AI Security 101 — Mapping the AI Attack Surface
A practical guide to the risks, blind spots, and protections every security team needs to know.
Intro — why AI changes the security game
AI is different not because it removes old risks — it layers new ones on top of them. When teams rapidly adopt foundation models, custom training pipelines, and public APIs, organizations suddenly have new classes of assets to track and protect. These assets are frequently owned by different teams, deployed quickly, and often left outside traditional security controls, creating blind spots that adversaries can exploit.
What is the AI attack surface?
The AI attack surface is the collection of all ways an adversary can compromise or extract value from an AI system. It spans:
Training data — the source data used to train models (sensitive PII, proprietary IP, secrets). If training data is exposed or inadvertently memorized, models can leak it.
Model artifacts — the model weights, checkpoints, and exported packages themselves — which can be stolen, probed for memorized content, or tampered with.
AI pipelines & orchestration — e.g., MLflow, SageMaker, Vertex AI, CI/CD for models — often require elevated privileges and complex configs. Misconfigurations here create broad exposure.
APIs & interfaces — public-facing LLM endpoints, chat UIs, or internal model endpoints that can be manipulated (prompt injection, malicious inputs).
Shadow AI — unsanctioned or developer-spun-up AI services outside governance (“shadow AI”) that security teams don’t see.
How AI risk differs from traditional cloud risk
Traditional cloud security looks for exposed services, misconfigured storage, and over-permissive identities. AI adds new failure modes: prompt injection, training-data leakage, model poisoning, and hidden model behavior that can arise from seemingly harmless data. The same cloud misconfiguration (e.g., an open blob/container) becomes an AI-specific problem when that storage contains training corpora or model artifacts.
Real-world failures — why this matters
These are not hypothetical risks. Recent examples include large data exposures caused by misconfigured storage and prompt-injection style vulnerabilities in public-facing AI features. Such incidents show how quickly AI additions can amplify the blast radius of conventional cloud mistakes.
Five practical steps to reduce your AI risk
Below are concrete, actionable measures any organization can start implementing today.
Map your environment (discover & inventory)
Inventory every model, dataset, API, and pipeline. Include “shadow AI” — scripts, notebooks, SaaS-hosted model endpoints spun up by teams. Consider creating an AI Bill of Materials (AI-BOM) to capture dependencies, data sources, owners, and locations.
Secure training data
Treat your training corpus like any sensitive datastore: strong access controls, encryption at rest/in transit, logging, and data classification. Remove PII or use synthetic/filtered datasets where possible. Audit data before it enters training.
Harden ML infrastructure
Apply least-privilege to pipeline roles, patch and scan model platforms (SageMaker, Vertex, etc.), and monitor for unusual privilege escalations. Include model stores, artifact registries, and compute instances in your standard hardening playbook.
Treat inference endpoints as sensitive
Rate-limit, authenticate, and monitor LLM endpoints. Detect anomalous prompt patterns or repeated probing attempts. Implement input/output filtering (where appropriate) and keep an auditable trail of interactions.
Create shared ownership & governance
Security, data science, dev, and cloud teams must share responsibility. Define policies for model development, deployment, and retirement. Use guardrails — e.g., data use policies, model risk checklists, and a lightweight approval process for production model rollouts.
Operational playbook (quick checklist)
Inventory: AI-BOM for every application.
Data controls: deploy DLP-like scans for corpora.
Pipeline hardening: RBAC, patching, secrets rotation.
Endpoint monitoring: WAF/rate-limiting, anomaly detection, logging.
Governance: model registry, owner tagging, retention & deletion policies.
Where product solutions (like Wiz) help
Solutions that provide horizontal visibility across cloud, identity, and AI layers can reduce blind spots. In the Wiz approach, an “AI security graph” connects AI-specific misconfigurations (e.g., exposed training buckets or endpoints) to identity and network context, surfacing real attack paths worth fixing first. If you use or evaluate AI-SPM tooling, look for: automated discovery of unmanaged models, context-aware prioritization (not just noisy alerts), and centralized AI governance features.