Prompt injection. Compliance breaks. Agents acting beyond their remit. AI does not just leak data, it can be manipulated and misused. Anzenna detects prompt injection, compliance violations, and risky agent behavior with the context to act before harm is done.
AI tools introduce failure modes traditional security never had to model. A poisoned document triggers prompt injection. A copilot is steered into a compliance violation. An autonomous agent takes an action far beyond what anyone intended.
Point tools see fragments. A prompt firewall inspects one model’s traffic. It has no view of the identity behind the request, the data the agent can reach, or whether the behavior is normal for that user.
Preventing misuse means reasoning about the whole picture: the actor, the tool, the data, and the intent, together.
An MCP server can be approved on Monday and behave like an exfiltration tool on Thursday. Anzenna learns what normal looks like for each agent and the person running it, then weighs live behavior against that baseline, so manipulation and drift surface as they happen.
Misuse is rarely one event. Anzenna reads the actor, the tool, and the intent together.
A poisoned document triggers prompt injection, steering an assistant off task.
A copilot is pushed into handling regulated data it was never cleared for.
An autonomous agent takes write actions far beyond its intended remit.
A confirmed signal opens a case the agent has already reasoned through.
Anzenna grounds AI behavior in the same behavioral graph it uses for insider risk, so misuse surfaces as a reasoned case, not an isolated signal.
Learn what normal AI usage looks like per person, role, and agent across your stack over a rolling behavioral window.
Flag prompt injection, jailbreak patterns, and agents taking actions inconsistent with their purpose or the user’s intent.
Surface usage that breaks policy or regulatory boundaries, with the data and identity context regulators expect.
Quarantine, revoke, or escalate with a fully-reasoned case file and a transparent audit trail.
Prompt firewalls and point tools inspect a single model’s traffic. Anzenna reasons over identity, behavior, and data context across your stack, so misuse surfaces with the evidence to act.
Misuse prevention is one layer. Pair it with discovery, access control, and data protection for full AI usage security.
