Four things claim to tell you whether you can be broken into. Each answers a different, narrower question. The agent is built to give you continuous coverage, proven exploitability, and a human you can put behind any finding, in one place.
Read across a row and the gap is usually the same one: everyone can produce a list, few can prove the list is real, and fewer still keep proving it as your systems change.
| Planck Operator | Vulnerability scanner | PTaaS / bug bounty | Annual manual pentest | Single-shot AI tool | |
|---|---|---|---|---|---|
| Cadence | Continuous, on every change | Continuous but shallow | On engagement | Once a year | A single run |
| What you receive | Exploit-proven findings | Unverified alerts | Verified but slow | Verified snapshot | Often unverified output |
| Attack chaining | Multi-step, across assets | None | Depends on the tester | Yes | Limited |
| False positives | Reproduced before delivery | High | Low | Low | Can be high |
| Coverage | Full surface, re-mapped | Signature based | Scoped assets | Scoped snapshot | Single target |
| Human validation | On demand, same team | None | Platform triage | Inherent | Usually none |
| Safe in production | Non-destructive, scoped, kill switch | Usually | Varies | Manual care | Varies |
| Cost model | Scoped subscription | Per seat | Per report or bounty | Per engagement | Per run |
A scanner is fast and cheap and never stops, but it matches signatures and versions. It hands you a queue of maybes and leaves your team to prove which ones an attacker could actually use.
A human engagement is deep and creative, and it ages the moment it ends. By the next deploy the report is a snapshot of a system that no longer exists.
A one-shot AI tool can be impressive and unproven at the same time. Without an independent verification step and a human behind it, you cannot tell a real finding from a confident guess.
Send us a domain and the rules of engagement. We will return a scoped run and show you what it surfaces, and what it proves.