AI security is the practice of protecting artificial intelligence systems, including the models, the data used to train and ground them, the prompts and responses that pass through them, and the tools and data sources AI agents can reach, from attack, manipulation, misuse and data leakage. It applies whether an organization builds its own AI applications or uses AI features inside software it buys.
At a glance
- AI security means securing AI systems; it is different from using AI to improve security, though vendors often blur the two.
- AI applications add new attack paths, such as prompt injection, alongside familiar ones like stolen credentials and data exposure.
- Risk grows with access: AI agents that can read data, call APIs or take actions need the same least-privilege thinking as people and service accounts.
- Controls usually combine existing security basics with AI-specific testing, monitoring and filtering.
- It operates within broader AI governance, which also covers legal, ethical and business risk.
What problem it solves
Organizations are putting large language models (LLMs) into customer chatbots, internal assistants and automated workflows, and employees are adopting AI tools on their own. These systems behave differently from traditional software. They follow instructions written in plain language, which means attackers can try to slip in their own instructions through a web page, email or document the AI reads. They may repeat sensitive data from their training or reference material. And when connected to company systems, they can act at machine speed with whatever permissions they were given.
Traditional security programs weren’t designed with these behaviors in mind. AI security extends the program to cover them: knowing where AI is in use, controlling what it can access, testing how it fails, and watching how it behaves in production.
How it works
Inventory and discovery. The first step is knowing where AI is used: approved platforms, AI features in SaaS tools, internally built applications, and shadow AI that staff adopted without review. Some tools discover AI usage from network, browser or SaaS data.
Data protection. Policies and controls decide what data may be sent to which AI services. Data loss prevention (DLP), data classification and contract terms with AI providers about data use and retention all play a part.
Access and agent controls. AI applications and agentic AI systems get their own identities with least-privilege access, approvals for sensitive actions, and limits on which tools and data they can call. This treats agents much like other non-human identities.
Application and model protection. Builders test AI applications for prompt injection, data leakage and unsafe outputs, often through AI-focused red teaming. Some add input and output filters, check third-party models and components before use, and protect training and reference data from tampering.
Monitoring and response. Prompts, responses and agent actions are logged where privacy rules allow, so unusual behavior can be detected and investigated, and incident response plans cover AI-specific scenarios.
Frameworks. Many organizations structure the work using published guidance, such as the NIST AI Risk Management Framework (AI RMF) and industry lists of common AI application risks.
When it matters for buyers
- When deploying a customer-facing chatbot or assistant. Public-facing AI is exposed to anyone who wants to manipulate it.
- When connecting AI to company data or giving agents the ability to act. Access is where the largest risks usually sit.
- When buying software with embedded AI features. Ask how the vendor secures them and what happens to your data.
- When staff are already using AI tools. Discovery and clear policy usually come before new technology.
- When customers, auditors or insurers ask. Questionnaires increasingly include AI-specific questions.
For help choosing AI platforms and providers, see our artificial intelligence overview.
Questions to ask vendors
- What happens to our prompts, files and outputs: are they stored, for how long, and are they used for training?
- How do you defend against prompt injection, and how do you test for it?
- What permissions do your AI features or agents run with, and can we restrict them?
- What logs of prompts, responses and agent actions can we access for investigation?
- Which third-party models and components do you rely on, and how do you assess them?
- Has the AI functionality been independently tested, and can we see a summary?
- How do you notify customers of AI-related security incidents?
How it differs from AI for security
The two phrases sound alike but point in opposite directions. AI security, sometimes called security for AI, protects AI systems from being attacked, manipulated or leaking data. AI for security uses AI to make defense better, for example summarizing alerts in a security operations center, spotting fraud or helping analysts investigate incidents. A security product that uses AI does not by itself secure your AI applications, and a tool that secures your AI may use no AI at all. When a vendor says it offers “AI security”, ask which of the two it means. Many organizations need both, and the AI-for-security tools themselves are AI systems that need securing.