Introduction

Your AI models are probably vulnerable to FGSM, PGD or prompt injection. And you don’t know it. This is not a provocative claim, it is a statistical reality confirmed by Gartner in 2025: 80% of AI models deployed in production have never been subjected to adversarial security testing. 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.

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?

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.

I. General Overview: AI Risk Assessment and Adversarial AI

1. Definition and Foundations

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.

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.

Adversarial AI 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.

2. The Scale of the Problem: Key Figures

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.

II. Detailed Analysis: Threat Landscape

1. The 5 C-I-D-T-P Pillars of AI Risk

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.

2. MITRE ATLAS: The 80+ TTPs You Need to Know

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.

3. Four Critical Attack Vectors in Production

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.

4. Concrete Sector Cases

III. Methodology: Mapping Your AI Exposure in 5 Steps

Step 1. Define Scope and Governance

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.

• Audit sponsor: CISO or CRO (Chief Risk Officer) with executive mandate
• Data Science / MLOps: model architecture, training pipelines, deployment infrastructure
• Business units: use cases, decision impact, user populations affected
• Legal / DPO: GDPR, AI Act, sector-specific regulatory constraints
• Cybersecurity / Red Team: threat modeling, penetration testing capabilities

Scope definition must explicitly address three boundaries: organizational (which business units, subsidiaries, third-party vendors), technical (which model types: supervised, generative, reinforcement learning), and temporal (production models only, or including models in development and decommissioned models still accessible).

Step 2. Inventory All Production AI Models

The inventory must combine four complementary discovery methods to achieve the exhaustiveness required for a defensible audit:

1. Structured business unit questionnaire: standardized form identifying all AI tools in use (automation, NLP, computer vision, recommendation, scoring, generative AI).
2. Technical infrastructure scan: analysis of model registries (MLflow, SageMaker, Vertex AI), containerized inference services, API gateways and cloud AI service subscriptions.
3. SaaS and procurement audit: cross-reference of software spend against known AI vendor databases to identify undeclared AI tool usage.
4. Developer interview series: structured interviews with data scientists and MLOps engineers to surface models built outside formal project governance.

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.

Step 3. Classify Vulnerabilities by C-I-D-T-P Pillar

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.

Step 4. Conduct AI Red Teaming

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:

• Black-box adversarial testing: FGSM and PGD attacks using only inference API access. This simulates the most realistic attacker scenario with no model internals access.
• White-box testing (where accessible): gradient-based attacks with full model access. This produces worst-case vulnerability bounds for internal models.
• Data poisoning simulation: inject controlled poisoned samples into the training pipeline and measure model behavior degradation. Critical for continuously trained models.
• Prompt injection assessment (LLMs): structured testing of system prompt extraction, jailbreaking, indirect injection via retrieved documents, and tool-use exploitation.
• Membership inference testing: statistical attack to determine whether specific individuals’ data was used in training. Direct privacy compliance validation.

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.

Step 5. Document, Remediate and Maintain

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:

Formal documentation: 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.

Prioritized remediation roadmap: 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.

Continuous monitoring framework: 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.

AI Risk Audit Checklist

IV. Conclusion

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.

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.

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.

Your models are probably vulnerable. The only question is whether you know exactly how, and to what degree.

V. References