AI Glossary
AI Security
The practice of protecting AI systems, models, data, and infrastructure from threats and misuse.
AI Security
Overview
Artificial intelligence systems create new opportunities.
They also create new risks.
As AI becomes more integrated into business operations, organizations must think carefully about how these systems are protected.
AI Security refers to the practices used to protect AI models, data, infrastructure, applications, and users from threats, attacks, and misuse.
A helpful way to think about AI security is protecting both a building and everything inside it.
The structure must be secure.
The information must be secure.
And the people using it must be protected as well.
AI security extends beyond traditional cybersecurity because organizations must also consider risks that are unique to AI systems.
Model manipulation.
Prompt injection.
Data poisoning.
Unauthorized access.
Adversarial attacks.
These are examples of challenges that can affect AI environments.
As AI adoption grows, security is becoming an increasingly important part of responsible AI development and deployment.
Why It Matters
AI security helps organizations protect information, systems, and users while reducing risk.
Real-World Example
A company may implement access controls and monitoring systems to prevent unauthorized use of internal AI tools.