AI security is the set of practices and tools used to protect AI systems, including machine learning models and LLMs, from attacks such as data poisoning, prompt injection, and model theft. It covers the full lifecycle from training data through deployed agents.

What are the biggest threats to AI systems?

The most common threats are training data poisoning, adversarial inputs that cause wrong outputs, prompt injection attacks that hijack LLM behavior, and data leakage through model outputs. AI agents that call external tools also introduce risks around excessive permissions and identity spoofing.

How is AI security different from traditional cybersecurity?

Traditional cybersecurity tools protect code, networks, and endpoints. AI security tools protect constructs that are unique to AI: model weights, training datasets, embedding spaces, and prompt interfaces. Standard firewalls and SAST scanners do not inspect these.

Do I need AI security if I only use third-party AI APIs?

Yes. Even when using a third-party LLM API, your application can be vulnerable to prompt injection, data leakage through model responses, and insecure agent permissions. AI security controls apply at the integration layer, not just the model itself.

What is AI Security?

AI Security is the practice of protecting artificial intelligence systems, machine learning models, large language models, and AI-powered applications from cyber threats. It covers the full lifecycle: training data, model weights, inference endpoints, and AI agents.

What it does

AI Security tools address threats that are specific to AI systems and do not fit neatly into traditional application security. They perform tasks such as:

Scanning training datasets for poisoned or manipulated samples
Testing models with adversarial inputs and red-team prompts
Detecting prompt injection attacks at inference time
Enforcing policies on what data an LLM can return to a user
Issuing and validating hardware-anchored identities for AI agents
Logging every human-to-AI interaction for audit purposes
Rotating or scoping the tokens that AI agents use to call external APIs

Why teams buy it

AI systems introduce attack surfaces that standard web application firewalls and endpoint agents do not cover. A poisoned training dataset can corrupt a model silently. A prompt injection can cause an LLM to exfiltrate data or take unintended actions. Regulators in the EU, US, and elsewhere are beginning to require documented controls over AI systems. Security teams buy AI Security tools to close these gaps before models reach production and to satisfy audit requirements.

What to look for

When evaluating tools in this category, consider which part of the AI lifecycle you need to cover:

Training phase: data poisoning detection, dataset integrity checks
Model phase: adversarial robustness testing, model red-teaming, weight protection
Inference phase: prompt injection detection, output filtering, data leakage prevention
Agent phase: identity management, scoped permissions, continuous behavioral monitoring
Governance: policy enforcement, audit trails, compliance reporting

What is AI Security?

What it does

Why teams buy it

What to look for

Common confusions

AI Security tools

Frequently asked questions