Use LLMs to accelerate authorized penetration tests without letting unverified output become client-facing evidence
Define where an LLM can support a scoped penetration test and identify the evidence that remains necessary for a client-facing finding.
Evaluate engagement data for sharing with LLM tools by applying redaction, minimization, and documented scope decisions.
Create recon summaries that preserve source traceability while separating observed facts, hypotheses, and next verification steps.
Triage scanner output into candidate findings by checking raw evidence and rejecting false positives or invented vulnerability references.
Produce a client-ready report by verifying explanations, severity rationales, citations, scope, data handling, and final quality gates.
3 sections · 6 lessons
Define the LLM's role in a scoped engagement and examine which client data may be shared, how it should be minimized, and how decisions are documented.
2 lessonsTrace recon summaries to their sources and evaluate scanner output against raw evidence, separating facts from hypotheses and blocking false positives or invented vulnerability references.
2 lessonsBuild verified finding explanations and severity rationales, then apply scope, data-handling, citation, and final quality gates before delivery.
2 lessonsAuthorized penetration testing requires clear boundaries around what an LLM may assist with and what remains the tester's responsibility. This course develops a defensible workflow for handling engagement data, checking AI-generated recon summaries and scan interpretations, validating finding severity and references, and applying final quality gates before a report reaches the client.
The course covers LLM boundaries in scoped engagements, engagement-data handling, recon summarization, scan triage, evidence verification, finding explanations, severity rationale, citation checks, and client-ready reporting with final quality gates.
From project managers and operators to founders, analysts, and teams adopting AI, Fenzo is for people who want to get better at the work they already do.
Anything from using AI for project planning and analysis to improving workflows, decision-making, communication, and delivery. If you can describe the skill or task, Fenzo can build a course around it.
Yes. Tell Fenzo what you do, what you’re trying to accomplish, and where you’re stuck, and Fenzo will shape the course around your role, your goals, and the work you actually have to do.
No. Fenzo adapts to your starting point, so you can begin with the fundamentals or jump straight into more advanced work.
Yes. Fenzo for Business puts the whole team on one plan — shared seats, courses you can assign, and reporting on who has finished what.
See Fenzo for BusinessYou start by describing your goal or taking a short skill benchmark. Fenzo uses that context to identify gaps and shape the learning experience around them.
Yes, Fenzo works in your mobile browser.
Something to operate in every section.
Three findings from decades of learning research shaped how every Fenzo lesson is built.
Average pass rate · Freeman et al., PNAS 2014
What the science says
Students in lecture-only classes are 1.5× more likely to fail.
How Fenzo does it
Every Fenzo lesson is interactive. No passive reading or watching, ever.
You work through itAI does it for you
Skill retention over time · with vs. without practice
What the science says
Offloading thinking to AI erodes the very skills you’re trying to build.
How Fenzo does it
Fenzo makes you do the thinking — guided, but never done for you.
Retrieval practiceRe-reading
Long-term recall · retrieval vs. re-reading
What the science says
Recalling information beats re-reading it for building durable understanding.
How Fenzo does it
Lessons weave in quizzes and recall prompts, so it sticks.