How we measure readiness
How we measure readiness
Knowing which training an operator has completed is not enough. ForceGen connects each work role with its tasks, capabilities and performance evidence to show what the operator can do today, what is missing and what to strengthen next.
Executive summary
Currency
Readiness is not a one-time result. Evidence has a date, and the model takes into account how current it is: what an operator demonstrated last year does not weigh the same as what they demonstrated this month.
Evidence
Every readiness indicator is linked to the missions, tasks and performance evidence behind it. It is not course hours: it is what the operator did in the lab.
Human decision
The model derives readiness, gaps and a proposed path. Instructors and decision-makers review, adjust and approve before anything changes.
On this page
What an ontology is
The term comes from philosophy: on, ontos (being, that which exists) and logos (study, reason). Philosophy used it to answer what things exist, what kinds they are, what properties they have and how they relate.
Computer science and artificial intelligence adopted the term because they faced the same problem in technical domains, and turned it into an operational definition: an ontology is a formal, explicit representation of the concepts of a domain, their properties, their relationships, their categories and the rules that govern them.
The key word is derive. The goal is not only to describe what exists, but to do it in a way a machine can process, reason over and use to derive new knowledge from the defined relationships.
Three ways to represent knowledge
A taxonomy organizes a catalog into categories and subcategories. It is the first step in structuring a domain, but it only expresses the parent-child hierarchy: it says nothing about what an analyst does, what a hunter needs to know or how a monitor proves their readiness.
A framework mapping establishes correspondences: it lets you say that Q-Hunter maps to a DCWF code and a NICE code. It is valuable for interoperability and framework alignment, but it does not define what the role is, which tasks it involves or how to measure someone’s readiness in it.
An ontology defines what each entity is, what each relationship means and what can be derived from them. Every arrow has a name and a precise meaning. That turns a graph of relationships into a model you can reason over. That is what ForceGen builds.
| Capability | Taxonomy | Matrix | Ontology |
|---|---|---|---|
| Classify elements | Yes | partial | Yes |
| Relate frameworks | No | Yes | Yes |
| Define meaning | No | No | Yes |
| Derive readiness | No | No | Yes |
| Derive multiple views | No | No | Yes |
What each form can do
Classify elements
- Taxonomy
- Yes
- Matrix
- partial
- Ontology
- Yes
Relate frameworks
- Taxonomy
- No
- Matrix
- Yes
- Ontology
- Yes
Define meaning
- Taxonomy
- No
- Matrix
- No
- Ontology
- Yes
Derive readiness
- Taxonomy
- No
- Matrix
- No
- Ontology
- Yes
Derive multiple views
- Taxonomy
- No
- Matrix
- No
- Ontology
- Yes
The PCT model: entities and relationships
PCT stands for Persistent Cyber Training. The model has eight entities and nine named relationships that connect each person with their role, tasks, capabilities and evidence.
Person performs Role. Role requires Task. Task requires K / S / A. Mission exercises Task. Mission runs as Operation. Operation produces Evidence. Evidence demonstrates Task. Task contributes to Readiness. Readiness informs Person.
What the model derives
- PersonperformsRole
- RolerequiresTask
- TaskrequiresK / S / A
- MissionexercisesTask
- Missionruns asOperation
- OperationproducesEvidence
- EvidencedemonstratesTask
- Taskcontributes toReadiness
- ReadinessinformsPerson
All relationships 09
- PersonperformsRole
- RolerequiresTask
- TaskrequiresK / S / A
- MissionexercisesTask
- Missionruns asOperation
- OperationproducesEvidence
- EvidencedemonstratesTask
- Taskcontributes toReadiness
- ReadinessinformsPerson
| Relationship | Meaning | Where it lives in ForceGen |
|---|---|---|
| Person performs Role | An operator performs one or more Q-Roles aligned to DCWF and NICE. | Q-Camp |
| Role requires Task | Each role requires a set of tasks that must be performable. | Q-Camp · Ontology |
| Task requires K/S/A | Each task requires specific knowledge, skills and abilities. | Ontology |
| Mission exercises Task | A TTX, CTF, STX or FMX exercise tests specific tasks. | Q-Trainer · Q-Range |
| Operation produces Evidence | Running the mission in a real environment leaves performance evidence. | Q-Range |
| Evidence demonstrates Task | Evidence shows that a task was performed, and how well. | Q-Score |
| Task contributes to Readiness | The tasks an operator has demonstrated contribute to their readiness for the role. | Q-Insight |
| Readiness informs Person | The readiness state returns to the operator as a recommendation, pending instructor review. | Q-Sensus |
What each relationship means
Person performs Role
- Meaning
- An operator performs one or more Q-Roles aligned to DCWF and NICE.
- Where it lives in ForceGen
- Q-Camp
Role requires Task
- Meaning
- Each role requires a set of tasks that must be performable.
- Where it lives in ForceGen
- Q-Camp · Ontology
Task requires K/S/A
- Meaning
- Each task requires specific knowledge, skills and abilities.
- Where it lives in ForceGen
- Ontology
Mission exercises Task
- Meaning
- A TTX, CTF, STX or FMX exercise tests specific tasks.
- Where it lives in ForceGen
- Q-Trainer · Q-Range
Operation produces Evidence
- Meaning
- Running the mission in a real environment leaves performance evidence.
- Where it lives in ForceGen
- Q-Range
Evidence demonstrates Task
- Meaning
- Evidence shows that a task was performed, and how well.
- Where it lives in ForceGen
- Q-Score
Task contributes to Readiness
- Meaning
- The tasks an operator has demonstrated contribute to their readiness for the role.
- Where it lives in ForceGen
- Q-Insight
Readiness informs Person
- Meaning
- The readiness state returns to the operator as a recommendation, pending instructor review.
- Where it lives in ForceGen
- Q-Sensus
What it derives that a matrix cannot
Because every relationship has a formal meaning, the system can chain them and derive knowledge that is not explicitly written in any data point.
Real readiness: a person’s level for a role is derived from their performance evidence, not from completed course hours.
Capability gaps: by comparing the K/S/A the role requires with those the person has demonstrated, the model identifies knowledge, skill and ability gaps.
Training paths: from the gaps, a path is proposed: which task to practice, which knowledge to acquire and which mission to run to close each one. The instructor reviews it before it is applied.
Ontology and matrix: brain and view
The PCT Ontology is the conceptual brain: it defines the complete semantic structure of the domain, from the person to readiness, through role, task, K/S/A, capability, activity, evidence and performance. It is the single, canonical model from which every other representation is derived.
The PCT Matrix is a tabular view of part of the ontology: Q-Role × DCWF × NICE × Tasks × K/S/A × NIST CSF × ATT&CK. It is useful and necessary for framework alignment, but it captures only a subset of the model’s relationships.
One source, many views
A single semantic structure powers very different platform functions: the PCT Matrix for framework alignment, the Role Progress Map that decision-makers see in Q-Insight, and the Skill Gap Analysis that Q-Sensus uses to recommend activities to the instructor.
It is the underlying architecture decision: do not duplicate data structures for each function, derive them all from a single source of truth.
Keep exploring
A single semantic structure. Many views. One source of truth for the whole ForceGen platform.