PrefactorvsAI security platforms

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.

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support-agent v4 · one run, two layersexample
Illustrative run, showing what each layer tells you
AI security platformssee
fetch_customer212ms · 1.2k tok
apply_refund1.4s · 3.1k tok
send_reply340ms · 0.8k tok
trace recorded, no verdict
Prefactoradds
Did its job✓ yes
Quality84 / 100
Cost$0.42 · in budget
Drift vs baselinenone
✓ a run that breaches its schema is held for review
§01 / THE SHORT ANSWERtl;dr: which, and when
TL;DR

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.

The short answer

AI security platforms or Prefactor, in one table

Decision factorAI security platformsPrefactor
Primary concernAn attacker exploiting the agentThe agent drifting from its job
Failure it catchesPrompt injection, jailbreaks, data leaksWrong outputs, overspend, out-of-scope actions
Typical buyerThe security teamThe team that owns the agent in production
What it measuresThreats blocked and exposureA quality score, drift, and cost per agent
How it attachesInline filter or scanningNative SDK, core SDK, or OpenTelemetry ingest, no rebuild
Use them together?Defend with a security platformEvaluate with Prefactor
§02 / HONEST CONTRASTscope: different jobs
Honest contrast

What each one is for

What AI security platforms do well
  • 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.

What Prefactor adds
  • 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.

§03 / CAPABILITY MATRIXside by side: what each covers
Side by side

Side by side, defending against evaluating

CapabilityAI security platformsPrefactor
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 reviewFor threats✓
One queryable record per agentPartial✓
Scores agents across frameworks from one place—✓
Audit trail for a decisionPartial✓
§04 / THE CATEGORYwho's in it: the landscape
The category

The tools in this space

ZenitySecurity and controls for enterprise copilots and agents.Compare →Aim SecurityAI security posture management and shadow AI discovery.Compare →Prisma AIRSAI runtime security inside the Palo Alto Networks platform.Compare →LakeraReal-time prompt injection detection and an LLM firewall.Compare →
§05 / THE QUALITY GAPour take: where it stops
Our take

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.

01
Failures with no attacker

An agent can pass every threat check and still be wrong. Prefactor checks each run for outcome quality and cost.

02
Drift, not just attacks

The quality score is tracked per agent across versions, and a shift in behaviour after a change is flagged before a user hits it.

03
Evidence you can hand over

Each run feeds a record you can hand to a customer or an auditor.

04
Alongside the security layer

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.

See it on your own agents

A working session on a fleet like yours: watch a run evaluated, catch a drift, walk the record.

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§06 / QUESTIONSfaq: the common ones
Questions
Does Prefactor replace my AI security platform?
No. A security platform stops attacks; Prefactor judges whether the agent did its job. These are independent failure modes, so most teams run both.
Can an agent be secure and still fail?
Yes. It can pass every threat check and still return a wrong answer, exceed its budget, or act outside its approved scope. Those are the failures Prefactor scores.
Does Prefactor detect prompt injection or jailbreaks?
No. That is the security platform's job. Prefactor evaluates outcomes: quality, cost, and scope per run. Use it alongside a threat platform rather than in place of one.
How does Prefactor attach to agents already behind a security layer?
Through a native SDK, the core SDK for TypeScript and Python, or OpenTelemetry ingest. It reads the traces your agents already emit, so there is no rebuild and no gateway in the request path.
Who owns Prefactor, the security team or the agent team?
Usually the team that owns the agent in production, since it reports on outcomes and cost. It sits next to the security stack rather than inside it.
Reviewed against public sources on March 19, 2026Suggest a correction

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See how every agent performs, and make it better

Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.

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