Prisma AIRS secures the AI runtime. Prefactor evaluates every run against the agent's job.
One defends against prompt injection and tool misuse, the other catches runs that miss the outcome or leave approved scope, so they sit at different layers of the same stack.[1][2]
What is the difference between Prisma AIRS and Prefactor?
Prisma AIRS is Palo Alto's security layer for AI: runtime threat detection, model scanning, and red teaming. Prefactor judges each run's outcome: quality, drift, and cost per agent, on any framework. An agent can pass every security check and still fail its task, so the two layers run together.
Prisma AIRS or Prefactor, in one table
| Decision factor | Prisma AIRS | Prefactor |
|---|---|---|
| Where it fits | Securing the AI runtime against attacks | Knowing the agent did its job in production |
| Primary question | Is this agent being attacked or compromised? | Did this agent produce the right outcome, at what cost? |
| Scope | Model supply chain through runtime defence | The deployed agent, on any framework |
| What you get | Threat detection, model scanning, red teaming | A quality score per run, drift detection, and cost per agent |
| How it attaches | A security profile in the Palo Alto stack | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild, no gateway in the request path |
| Use them together? | Secure the runtime with Prisma AIRS | Check the work with Prefactor |
What each one is for
- AI runtime defence: inline protection against prompt injection, tool misuse, and malicious agent behaviour.
- Model security: analysis of open-source models for backdoors, poisoning, and hidden code across the supply chain.
- Red teaming: persistent automated adversarial testing that adapts as an attacker would, rather than periodic checks.
- Shadow agent discovery: across sanctioned and unsanctioned deployments.
- Palo Alto scale: tens of thousands of enterprise customers and integration with Cortex, Prisma Cloud, and XSIAM.
Best for security teams that need protection across the AI lifecycle, from the model supply chain to runtime defence.
- Watches every run and judges the outcome for quality, cost, and whether the agent stayed in its approved scope.
- A quality score per agent tracked across versions, so a regression shows up as a trend rather than a surprise.
- 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, once a score or scope check crosses a threshold.
- One record across frameworks: agents evaluated from the same place, with an audit trail for each decision.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
Side by side, security versus evaluation
| Capability | Prisma AIRS | Prefactor |
|---|---|---|
| Securing the AI runtime | ||
| Runtime threat detection | ✓ | — |
| Model supply chain security | ✓ | — |
| Automated red teaming | ✓ | — |
| Shadow agent discovery | ✓ | — |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Cost attributed per agent and version | — | ✓ |
| Drift detection against a baseline | — | ✓ |
| Hold or escalate a risky action before a user is affected | On threat signals | On quality and scope signals |
| Across your stack | ||
| Evaluates agents built on any framework | — | ✓ |
| One queryable record per agent | — | ✓ |
| Audit trail for a decision | For security events | ✓ |
Where security stops: whether the agent did its job
We sell the layer this section describes. Read it with that in mind.
Prisma AIRS answers whether an agent is under attack or a model has been tampered with, across the AI lifecycle. It does not say whether the deployed agent did its job, at acceptable quality and cost.
Each run gets a quality score, tracked per agent across versions.
When behaviour shifts after a model update or a prompt edit, Prefactor flags it.
When a score or a scope check crosses a threshold, Prefactor can hold or escalate the action before a user is affected.
Every decision keeps a record you can hand to a customer or an auditor. Prefactor reads the traces you already emit, through a native SDK or any OpenTelemetry source, so it works alongside the security stack you already run.
Which one fits
Reach for Prisma AIRS when
- Your priority is defending the AI runtime and the model supply chain.
- A security operations team owns AI risk and needs threat response.
- You already run Palo Alto tooling and want AI security in the same stack.
Reach for Prefactor when
- Agents are doing real work for real users and quality matters per run.
- You need a quality score per agent and version, not a threat alert.
- A regression after a prompt or model change has to surface before a user hits it.
- Someone asks you to prove an agent did its job.
How we reviewed this comparison
Reviewed against public product and documentation pages on March 19, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Sources reviewed
Prefactor contextMethodology
- Reviewed public product, documentation, and launch material visible at the time of writing.
- Mapped each page to the primary buyer, control layer, and runtime capabilities each vendor describes publicly.
- Prefer direct product and documentation pages over analyst summaries or reseller material.
Does Prefactor compete with Prisma AIRS?
What does Prefactor do that Prisma AIRS does not?
Does Prisma AIRS already cover evaluation?
Does Prefactor work alongside Prisma AIRS?
Which team buys Prefactor?
Related
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