Threat platforms stop the attacker. Prefactor catches the agent's own mistakes.
An agent can pass every threat check and still return the wrong answer, so the two cover different failures.
An AI security platform defends agents against prompt injection, jailbreaks, data leaks, and shadow deployments. Prefactor scores what the agent itself did on every run: wrong outputs, overspend, and drift involve no attacker at all. Most teams running agents in production need both.
AI security platforms or Prefactor, in one table
| Decision factor | AI security platforms | Prefactor |
|---|---|---|
| Primary concern | An attacker exploiting the agent | The agent drifting from its job |
| Failure it catches | Prompt injection, jailbreaks, data leaks | Wrong outputs, overspend, out-of-scope actions |
| Typical buyer | The security team | The team that owns the agent in production |
| What it measures | Threats blocked and exposure | A quality score, drift, and cost per agent |
| How it attaches | Inline filter or scanning | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| Use them together? | Defend with a security platform | Evaluate with Prefactor |
What each one is for
- Threat detection: spot prompt injection, jailbreaks, and other attempts to subvert an agent.
- Shadow discovery: find agents and copilots running without approval.
- Posture management: track configuration and exposure across agent deployments.
- Inline defence: block or filter a malicious input before it reaches the model.
- Incident response: record and alert on an attempted attack for the security team.
Best for security teams defending agents against attackers and unapproved deployments.
- Scores each run for outcome quality, cost, and whether the agent stayed in its approved scope, with no attacker involved.
- A quality score per agent tracked across versions, so a regression shows up as a trend.
- Drift detection when behaviour shifts after a model update or a prompt edit, before a user hits it.
- Holds or escalates a risky action for review before it reaches a user, not after.
- One record across frameworks: agents scored from the same place, with an audit trail per decision.
Best for the team that owns an agent in production and needs to know it is doing its job, and show the evidence.
Side by side, defending against evaluating
| Capability | AI security platforms | Prefactor |
|---|---|---|
| Defending against attackers | ||
| Prompt injection and jailbreak detection | ✓ | — |
| Shadow agent discovery | ✓ | — |
| Inline blocking of malicious input | ✓ | — |
| Evaluating the outcome | ||
| Quality score per run | — | ✓ |
| Quality score tracked per agent and version | — | ✓ |
| Drift detection after a model or prompt change | — | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Acting and recording | ||
| Hold or escalate a risky action for review | For threats | ✓ |
| One queryable record per agent | Partial | ✓ |
| Scores agents across frameworks from one place | — | ✓ |
| Audit trail for a decision | Partial | ✓ |
The tools in this space
Where security stops: whether the agent did its job
We sell the layer this section describes. Read it with that in mind.
A security platform assumes an attacker is trying to exploit the agent. That leaves a second failure untouched: the agent itself drifting, returning wrong answers, or overspending, with no attacker involved.
An agent can pass every threat check and still be wrong. Prefactor checks each run for outcome quality and cost.
The quality score is tracked per agent across versions, and a shift in behaviour after a change is flagged before a user hits it.
Each run feeds a record you can hand to a customer or an auditor.
It reads the traces your agents already emit, through a native SDK or any OpenTelemetry source, so it sits next to your defences rather than replacing them.
Does Prefactor replace my AI security platform?
Can an agent be secure and still fail?
Does Prefactor detect prompt injection or jailbreaks?
How does Prefactor attach to agents already behind a security layer?
Who owns Prefactor, the security team or the agent team?
Evaluate the agents your security stack protects
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent.
Unified performance platform for agents, authentication, and risk management