# What is AI Security?

> AI security is the discipline of protecting artificial intelligence systems and the environments around them from malicious manipulation, unauthorized access, disclosure, theft and disruption.

- Canonical URL: https://yellowcube.eu/glossary/ai-security/
- Publisher: Yellow Cube
- Language: en
- Contact: hello@yellowcube.eu

## Content

Its scope includes training and evaluation data, models and weights, code, prompts, retrieval stores, development pipelines, identities, infrastructure, interfaces, connected tools and the decisions or actions an AI-enabled application can produce.

The work extends established cybersecurity practices across the AI lifecycle while addressing attack paths created or amplified by machine learning. A sound program starts with the system’s purpose, assets, actors and trust boundaries, then selects controls according to realistic consequences. It protects confidentiality, integrity and availability without assuming that a well-performing model or a reputable provider makes the complete application secure.

### Key points

- **Lifecycle scope:** Assess acquisition, data preparation, training or customization, evaluation, deployment, operation, change, incident response and retirement — not only the public model endpoint.
- **Core controls:** Maintain an asset inventory; verify provenance; apply least privilege, secure development and supply-chain controls; isolate sensitive resources; validate outputs before use; and monitor both conventional and AI-specific abuse.
- **Assurance evidence:** Combine threat modeling, security testing, adversarial evaluation, access and configuration review, incident exercises, monitoring and retesting after material changes.
- **Important limitation:** AI security is not a synonym for AI safety or overall trustworthiness. A system can resist attackers yet still be inaccurate, unfair, unsafe in its intended context or poorly governed, and no single gateway or scanner covers the full risk.

### Related terms

[Adversarial machine learning](<https://yellowcube.eu/glossary/adversarial-machine-learning/>) · [AI red teaming](<https://yellowcube.eu/glossary/ai-red-teaming/>) · [Prompt injection](<https://yellowcube.eu/glossary/prompt-injection/>) · [AI risk management](<https://yellowcube.eu/glossary/ai-risk-management/>) · [AI in cybersecurity](<https://yellowcube.eu/glossary/ai-in-cybersecurity/>) · [Secure by design](<https://yellowcube.eu/glossary/secure-by-design/>) · [Machine learning security operations (MLSecOps)](<https://yellowcube.eu/glossary/machine-learning-security-operations/>) · [AI governance](<https://yellowcube.eu/glossary/ai-governance/>)

### Sources

[NSA Artificial Intelligence Security Center](https://www.nsa.gov/AISC/) · [NIST AI Risk Management Framework 1.0](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10) · [NIST AI 600-1, Generative AI Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence)

## Attribution and scope

This Markdown representation is generated from the same approved content records as the canonical HTML page. Cite the canonical URL above when referencing this material. Product and service descriptions are informational; confirm project-specific requirements with Yellow Cube.