RunSybil sits at the fully-autonomous pole of AI pentesting, with minimal human steering. Planck Operator keeps that autonomy's breadth and adds a human on the loop, so you can direct the agent at the business logic and edge cases a pure-autonomous run tends to miss, with proof attached to every finding.
Both run as autonomous AI pentesters, so both give you breadth and constancy. The difference is control. RunSybil represents the pure-autonomous pole; Planck Operator is human-on-the-loop, a named category between fully-autonomous and human-validated.
| Planck Operator | RunSybil | |
|---|---|---|
| Autonomy model | Steerable, human-on-the-loop | Fully-autonomous, minimal steering |
| Business-logic depth | Direct the agent at context-specific abuse | Strongest on broad, known classes |
| Breadth | Autonomous, continuous | Autonomous, continuous |
| Reporting | Exploit-proven: request/response, repro, CVSS v3.1 | Autonomous findings |
| Pricing | Published, self-serve free Recon tier | Not publicly published |
| Human validation | On demand | Not the primary model |
RunSybil is a well-funded, fully-autonomous AI pentest startup (around $40M in funding, third-party estimate, as of 2026). The contrast here is about steering model, not capability to fund the work.
RunSybil is a credible player at the fully-autonomous pole. For teams that want breadth with as little human involvement as possible, a pure-autonomous agent is a coherent design, and RunSybil has the funding to invest in it.
We share RunSybil's core premise: autonomy is the right way to get continuous breadth across a changing attack surface. Where we differ is that we think the best results come from keeping a human able to steer, not from removing the human entirely.
Fully-autonomous runs are strong on known vulnerability classes. What they tend to miss is context: the multi-step abuse tied to how your product actually works, and the edge cases a human would prioritize. Steering closes that gap without giving up breadth.
RunSybil represents the fully-autonomous pole of AI pentesting, with minimal human steering. Planck Operator is steerable and human-on-the-loop: it keeps autonomy's breadth but lets a human direct it at business logic and edge cases a pure-autonomous run tends to miss.
No. Planck Operator runs autonomously for breadth by default. Steering is optional and human-on-the-loop, so you can point it at priorities or edge cases without giving up the constancy of an autonomous run.
Pure-autonomous runs are strong on broad, known vulnerability classes but can miss context-specific business logic, multi-step abuse tied to how your product actually works, and edge cases a human would prioritize. A human-on-the-loop can direct the agent at exactly those.
Every finding is exploit-proven and proof-first: it ships with the request and response, reproduction steps, and a CVSS v3.1 severity. Anything the agent cannot reproduce does not reach your report.
Start on the self-serve free Recon discovery tier without a sales call. Paid tiers (Active-Safe, Full-Validate, Deep Validate, and Enterprise) scale by verified domain and depth, so coverage grows with your attack surface rather than a fixed package.
Point Operator at your surface for breadth, then direct it at what matters, with proof on every finding.