Glossary

Achilles heel. A leaf that many paths to a goal share, weighted by those paths’ likelihood.

Actor, threat actor. Someone who attacks the system, described by skill, resources, access, risk appetite, and optionally occurrence.

AND node. A node whose children are steps that must all succeed. Likelihoods multiply and costs add.

Attack tree. A tree whose root is an attacker’s goal, whose inner nodes are subgoals, and whose leaves are concrete attacks.

Attack vector. A leaf: a concrete action with a complexity, a likelihood, and optionally a cost and requirements.

Bypass rate. The measured share of attacks that get through a probabilistic control. Once recorded, it replaces the control’s assumed effect.

Choke point. A node that every feasible path to a goal crosses. A control there covers every path.

Complexity. How hard a leaf is to execute, from trivial to extreme. It sets the skill an actor needs and the default likelihood.

Control. A security measure attached to one or more nodes. It reduces the likelihood of everything below them.

Control requirement (CR-###). A testable obligation derived from one or more controls, with Given/When/Then acceptance scenarios, published into spec.md.

Converged. No goal at blocking residual risk with verified controls only, every requirement verified, no expired decision, no drift. See Verification.

Decision. An owner’s acceptance or transfer of a goal’s risk, or of every path through a node, until an expiry date.

Defence in depth. In the what-if table, a control that lowers no goal’s best path today but cuts alternative paths.

Drift. A change to spec.md, plan.md, or the OTM file since the tree was generated, detected by content hashes.

Effect. How much a control reduces the likelihood below its node: low (25 %), medium (50 %), high (80 %), or eliminates (100 %).

Evidence level. How a control’s verification was established: assumed, design-reviewed, lab-validated, end-to-end-validated, or regression-tested.

Feasible. A leaf or path that at least one modelled actor can perform.

Goal. The root of a tree: an outcome an attacker wants, with an impact rating per class.

Impact. The damage if a goal is reached, rated per class (business, customer, data, compliance, safety) from low to critical.

Micro attack simulation. A small, targeted exercise that runs a control’s validation steps against the real system to measure whether and how often it works.

Monte Carlo. Repeated evaluation of the tree with ratings sampled around their estimates, to show how sensitive each goal is to them.

Needs clarification. A node the spec does not settle. It is left out of the evaluation until the question is answered.

Occurrence. How likely an actor is to target the system at all, from 1 to 100. It scales every path the actor can run.

OR node. A node whose children are alternatives. The best one counts.

Path. A set of leaves that together achieve a goal: one choice at every OR node on the way.

Probabilistic control. A control that reduces rather than blocks, such as a classifier, guardrail, or human approval. Its effect needs measuring.

Residual risk. A goal’s risk level after the controls of a scenario, from its likelihood level and impact level.

Scenario. The set of control statuses counted by a simulation: none, current, planned, verified, or all.

Single point of failure. An active control whose removal alone raises a goal’s risk level.

Touchpoint. The file or component where a control is implemented.

Zone. In the agentic profile, the part of an AI agent a node targets: input, reasoning, tools, memory, or inter-agent.