This article explains five AI tools covered in CEH v13, how they are used in labs, and practical steps for students in India to master them.
You will get clear tool names, short lab tasks you can try, validation steps to record, and interview ready examples.
If you want hands on skills you can show recruiters, these tool workflows are the most useful parts of CEH v13 training.
Tool 1 — ShellGPT (AI command line assistant)
ShellGPT is a command line assistant that helps generate, test, and refine shell commands for Linux and PowerShell, and CEH v13 includes guided labs to practice it.
ShellGPT speeds up writing commands, suggests command variations, and helps you convert manual steps into scripts. In CEH v13 training you use it to prototype commands, then validate them safely inside lab VMs.
Short lab task (3 steps):
- Use ShellGPT to generate a nmap scan command for an internal lab subnet.
- Run the command in a Parrot VM and capture the output.
- Tweak the command based on output and save the final command in your lab journal.
Validation step: always run the suggested command on a lab VM and compare the output to expected ports and services. Note any syntactic changes you made.
2 minute interview line: “I used ShellGPT to iterate nmap commands in the lab, validated outputs on a Parrot VM, and recorded the final command and findings in my lab journal.”
Tool 2 — AI-assisted reconnaissance tools (automated profiling)
CEH v13 introduces AI-assisted reconnaissance tools that summarise public data, prioritise likely targets, and suggest investigation paths you then verify in lab work.
These tools reduce noise by highlighting high probability targets from OSINT, domain records, and public code repositories. You still need to verify each lead manually and document source and method.
Short lab task (3 steps):
- Run an AI recon tool on a permitted target scope in the lab.
- Collect a short list of 3 priority targets the tool suggests.
- Manually verify one target with passive and active recon commands.
Validation step: keep a screenshot or text log of the AI output and the manual commands used to confirm one finding. This proves you did not rely solely on the tool.
2 minute interview line: “I used an AI recon tool to focus OSINT, then verified a target with manual scanning and captured both outputs in my report.”
Tool 3 — AI-driven vulnerability scanners & prioritisation engines
AI scanners automate detection and rank findings by exploitability; CEH v13 shows you how to interpret results and avoid false positives.
AI scanners can turn lengthy reports into prioritised issues, but they sometimes flag false positives or low risk items. The course teaches how to validate a high priority finding with a short proof of concept.
Short lab task (3 steps):
- Run an AI enabled vulnerability scan on a lab VM.
- Select the top ranked finding and research the common exploit steps.
- Build a one step proof of concept and record the evidence.
Validation step: reproduce the finding manually or with a focused exploit and capture the proof of concept output. Note any differences from the scanner report.
2 minute interview line: “I validated the top scanner finding by building a one step proof of concept and wrote a concise remediation note showing impact and fix.”
AI tools in CEH v13 training often focus on how to use these scanners responsibly and how to turn their output into actionable remediation notes.
Tool 4 — AI for malware/code analysis and triage
CEH v13 teaches AI tools that perform quick static and dynamic analysis, extract indicators, and propose investigation steps you validate in the lab.
These tools speed initial triage by highlighting suspicious files, API calls, and indicators of compromise. You then use manual analysis to confirm behavior and to write clear indicators for reporting.
Short lab task (3 steps):
- Feed a sample binary or script into an AI analysis tool in the lab.
- Extract IOC items such as suspicious domains or file hashes.
- Run a simple dynamic test in an isolated VM and compare behavior to the tool summary.
Validation step: capture the dynamic test logs and link them to the AI tool output so your report shows both automated and manual evidence.
2 minute interview line: “I used an AI analysis tool for quick triage, then ran a controlled dynamic test to confirm behavior and documented the indicators.”
Tool 5 — LLM workflows for reporting, policy drafting and playbooks
CEH v13 shows how to use LLM workflows to draft pentest reports, security policies, and playbooks that you then tailor, validate, and sign off with evidence.
LLM workflows save time on formatting and first drafts, but the course stresses accuracy and source citation. You learn templates to convert lab outputs into clear executive summaries and remediation steps.
Short lab task (3 steps):
- Take raw findings from one lab and create a short data block: commands, outputs, timestamps.
- Use an LLM workflow to produce a report draft.
- Edit the draft to add exact evidence and validate every claim with your lab logs.
Validation step: ensure every claim in the LLM draft is linked to an evidence file or command output in your submission.
2 minute interview line: “I used an LLM workflow to draft a pentest summary, then validated every claim with lab logs and delivered a concise remediation plan.”
How these tools are taught in labs (practical method)
Labs pair tool demos with hands on exercises, timed CTF challenges, and focused validation tasks so students practice both tool use and verification.
Typical lab structure in CEH v13 training follows a four step loop: demo, guided exercise, independent challenge, and report writing. The labs use CyberQ with isolated VMs, session resets, and sanctioned tool access to replicate real scenarios.
Lab session bullets:
- Live demo of the tool and explanation of safe limits.
- Guided lab where you follow steps with instructor support.
- Timed challenge to apply the tool and validate one finding.
- Report writing to link tool outputs to manual evidence.
For full lab bundles and guided mock practicals consider the CEH v13 AI-powered course at Appin Indore which includes lab hours, tool access, and mock practical sessions.
AI tools in CEH v13 training are taught with an emphasis on validation and documentation so your work is interview ready.
Ethical and safety rules to teach alongside tools
CEH v13 enforces legal and ethical boundaries: only test authorised targets, record consent, and avoid harmful automation without oversight.
Ethical training covers scope, consent, and safe handling of potentially dangerous scripts or binaries. You learn to maintain logs, preserve evidence, and stop tests when they risk harm.
Ethics checklist:
- Only test targets with explicit permission.
- Log date, time, commands, and observer notes.
- Avoid automated wide scope scans without approval.
- Sanitize any shared outputs to remove sensitive data.
This discipline protects you legally and builds trust with employers.
Interview examples and how to prove tool proficiency
Present a one page lab summary, a timed challenge result, and explain one tool output and your validation steps in two minutes.
Recruiters want concise proof of skill, not long tool lists. Prepare a one page summary that has: the tool name, one lab task, one piece of evidence, and one remediation suggestion. Practice explaining the workflow in two minutes.
Interview prep bullets:
- One page lab summary: objective, commands, evidence snippet, remediation.
- Timed challenge result: time taken, outcome, and learnings.
- Two minute script: what you did, why it mattered, how you validated it.
Use this structure to show decision making and verification, not just tool familiarity. AI tools in CEH v13 training give you concrete lab outputs you can present.
Conclusion
Enquire about Appin’s CEH v13 AI course to get guided lab access, mock practicals, and placement support.
If you want hands on practice with ShellGPT, AI recon, scanners, malware triage tools, and LLM workflows, contact Appin Indore for batch details and a lab schedule. Practical, supervised lab time is the fastest way to move from reading about tools to proving them in interviews.
Next step: enquire now with Appin to check batch dates, lab access, and the mock practical schedule.
