AI Security.

Prevent unauthorized API actions

An agent calls APIs the user never authorized.

Prevent prompt injection from triggering tools

Untrusted text in a page or file makes the agent run a tool.

Restrict agent access to sensitive databases

The agent can read or write records outside its task.

Detect malicious tool use

Tool calls that leak data or change systems look like normal work.

Resist adversarial inputs

Small changes to an input flip the model decision.

Prevent model extraction

Repeated queries let an outsider copy the model behaviour.

Prevent model manipulation

Training data or updates change the model toward an attacker goal.

Prevent jailbreaking

A crafted prompt gets the model to ignore its safety rules.

Prevent data exfiltration

The system sends private data out through a reply, a tool, or a log.

Prevent supply-chain attacks

A dependency, dataset, or plugin changes the system before it ships.

Detect compromised AI pipelines

Training or deployment steps are altered, so the shipped system is not the one that was reviewed.

Prevent unauthorized model access

People or services reach a model they were not allowed to use.

Verify agent identity

A caller can act as an agent without proving which agent it is.

Manage agent permissions

Agent access stays broad after the task that needed it is done.

Prevent privilege escalation

The agent gains rights beyond the account it was given.

Implement least-privilege AI access

The agent holds permissions for data and tools it does not need.

Secure model hosting

The host that serves the model can be reached or changed by outsiders.

Protect inference infrastructure

Requests, logs, or weights on the inference path are exposed.

Secure model updates

An update replaces the model without a check that it is the intended one.

Detect compromised AI infrastructure

A breached host, key, or pipeline keeps serving traffic unnoticed.