AI Glossary
Secure AI Development
The practice of incorporating security principles throughout the AI development lifecycle.
Secure AI Development
Overview
Security is most effective when it is considered from the beginning.
Waiting until a system is deployed often makes security issues more difficult and expensive to address.
This principle applies to artificial intelligence just as it does to traditional software.
Secure AI Development refers to the practice of integrating security considerations throughout the AI lifecycle, from design and development to deployment and ongoing maintenance.
A helpful way to think about secure development is constructing a building.
It is easier to include safety features during construction than to add them after the building is complete.
Similarly, AI systems benefit when security is built into the development process rather than added later.
Secure AI development may include risk assessments, security testing, access controls, monitoring, governance reviews, and threat modeling.
Organizations that adopt secure development practices are often better positioned to identify and address issues before they become larger problems.
Why It Matters
Secure AI development helps reduce vulnerabilities and improve the reliability of AI systems.
Real-World Example
A company may perform security reviews and threat assessments before deploying an AI-powered application.