do so under emergency conditions?<\/p>\n\n<p>This guide proposes a complete methodology structured around five auditable steps, grounded in the MITRE ATLAS framework (80+ documented Tactics, Techniques and Procedures for AI systems) and the five pillars of AI risk: Confidentiality, Integrity, Denial, Traceability, and Privacy (C-I-D-T-P). Each step produces concrete deliverables that are defensible before regulators, insurers and executive committees.<\/p>\n\n<div class=\"aiact-update\">\n<h4>\u26a1 CONTEXT, GARTNER 2025 + MITRE ATLAS V4<\/h4>\n<p>According to Gartner&#8217;s 2025 AI Security report, 80% of production AI models have never been adversarially tested. MITRE ATLAS v4, the reference taxonomy for AI-specific threats, now catalogs over 80 TTPs (Tactics, Techniques and Procedures) targeting machine learning systems, from training data poisoning to model extraction and prompt injection against large language models.<\/p>\n<\/div>\n\n<h1 style=\"margin-top:64px\">I. General Overview: AI Risk Assessment and Adversarial AI<\/h1>\n<div class=\"aiact-divider\"><\/div>\n\n<h2>1. Definition and Foundations<\/h2>\n\n<p>AI Risk Assessment designates the structured process of identifying, analyzing and evaluating the vulnerabilities specific to artificial intelligence systems. It differs fundamentally from classical cybersecurity audit in that it must address threats that are intrinsic to the statistical nature of machine learning models. Threats that no conventional penetration testing approach can detect.<\/p>\n\n<p>An AI model is not a deterministic software program. It is a mathematical function trained on data, whose behavior can be manipulated through carefully crafted inputs (adversarial examples), corrupted via its training pipeline (data poisoning), or reverse-engineered through repeated queries (model inversion). These attack surfaces do not exist in traditional software, which is why they require a dedicated methodology.<\/p>\n\n<p><strong>Adversarial AI<\/strong> refers to the set of techniques used to deliberately exploit these specificities, either to cause the model to produce incorrect outputs, to extract sensitive information from it, or to undermine confidence in its results. The MITRE ATLAS framework (Adversarial Threat Landscape for Artificial-Intelligence Systems) is today the reference taxonomy for structuring this threat landscape.<\/p>\n\n<h2>2. The Scale of the Problem: Key Figures<\/h2>\n\n<div class=\"fig\">\n<div class=\"fig-head\"><span><span class=\"dots\"><span>\u25cf<\/span><span>\u25cf<\/span><span>\u25cf<\/span><\/span>FIGURE 01 \u00b7 AI SECURITY EXPOSURE \u00b7 KEY METRICS 2025<\/span><span class=\"fig-tag\">GARTNER 2025<\/span><\/div>\n<div class=\"stat-row\">\n<div class=\"stat-card\"><div class=\"stat-val\">80%<\/div><div class=\"stat-label\">of production AI models never adversarially tested<br><em>(Gartner, 2025)<\/em><\/div><\/div>\n<div class=\"stat-card\"><div class=\"stat-val\">80+<\/div><div class=\"stat-label\">TTPs documented in MITRE ATLAS v4 targeting AI\/ML systems specifically<\/div><\/div>\n<div class=\"stat-card\"><div class=\"stat-val\">3\u00d7<\/div><div class=\"stat-label\">higher probability of undetected breach in untested ML models vs. audited models<\/div><\/div>\n<\/div>\n<div class=\"fig-foot\">Sources: Gartner AI Security Survey 2025 \u00b7 MITRE ATLAS v4 \u00b7 Neo Coeur Intelligence analysis<\/div>\n<\/div>\n\n<p>These figures reveal a structural paradox: organizations invest massively in deploying AI systems while systematically neglecting the security dimension specific to these systems. This gap is explained by three converging factors: the novelty of adversarial threats, the absence of standardized audit frameworks until recently, and the organizational separation between data science teams and cybersecurity teams.<\/p>\n\n<h1 style=\"margin-top:64px\">II. Detailed Analysis: Threat Landscape<\/h1>\n<div class=\"aiact-divider\"><\/div>\n\n<h2>1. The 5 C-I-D-T-P Pillars of AI Risk<\/h2>\n\n<p>Classical information security is organized around the CIA triad (Confidentiality, Integrity, Availability). AI systems require an extended framework that takes into account threats specific to their statistical architecture and their role in automated decision-making. The C-I-D-T-P framework extends this triad to five dimensions.<\/p>\n\n<div class=\"fig\">\n<div class=\"fig-head\"><span><span class=\"dots\"><span>\u25cf<\/span><span>\u25cf<\/span><span>\u25cf<\/span><\/span>FIGURE 02 \u00b7 THE 5 C-I-D-T-P PILLARS OF AI RISK<\/span><span class=\"fig-tag\">FRAMEWORK<\/span><\/div>\n<div class=\"pillar-grid\">\n<div class=\"pillar-card p-c\"><div class=\"pillar-letter\">C<\/div><div class=\"pillar-name\">Confidentiality<\/div><div class=\"pillar-def\">Preventing extraction of sensitive data embedded in the model or its training set<\/div><\/div>\n<div class=\"pillar-card p-i\"><div class=\"pillar-letter\">I<\/div><div class=\"pillar-name\">Integrity<\/div><div class=\"pillar-def\">Ensuring model outputs cannot be manipulated through adversarial inputs or data poisoning<\/div><\/div>\n<div class=\"pillar-card p-d\"><div class=\"pillar-letter\">D<\/div><div class=\"pillar-name\">Denial<\/div><div class=\"pillar-def\">Maintaining model availability and performance under adversarial load or evasion attacks<\/div><\/div>\n<div class=\"pillar-card p-t\"><div class=\"pillar-letter\">T<\/div><div class=\"pillar-name\">Traceability<\/div><div class=\"pillar-def\">Ensuring model decisions are explainable, auditable and attributable (XAI requirements)<\/div><\/div>\n<div class=\"pillar-card p-p\"><div class=\"pillar-letter\">P<\/div><div class=\"pillar-name\">Privacy<\/div><div class=\"pillar-def\">Protecting individuals from inference, re-identification or membership inference attacks<\/div><\/div>\n<\/div>\n<div class=\"fig-foot\">Neo Coeur Intelligence C-I-D-T-P framework, extended AI risk taxonomy beyond classical CIA triad<\/div>\n<\/div>\n\n<h2>2. MITRE ATLAS: The 80+ TTPs You Need to Know<\/h2>\n\n<p>MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is the authoritative taxonomy for AI-specific threats, maintained by MITRE Corporation in collaboration with over 30 organizations including leading AI labs and government agencies. Version 4 catalogs more than 80 TTPs structured across 14 tactics, from initial reconnaissance against AI infrastructure to impact and exfiltration.<\/p>\n\n<div class=\"fig\">\n<div class=\"fig-head\"><span><span class=\"dots\"><span>\u25cf<\/span><span>\u25cf<\/span><span>\u25cf<\/span><\/span>FIGURE 03 \u00b7 MITRE ATLAS TACTIC STRUCTURE \u00b7 SELECTED HIGH-IMPACT TACTICS<\/span><span class=\"fig-tag\">MITRE ATLAS V4<\/span><\/div>\n<div class=\"risk-matrix\">\n<div class=\"risk-cell critical\"><div class=\"risk-icon\">\u2620<\/div><div class=\"risk-label\">TACTIC AML.TA0000<\/div><div class=\"risk-title\">Reconnaissance<\/div><div class=\"risk-desc\">Intelligence gathering on model architecture, APIs, training data sources and deployment infrastructure<\/div><\/div>\n<div class=\"risk-cell high\"><div class=\"risk-icon\">\u2697<\/div><div class=\"risk-label\">TACTIC AML.TA0002<\/div><div class=\"risk-title\">Resource Dev.<\/div><div class=\"risk-desc\">Acquiring adversarial tools, shadow model training, crafting malicious datasets for supply chain attacks<\/div><\/div>\n<div class=\"risk-cell critical\"><div class=\"risk-icon\">\ud83d\udc89<\/div><div class=\"risk-label\">TACTIC AML.TA0005<\/div><div class=\"risk-title\">ML Attack Staging<\/div><div class=\"risk-desc\">Crafting adversarial examples (FGSM, PGD, C&#038;W), poisoned inputs and prompt injection payloads<\/div><\/div>\n<div class=\"risk-cell high\"><div class=\"risk-icon\">\ud83d\udd13<\/div><div class=\"risk-label\">TACTIC AML.TA0006<\/div><div class=\"risk-title\">Exfiltration<\/div><div class=\"risk-desc\">Model extraction via repeated queries, membership inference to reconstruct training data<\/div><\/div>\n<div class=\"risk-cell medium\"><div class=\"risk-icon\">\ud83c\udfad<\/div><div class=\"risk-label\">TACTIC AML.TA0007<\/div><div class=\"risk-title\">Impact<\/div><div class=\"risk-desc\">Degrading model accuracy, inducing targeted misclassification, undermining trust in AI outputs<\/div><\/div>\n<div class=\"risk-cell info\"><div class=\"risk-icon\">\ud83d\udd0d<\/div><div class=\"risk-label\">TACTIC AML.TA0003<\/div><div class=\"risk-title\">Initial Access<\/div><div class=\"risk-desc\">Compromising MLOps pipelines, model registries, training data storage or inference APIs<\/div><\/div>\n<\/div>\n<div class=\"fig-foot\">Source: MITRE ATLAS v4 \u00b7 mitre-atlas.github.io \u00b7 selected tactics most relevant to enterprise AI deployments<\/div>\n<\/div>\n\n<h2>3. Four Critical Attack Vectors in Production<\/h2>\n\n<p>Among the 80+ TTPs documented in MITRE ATLAS, four attack vectors are statistically the most prevalent in enterprise production environments and must be prioritized in any AI risk audit.<\/p>\n\n<div class=\"fig\">\n<div class=\"fig-head\"><span><span class=\"dots\"><span>\u25cf<\/span><span>\u25cf<\/span><span>\u25cf<\/span><\/span>FIGURE 04 \u00b7 FOUR PRIORITY ATTACK VECTORS \u00b7 ENTERPRISE AI PRODUCTION<\/span><span class=\"fig-tag\">CRITICAL THREATS<\/span><\/div>\n<div class=\"attack-grid\">\n<div class=\"atk-card\"><div class=\"atk-id\">AML.T0015 \u00b7 FGSM \/ PGD<\/div><div class=\"atk-name\">Adversarial Examples<\/div><div class=\"atk-desc\">Imperceptible perturbations added to inputs (images, text, tabular data) that cause the model to produce incorrect outputs with high confidence. FGSM (Fast Gradient Sign Method) and PGD (Projected Gradient Descent) are the two most widely used algorithms.<\/div><span class=\"atk-impact critical\">CRITICAL INTEGRITY<\/span><\/div>\n<div class=\"atk-card\"><div class=\"atk-id\">AML.T0020 \u00b7 SUPPLY CHAIN<\/div><div class=\"atk-name\">Data Poisoning<\/div><div class=\"atk-desc\">Injection of corrupted or backdoored samples into the training dataset, causing the model to learn a hidden behavior triggered by specific inputs. Particularly dangerous in federated learning and third-party data acquisition pipelines.<\/div><span class=\"atk-impact critical\">CRITICAL INTEGRITY<\/span><\/div>\n<div class=\"atk-card\"><div class=\"atk-id\">AML.T0037 \u00b7 PRIVACY<\/div><div class=\"atk-name\">Model Inversion<\/div><div class=\"atk-desc\">Reconstruction of sensitive training data (faces, medical records, PII) by querying the model repeatedly and analyzing its outputs. Directly violates GDPR data minimization principles and AI Act transparency requirements.<\/div><span class=\"atk-impact high\">HIGH PRIVACY<\/span><\/div>\n<div class=\"atk-card\"><div class=\"atk-id\">AML.T0051 \u00b7 LLM-SPECIFIC<\/div><div class=\"atk-name\">Prompt Injection<\/div><div class=\"atk-desc\">Malicious instructions embedded in user inputs that override the system prompt of an LLM, causing it to bypass safety guardrails, leak confidential information or execute unauthorized actions. Critical for all GenAI deployments.<\/div><span class=\"atk-impact critical\">CRITICAL INTEGRITY<\/span><\/div>\n<\/div>\n<div class=\"fig-foot\">Source: MITRE ATLAS v4. Technique IDs are indicative; refer to official ATLAS matrix for complete taxonomy<\/div>\n<\/div>\n\n<h2>4. Concrete Sector Cases<\/h2>\n\n<div class=\"case c1\">\n<h4>\ud83c\udfe6 CASE 1. Financial Services: Credit Scoring Under Model Inversion Attack<\/h4>\n<p>A tier-1 bank deploys a gradient boosting model for consumer credit scoring. An adversary with API access conducts a model inversion attack through 50,000 carefully crafted queries over 72 hours. Result: partial reconstruction of training data including income brackets and demographic patterns of 12,000 individuals. A direct GDPR Article 9 breach, undetectable by conventional SIEM tools.<\/p>\n<\/div>\n\n<div class=\"case c2\">\n<h4>\ud83c\udfe5 CASE 2. Healthcare: Adversarial Attack on Medical Imaging<\/h4>\n<p>A radiology AI system for tumor detection is subjected to FGSM perturbations applied to input X-ray images. With perturbations invisible to the human eye (L\u221e norm < 0.01), the model's sensitivity drops from 94% to 31% on perturbed samples. The attack requires no access to model weights, only black-box query access to the inference API, available to any connected diagnostic workstation.<\/p>\n<\/div>\n\n<div class=\"case c3\">\n<h4>\u2713 CASE 3. Successful Remediation: LLM Prompt Injection Containment<\/h4>\n<p>A global consulting firm deploys an internal LLM assistant with access to confidential client documents. Pre-production AI red teaming identifies 14 prompt injection vectors allowing system prompt extraction and document boundary bypassing. Remediation through input sanitization, output filtering and privilege separation reduces the attack surface by 89% before production deployment, avoiding a critical client data breach.<\/p>\n<\/div>\n\n<div class=\"case c4\">\n<h4>\u26a0 CASE 4. Manufacturing: Data Poisoning in Predictive Maintenance<\/h4>\n<p>A smart factory deploys an LSTM model for predictive maintenance of critical equipment. A supply chain compromise introduces 3% poisoned samples into the continuous training pipeline. The backdoored model systematically misclassifies a specific failure pattern as &#8220;nominal operation&#8221; when a trigger signal is present, creating a silent vulnerability exploitable for industrial sabotage.<\/p>\n<\/div>\n\n<h1 style=\"margin-top:64px\">III. Methodology: Mapping Your AI Exposure in 5 Steps<\/h1>\n<div class=\"aiact-divider\"><\/div>\n\n<div class=\"fig\">\n<div class=\"fig-head\"><span><span class=\"dots\"><span>\u25cf<\/span><span>\u25cf<\/span><span>\u25cf<\/span><\/span>FIGURE 05 \u00b7 OVERVIEW \u00b7 5-STEP AI RISK MAPPING METHODOLOGY<\/span><span class=\"fig-tag\">PROCESS<\/span><\/div>\n<div class=\"process5\">\n<div class=\"proc-step\"><div class=\"step-circle s1\">1<\/div><div class=\"name\">Scope &#038; Governance<\/div><div class=\"desc\">Team \u00b7 RACI \u00b7 Boundaries<\/div><\/div>\n<div class=\"proc-arrow\">\u2192<\/div>\n<div class=\"proc-step\"><div class=\"step-circle s2\">2<\/div><div class=\"name\">Model Inventory<\/div><div class=\"desc\">Register \u00b7 Shadow AI \u00b7 Sheets<\/div><\/div>\n<div class=\"proc-arrow\">\u2192<\/div>\n<div class=\"proc-step\"><div class=\"step-circle s3\">3<\/div><div class=\"name\">C-I-D-T-P Classification<\/div><div class=\"desc\">ATLAS \u00b7 Pillars \u00b7 Risk grid<\/div><\/div>\n<div class=\"proc-arrow\">\u2192<\/div>\n<div class=\"proc-step\"><div class=\"step-circle s4\">4<\/div><div class=\"name\">AI Red Teaming<\/div><div class=\"desc\">FGSM \u00b7 PGD \u00b7 Injection<\/div><\/div>\n<div class=\"proc-arrow\">\u2192<\/div>\n<div class=\"proc-step\"><div class=\"step-circle s5\">5<\/div><div class=\"name\">Document &#038; Remediate<\/div><div class=\"desc\">Register \u00b7 XAI \u00b7 Roadmap<\/div><\/div>\n<\/div>\n<div class=\"fig-foot\">Neo Coeur Intelligence methodology. Each step produces auditable deliverables defensible before regulators and insurers<\/div>\n<\/div>\n\n<h2>Step 1. Define Scope and Governance<\/h2>\n<div class=\"step-card sc1\"><div class=\"step-num-box\"><div class=\"n\">1<\/div><div class=\"l\">STEP<\/div><\/div><div class=\"step-body\"><h4>Governance and scope definition<\/h4><p>Identify stakeholders, define the perimeter of the audit, and establish the RACI matrix. This is the indispensable foundation for an exhaustive and non-redundant inventory.<\/p><\/div><\/div>\n\n<p>An AI risk audit that starts without clear governance produces incomplete inventories and unresolved classification conflicts. The first step is to constitute a multidisciplinary committee and formally define the scope before any technical activity begins.<\/p>\n\n<ul class=\"aiact-ul arrow\">\n<li><strong>Audit sponsor:<\/strong> CISO or CRO (Chief Risk Officer) with executive mandate<\/li>\n<li><strong>Data Science \/ MLOps:<\/strong> model architecture, training pipelines, deployment infrastructure<\/li>\n<li><strong>Business units:<\/strong> use cases, decision impact, user populations affected<\/li>\n<li><strong>Legal \/ DPO:<\/strong> GDPR, AI Act, sector-specific regulatory constraints<\/li>\n<li><strong>Cybersecurity \/ Red Team:<\/strong> threat modeling, penetration testing capabilities<\/li>\n<\/ul>\n\n<p>Scope definition must explicitly address three boundaries: <strong>organizational<\/strong> (which business units, subsidiaries, third-party vendors), <strong>technical<\/strong> (which model types: supervised, generative, reinforcement learning), and <strong>temporal<\/strong> (production models only, or including models in development and decommissioned models still accessible).<\/p>\n\n<div class=\"warning-box\">\n<h4>\u2298 CRITICAL: SHADOW AI IS NOT OPTIONAL<\/h4>\n<p>Limiting the inventory to IT-declared systems will miss a significant fraction of your actual exposure. Business teams routinely deploy SaaS AI tools, fine-tuned open-source models and API-connected services without centralized oversight. Shadow AI must be actively discovered through network traffic analysis, SaaS spend audits and business unit questionnaires. Not assumed to be absent.<\/p>\n<\/div>\n\n<h2>Step 2. Inventory All Production AI Models<\/h2>\n<div class=\"step-card sc2\"><div class=\"step-num-box\"><div class=\"n\">2<\/div><div class=\"l\">STEP<\/div><\/div><div class=\"step-body\"><h4>Exhaustive model inventory<\/h4><p>Produce a complete register of all AI systems in production, development and acquisition, including shadow AI and third-party model dependencies.<\/p><\/div><\/div>\n\n<p>The inventory must combine four complementary discovery methods to achieve the exhaustiveness required for a defensible audit:<\/p>\n\n<ol class=\"aiact-ol\">\n<li><strong>Structured business unit questionnaire:<\/strong> standardized form identifying all AI tools in use (automation, NLP, computer vision, recommendation, scoring, generative AI).<\/li>\n<li><strong>Technical infrastructure scan:<\/strong> analysis of model registries (MLflow, SageMaker, Vertex AI), containerized inference services, API gateways and cloud AI service subscriptions.<\/li>\n<li><strong>SaaS and procurement audit:<\/strong> cross-reference of software spend against known AI vendor databases to identify undeclared AI tool usage.<\/li>\n<li><strong>Developer interview series:<\/strong> structured interviews with data scientists and MLOps engineers to surface models built outside formal project governance.<\/li>\n<\/ol>\n\n<p>Each identified system must be documented in a standardized model card covering: model type and architecture, training data provenance, inference API exposure, decision impact scope (how many individuals affected, reversibility of decisions), and current monitoring status.<\/p>\n\n<h2>Step 3. Classify Vulnerabilities by C-I-D-T-P Pillar<\/h2>\n<div class=\"step-card sc3\"><div class=\"step-num-box\"><div class=\"n\">3<\/div><div class=\"l\">STEP<\/div><\/div><div class=\"step-body\"><h4>C-I-D-T-P risk classification<\/h4><p>Map each inventoried model against the five C-I-D-T-P pillars and the relevant MITRE ATLAS TTPs to produce a prioritized risk register.<\/p><\/div><\/div>\n\n<p>Classification is the analytical core of the methodology. For each model in the inventory, the auditor must systematically evaluate exposure across all five C-I-D-T-P dimensions, cross-referenced with the relevant ATLAS TTPs.<\/p>\n\n<div class=\"fig\">\n<div class=\"fig-head\"><span><span class=\"dots\"><span>\u25cf<\/span><span>\u25cf<\/span><span>\u25cf<\/span><\/span>FIGURE 06 \u00b7 C-I-D-T-P CLASSIFICATION GRID \u00b7 MODEL RISK SCORING<\/span><span class=\"fig-tag\">RISK MATRIX<\/span><\/div>\n<div class=\"risk-matrix\">\n<div class=\"risk-cell critical\"><div class=\"risk-icon\">\ud83d\udd12<\/div><div class=\"risk-label\">C \u00b7 CONFIDENTIALITY<\/div><div class=\"risk-title\">Model Extraction Risk<\/div><div class=\"risk-desc\">Is the model API publicly accessible? Does it handle proprietary IP or sensitive training data? Model inversion \/ extraction TTPs applicable.<\/div><\/div>\n<div class=\"risk-cell critical\"><div class=\"risk-icon\">\u26a0<\/div><div class=\"risk-label\">I \u00b7 INTEGRITY<\/div><div class=\"risk-title\">Adversarial Manipulation Risk<\/div><div class=\"risk-desc\">Can model outputs be manipulated by crafted inputs? Is the training pipeline protected against poisoning? FGSM, PGD, backdoor TTPs applicable.<\/div><\/div>\n<div class=\"risk-cell high\"><div class=\"risk-icon\">\ud83d\udeab<\/div><div class=\"risk-label\">D \u00b7 DENIAL<\/div><div class=\"risk-title\">Availability &#038; Evasion Risk<\/div><div class=\"risk-desc\">Can adversarial inputs cause systematic evasion of detection? Is the model robust to distribution shift attacks? Sponge examples, model DoS TTPs.<\/div><\/div>\n<div class=\"risk-cell medium\"><div class=\"risk-icon\">\ud83d\udccb<\/div><div class=\"risk-label\">T \u00b7 TRACEABILITY<\/div><div class=\"risk-title\">Explainability Gap Risk<\/div><div class=\"risk-desc\">Can every model decision be explained and attributed? Are SHAP\/LIME explanations available? XAI coverage and audit log completeness assessment.<\/div><\/div>\n<div class=\"risk-cell high\"><div class=\"risk-icon\">\ud83d\udc64<\/div><div class=\"risk-label\">P \u00b7 PRIVACY<\/div><div class=\"risk-title\">Inference Attack Risk<\/div><div class=\"risk-desc\">Is the model vulnerable to membership inference? Can individuals be re-identified from model outputs? GDPR and AI Act privacy compliance exposure.<\/div><\/div>\n<div class=\"risk-cell neutral\"><div class=\"risk-icon\">\ud83d\udcca<\/div><div class=\"risk-label\">AGGREGATE SCORE<\/div><div class=\"risk-title\">Composite Risk Level<\/div><div class=\"risk-desc\">Weighted average of C-I-D-T-P scores, adjusted for business criticality and regulatory exposure, produces the audit priority ranking.<\/div><\/div>\n<\/div>\n<div class=\"fig-foot\">Neo Coeur Intelligence C-I-D-T-P scoring grid. Scores from 1 (negligible) to 5 (critical) per pillar<\/div>\n<\/div>\n\n<h2>Step 4. Conduct AI Red Teaming<\/h2>\n<div class=\"step-card sc4\"><div class=\"step-num-box\"><div class=\"n\">4<\/div><div class=\"l\">STEP<\/div><\/div><div class=\"step-body\"><h4>AI red teaming and adversarial testing<\/h4><p>Empirically validate classification findings through structured adversarial testing of priority models. Moving from theoretical vulnerability assessment to confirmed attack surface measurement.<\/p><\/div><\/div>\n\n<p>Classification identifies potential exposure. Red teaming confirms actual exploitability. For each model classified as high or critical risk in Step 3, the following adversarial testing protocol should be applied proportionally to risk level and model accessibility:<\/p>\n\n<ul class=\"aiact-ul arrow\">\n<li><strong>Black-box adversarial testing:<\/strong> FGSM and PGD attacks using only inference API access. This simulates the most realistic attacker scenario with no model internals access.<\/li>\n<li><strong>White-box testing (where accessible):<\/strong> gradient-based attacks with full model access. This produces worst-case vulnerability bounds for internal models.<\/li>\n<li><strong>Data poisoning simulation:<\/strong> inject controlled poisoned samples into the training pipeline and measure model behavior degradation. Critical for continuously trained models.<\/li>\n<li><strong>Prompt injection assessment (LLMs):<\/strong> structured testing of system prompt extraction, jailbreaking, indirect injection via retrieved documents, and tool-use exploitation.<\/li>\n<li><strong>Membership inference testing:<\/strong> statistical attack to determine whether specific individuals&#8217; data was used in training. Direct privacy compliance validation.<\/li>\n<\/ul>\n\n<p>AI red teaming results must be quantified (attack success rate, minimum perturbation required, number of queries needed for extraction) to produce actionable metrics for remediation prioritization and risk acceptance decisions.<\/p>\n\n<h2>Step 5. Document, Remediate and Maintain<\/h2>\n<div class=\"step-card sc5\"><div class=\"step-num-box\"><div class=\"n\">5<\/div><div class=\"l\">STEP<\/div><\/div><div class=\"step-body\"><h4>Documentation, remediation roadmap and continuous governance<\/h4><p>Transform audit findings into a formal risk register, a prioritized remediation roadmap and a continuous monitoring framework. Making AI security an ongoing operational practice.<\/p><\/div><\/div>\n\n<p>An audit that produces a report filed away in a shared drive has zero security value. Step 5 converts findings into operational governance through three parallel workstreams:<\/p>\n\n<p><strong>Formal documentation:<\/strong> Each confirmed vulnerability is documented with its ATLAS TTP identifier, C-I-D-T-P pillar, confirmed attack success rate, affected model(s), business impact assessment, and remediation owner. This register serves as the primary artifact for regulatory audits, cyber insurance assessments and board-level AI risk reporting.<\/p>\n\n<p><strong>Prioritized remediation roadmap:<\/strong> Remediation actions are ranked by risk score and implementation effort. Quick wins (input validation hardening, output filtering for LLMs, API rate limiting) are separated from structural remediations (adversarial training, differential privacy implementation, model architecture changes) requiring longer timelines.<\/p>\n\n<p><strong>Continuous monitoring framework:<\/strong> One-time audits provide a snapshot. Production AI systems require ongoing monitoring through: adversarial input detection in inference logs, training data integrity checksums, model performance drift monitoring (a potential indicator of poisoning), and scheduled red teaming cycles aligned with model retraining cadence.<\/p>\n\n<h2>AI Risk Audit Checklist<\/h2>\n\n<div class=\"checklist-box\">\n<h3>\u2713 COMPLETE AI RISK AUDIT, MINIMUM VIABLE CHECKLIST<\/h3>\n<ul>\n<li>Governance committee constituted with RACI matrix validated by CISO\/CRO<\/li>\n<li>Audit scope formally defined: organizational, technical and temporal boundaries<\/li>\n<li>Shadow AI discovery protocol executed (network audit + SaaS spend + BU questionnaires)<\/li>\n<li>Complete model inventory produced with standardized model cards for all systems<\/li>\n<li>Each model classified against all five C-I-D-T-P pillars with ATLAS TTP cross-reference<\/li>\n<li>Business criticality and regulatory exposure factored into composite risk scoring<\/li>\n<li>Black-box adversarial testing conducted on all HIGH and CRITICAL classified models<\/li>\n<li>Prompt injection assessment completed for all LLM and GenAI deployments<\/li>\n<li>Membership inference attack testing conducted for models trained on personal data<\/li>\n<li>Data poisoning exposure assessed for all continuously trained or federated models<\/li>\n<li>XAI (explainability) coverage verified, SHAP\/LIME available for all decision-impacting models<\/li>\n<li>Confirmed vulnerabilities documented with ATLAS TTP IDs and quantified attack success rates<\/li>\n<li>Prioritized remediation roadmap approved with owners, timelines and success metrics<\/li>\n<li>Continuous monitoring framework deployed: drift detection, log anomaly, integrity checks<\/li>\n<li>Next scheduled red teaming cycle dated and resourced<\/li>\n<\/ul>\n<\/div>\n\n<h1 style=\"margin-top:64px\">IV. Conclusion<\/h1>\n<div class=\"aiact-divider\"><\/div>\n\n<p>The 80% of production AI models that have never been adversarially tested are not a statistic about negligence. They are a statistic about a discipline that did not formally exist five years ago. MITRE ATLAS, the C-I-D-T-P framework and structured AI red teaming methodologies have matured sufficiently to make systematic AI risk auditing both operationally feasible and strategically essential.<\/p>\n\n<p>The convergence of three forces makes 2026 the inflection point: the August 2, 2026 AI Act deadline for high-risk system obligations, the rapid commoditization of adversarial attack tools reducing the skill barrier for attackers, and the growing appetite of cyber insurers for quantified AI security posture evidence before issuing AI-related coverage.<\/p>\n\n<p>The five-step methodology presented in this article, from governance and scope definition through AI red teaming and continuous monitoring, provides a structured path from current exposure to defensible risk management. The organizations that execute this mapping before an incident occurs will be structurally better positioned than those that conduct it as a post-breach remediation exercise.<\/p>\n\n<p>Your models are probably vulnerable. The only question is whether you know exactly how, and to what degree.<\/p>\n\n<div class=\"cta-box\">\n<h3>Assess My AI Risk<\/h3>\n<p>You&#8217;ve read the methodology. Now apply it to your models.<\/p>\n<p>Our AI Risk Assessment service delivers a complete C-I-D-T-P audit of your production AI systems, from shadow AI discovery through adversarial red teaming, with a prioritized remediation roadmap defensible before regulators and insurers.<\/p>\n<a href=\"\/assess-my-ai-risk\/\" class=\"cta-btn\">Start My AI Risk Assessment \u2192<\/a>\n<\/div>\n\n<h1 style=\"margin-top:64px\">V. References<\/h1>\n<div class=\"aiact-divider\"><\/div>\n\n<div class=\"refs\">\n<p><strong>[1]<\/strong> MITRE Corporation. <em>MITRE ATLAS, Adversarial Threat Landscape for Artificial-Intelligence Systems, v4.<\/em> Available at: <a href=\"https:\/\/atlas.mitre.org\" target=\"_blank\">atlas.mitre.org<\/a><\/p>\n<p><strong>[2]<\/strong> Gartner, Inc. <em>AI Security Survey 2025: State of Adversarial Testing in Enterprise ML.<\/em> Gartner Research, 2025.<\/p>\n<p><strong>[3]<\/strong> Goodfellow, I.J., Shlens, J., Szegedy, C. <em>Explaining and Harnessing Adversarial Examples.<\/em> ICLR 2015. arXiv:1412.6572.<\/p>\n<p><strong>[4]<\/strong> Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A. <em>Towards Deep Learning Models Resistant to Adversarial Attacks (PGD).<\/em> ICLR 2018. arXiv:1706.06083.<\/p>\n<p><strong>[5]<\/strong> Fredrikson, M., Jha, S., Ristenpart, T. <em>Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures.<\/em> CCS 2015.<\/p>\n<p><strong>[6]<\/strong> Shokri, R., Stronati, M., Song, C., Shmatikov, V. <em>Membership Inference Attacks Against Machine Learning Models.<\/em> IEEE S&#038;P 2017.<\/p>\n<p><strong>[7]<\/strong> Perez, F., Ribeiro, M. <em>Ignore Previous Prompt: Attack Techniques for Language Models.<\/em> NeurIPS 2022 Workshop on ML Safety.<\/p>\n<p><strong>[8]<\/strong> Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). OJ L, 12.7.2024.<\/p>\n<p><strong>[9]<\/strong> European Commission. <em>Guidelines on the classification of AI systems as high-risk under Article 6 of the AI Act.<\/em> Published May 19, 2026.<\/p>\n<p><strong>[10]<\/strong> NIST. <em>AI Risk Management Framework (AI RMF 1.0).<\/em> National Institute of Standards and Technology, January 2023.<\/p>\n<\/div>\n\n<\/div>\n<\/div>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>ART-AI-RISK \u00b7 Neo Coeur Intelligence \u00b7 Published: July 2026 \u00b7 Based on MITRE ATLAS v4&hellip;<\/p>","protected":false},"author":4,"featured_media":26723,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[107],"tags":[],"class_list":["post-26714","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-risk-management"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI Risk Assessment - Adversarial AI: the Methodology to Map Your Exposure - NEO COEUR INTELLIGENCE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/neocoeurintelligence.com\/fr\/ai-risk-assessment-adversarial-ai-the-methodology-to-map-your-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:url\" content=\"https:\/\/neocoeurintelligence.com\/fr\/ai-risk-assessment-adversarial-ai-the-methodology-to-map-your-exposure\/\" \/>\n<meta property=\"og:site_name\" content=\"NEO COEUR INTELLIGENCE\" \/>\n<meta property=\"article:published_time\" content=\"2026-07-05T09:30:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-13T13:22:26+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/neocoeurintelligence.com\/wp-content\/uploads\/2026\/06\/VISUELBLOOG-1024x576.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"576\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Herman NOUBOUM NOUBI\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@https:\/\/x.com\/NeoCoeurAII\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Herman NOUBOUM NOUBI\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"20 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/neocoeurintelligence.com\\\/ai-risk-assessment-adversarial-ai-the-methodology-to-map-your-exposure\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neocoeurintelligence.com\\\/ai-risk-assessment-adversarial-ai-the-methodology-to-map-your-exposure\\\/\"},\"author\":{\"name\":\"Herman NOUBOUM NOUBI\",\"@id\":\"https:\\\/\\\/neocoeurintelligence.com\\\/#\\\/schema\\\/person\\\/862d5ea0e68544036a095e14d8d6d43b\"},\"headline\":\"AI Risk Assessment &#8211; 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