Likelihood Impact
Likelihood and impact literacy names matrix axes, risk appetite alignment, and priority bands — score one fictional risk on YOUR $GRC_LAB before treatment options.
Visual · t37_likelihood_impact
Likelihood impact literacy. $GRC_LAB only. Original Cyberlium.
Likelihood times impact drives priority — literacy teaches matrix scoring so treatment decisions on $GRC_LAB fictional orgs use consistent definitions.
Likelihood estimates how often a threat scenario may occur — rare, occasional, frequent. Impact estimates harm — reputational, financial, operational, regulatory — if the scenario succeeds. A 5×5 matrix maps combined scores to priority bands aligned with org risk appetite on YOUR fictional template. Cyberlium writes likelihood-impact row on YOUR $GRC_LAB — scenario, likelihood score, impact score, priority band, appetite alignment note. Next: Risk Lab.
1. Matrix components (named)
Likelihood axis: defined frequency bands with examples for YOUR fictional org. Impact axis: harm categories — confidentiality, integrity, availability, regulatory. Combined score: matrix lookup or formula. Appetite: which bands require treatment vs accept with approval.
On $GRC_LAB, write matrix row — likelihood, impact, combined priority, appetite note for one fictional scenario.
Command guide
Try these commands — Matrix components (named)
═══ LINUX / macOS (Terminal Practice) ═══
Check system state and user context
Command — copy this
id whoami uname -a
Inspect network sockets listening for connections
Command — copy this
ss -tuln 2>/dev/null || netstat -tuln
Audit active processes
Command — copy this
ps aux | grep -v "\[" | head -15
═══ WINDOWS (POWERSHELL) ═══ Query user identity and system information
Command — copy this
whoami /all Get-ComputerInfo | Select-Object CsName, OsName, OsVersion
Primary tools to practice this lesson: grep, python3. Reference sites: NIST CSF (https://www.nist.gov/cyberframework); ISO 27001 (https://www.iso.org/isoiec-27001-information-security.html); CIS Controls (https://www.cisecurity.org/controls). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.
2. Why defined scales prevent priority arguments
Two analysts scoring 'Medium' without definitions produce inconsistent registers. Appetite statements from fictional board charters anchor when acceptance is allowed. Documented matrix survives internal audit sampling on lab templates.
Students score lab scenarios on notes — production matrices follow enterprise risk committee approval.
3. Lab boundary
Forbidden: scoring real employer incidents without authorization in lab register. Allowed: likelihood-impact card — one scored scenario with $GRC_LAB fictional org context.
Ship: likelihood-impact row for YOUR lab scenario. Next: Risk Lab.
4. What you ship: likelihood-impact row for $GRC_LAB
Scenario, likelihood, impact, priority band. $GRC_LAB named. chmod 600.
5. What you record before the next lesson
Date. Likelihood-impact row. $GRC_LAB named. File t37-m02-l03-likelihood-impact.txt chmod 600.
6. Wrong vs right: fraudulent certs vs YOUR lab templates
Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.
- Wrong: Score impact as Critical for every risk because 'security is important.' Skip appetite alignment note.
- Right: Write likelihood-impact row for YOUR $GRC_LAB scenario. Next: Risk Lab.
Mission: score one risk on YOUR lab matrix
1) Define likelihood and impact scales. 2) Score one fictional scenario. 3) Name priority band and appetite note. 4) chmod 600.
Stuck? Ask Cyberlium AI Mentor
Appetite defines when acceptance is allowed — not every High risk gets the same treatment.
APPLY: Likelihood in risk matrix primarily estimates:
APPLY: True or False: Impact estimates harm if the scenario succeeds.
APPLY: Likelihood-impact literacy on Cyberlium uses: