AI Security 101: A Beginner’s Guide to Mapping and Securing the AI Attack Surface

Read full article from Wiz here: https://www.wiz.io/blog/ai-attack-surface/?utm_source=nhimg

Artificial Intelligence is redefining how organizations build, deploy, and interact with software — but it’s also reshaping the threat landscape. As enterprises accelerate their adoption of generative AI, large language models (LLMs), and custom machine learning pipelines, they are simultaneously expanding a new kind of risk perimeter: the AI attack surface.

The AI attack surface encompasses every entry point, process, and dependency that could be exploited in an AI ecosystem. This includes training data, model artifacts, APIs, orchestration pipelines, and user interfaces — all of which can expose sensitive data or become vectors for manipulation if not properly secured. Unlike traditional cloud vulnerabilities, AI introduces a dynamic, interconnected attack surface that evolves with model retraining, data updates, and third-party integrations.

How the AI Attack Surface Differs from Traditional Risks

AI systems inherit all the classic cloud risks — misconfigurations, exposed services, and unpatched components — but also introduce novel threats unique to machine learning environments. These include prompt injection, data leakage, model poisoning, and shadow AI, where teams deploy unauthorized AI tools outside established security controls. The result is a sprawling and unpredictable risk surface that traditional identity, access, and configuration management systems weren’t designed to handle.

For instance, an AI API endpoint can become a target for prompt injection attacks, while shared cloud storage used in model training might leak proprietary data. These vulnerabilities demonstrate how AI risks bridge development, infrastructure, and business layers — demanding unified visibility and governance.

Anatomy of the AI Attack Surface

A comprehensive view of AI risk includes:

• Training Data: Sensitive datasets may leak personal or proprietary information if not sanitized or access-controlled.
• Model Artifacts: Trained models can inadvertently memorize secrets, expose internal logic, or be repurposed insecurely.
• AI Pipelines: Platforms like MLflow, Amazon SageMaker, and Vertex AI automate the ML lifecycle but introduce complex permission and configuration chains.
• APIs and Interfaces: Open model endpoints, if unprotected, enable exploitation through prompt injection and data extraction.
• Shadow AI: Unmonitored usage of AI tools by developers or business units creates blind spots for governance and compliance.

These interconnected layers form a multi-dimensional attack surface where exploitation in one domain — such as a compromised pipeline — can cascade into others, leading to systemic breaches.

Real-World Examples of AI Security Incidents

Recent cloud and AI-related exposures illustrate the impact of these risks:

• Microsoft 38TB Exposure: A misconfigured SAS token leaked 38 terabytes of internal AI training data, credentials, and private documentation.
• BingBang Vulnerability: Discovered by Wiz Research, this issue allowed attackers to manipulate Bing’s AI output through prompt injection, demonstrating the real-world consequences of insecure LLM endpoints.
• Storm-0558 Breach: Though not AI-specific, the compromise of signing keys showed how large-scale identity and key management failures can expose AI workloads across environments.

Each case reinforces a central truth: AI security is inseparable from data, identity, and infrastructure security.

Practical Steps to Reduce AI Risk

Building resilient AI systems starts with visibility and shared ownership. Organizations should:

1. Map AI Usage: Identify all AI services, pipelines, and tools in use — including unsanctioned ones.
2. Secure Training Data: Audit data sources for sensitive information and enforce least privilege access.
3. Harden ML Infrastructure: Apply DevSecOps principles — scan, monitor, and restrict permissions for AI environments.
4. Monitor AI Endpoints: Treat LLM interfaces and APIs as critical assets, with continuous monitoring and abuse detection.
5. Foster Shared Accountability: Align security, DevOps, data, and business teams around AI risk governance.

How Wiz Secures the AI Attack Surface

Traditional tools often analyze security “vertically” — focusing on isolated layers like infrastructure or code. Wiz, by contrast, offers a horizontal, end-to-end security model spanning from source code to runtime. It enables real-time discovery, correlation, and prioritization of AI-related risks across cloud environments.

Key capabilities include:

• Automated Discovery: Detects unmanaged AI assets, shadow models, and unsanctioned APIs using AI-BOM (AI Bill of Materials).
• Contextual Risk Mapping: The Wiz Security Graph links misconfigurations (e.g., open buckets or exposed endpoints) with identity and network context to reveal true attack paths.
• Prioritized Remediation: Filters out noise to highlight vulnerabilities with real impact potential.
• AI Security Posture Management (AI-SPM): Delivers continuous governance and compliance monitoring across the entire AI lifecycle.

This unified visibility helps organizations move fast without compromising control — giving teams the insight to detect, respond, and prevent AI-related threats before they escalate.

The Future of AI Security

AI is accelerating faster than security practices can adapt. The organizations that will thrive are those that treat AI security as a shared, continuous discipline — embedding visibility, governance, and collaboration into every phase of their AI development lifecycle. By adopting platforms like Wiz, enterprises can transform AI risk from a blind spot into a managed, measurable component of their overall cloud security posture.