Train the workday decisions attackers exploit.

Work through a live-looking inbox, inspect a suspicious vendor message, and make the safest next move before money or credentials are lost.

Build safer password habits, spot MFA fatigue, and decide how to recover when a login pattern looks wrong.

Decide what belongs in an AI prompt, what must stay out, and how to use AI without leaking sensitive business context.

Practice resisting urgent requests that sound real, look real, and still need independent verification.

Practice safer decisions around text-message lures, QR codes, and work performed from cafés, airports, and event spaces.

Understand why browser plug-ins, AI assistants, and unsanctioned SaaS tools can quietly gain access to more company data than expected.

Practice spotting internal-style message fraud, fake help requests, and the “quick favour” tone that thrives in chat tools.

Know what to report, what evidence helps, and how to respond without making the situation worse.

Practice resisting fake meeting invites that weaponize calendar trust, browser logins, and executive timing pressure.

Spot malicious shared-document flows, fake file portals, and connected-app consent traps before access tokens are handed away.

Practice handling over-helpful AI assistants, broad data-access requests, and collaboration shortcuts that can expose more than employees realize.

Control agentic AI workflows before an assistant sends email, changes records, or takes action with the wrong context.

Catch unsupported AI claims, fabricated citations, and overconfident summaries before they influence real decisions.

Evaluate browser extensions before they read every page, capture tokens, or quietly exfiltrate sensitive work data.

Practice the first hour of a ransomware suspicion: isolate, preserve evidence, report fast, and avoid making recovery harder.

Use AI to turn CVE, KEV, IOC, vendor, ATT&CK, and report feeds into ranked, decision-ready security intelligence.

Design cyber defence agents with scoped authority, guardrails, auditability, approvals, and rollback for production SOC use.

Test AI-enabled security systems for prompt injection, poisoning, evasion, extraction, unsafe tool use, and output manipulation.

Connect LLMs into SOAR workflows for enrichment, classification, recommendations, approvals, and reversible response actions.

Build AI cyber governance with risk registers, system inventories, accountability matrices, review boards, and approval workflows.

Use AI to support incident triage, clustering, timeline reconstruction, evidence review, response rationale, and reporting.

Preserve model, prompt, tool-call, decision, signer, timestamp, retention, and residency artefacts for forensic reconstruction.

Run a mission-first assessment: discovery interviews, messy inventories, evidence collection, risk translation, and practical executive summaries.

Assess M365 and Google Workspace tenants for identity, admin privilege, email security, sharing, logging, evidence, and prioritized fixes.

Use a repeatable assessment lens for Slack, Zoom, 1Password, Okta, integrations, audit logs, retention, lifecycle, and unknown SaaS tools.

Turn findings into safe implementation plans with phases, owners, communications, exceptions, rollback paths, champions, and adoption measures.

Create client-ready reports, executive summaries, technical appendices, readouts, incident plans, tabletops, and calm evidence-backed recommendations.

Complete a RipRap-style engagement from discovery through assessment, evidence, findings, roadmap, rollout plan, and client presentation trade-offs.

Learn COBOL structure by comparing valid programs, arranging divisions, and simulating a minimal batch run.

Practice PIC clauses, packed decimal judgement, copybook reading, and fixed-width record interpretation.

Connect COBOL SELECT names to JCL DD names, handle file status, and design restartable batch changes.

Work with embedded SQL, SQLCODE handling, CICS response checks, and transaction-safe thinking.

Modernize legacy COBOL safely with characterization tests, structured refactors, API boundaries, and production-aware review.

Triage a contractor access request, verify the business owner, constrain privileges, and respond when a trusted vendor account behaves strangely.

Navigate airport Wi-Fi, charging, border and hotel risks while keeping corporate devices, conversations, and data protected.

Challenge unsafe entry, handle visitors professionally, protect printed information, and respond to an unknown device in a restricted area.

Move a sensitive dataset through classification, collaboration, retention, and disposal without losing control of access or evidence.

Detect malicious instructions hidden in retrieved content, preserve source trust, and contain a compromised enterprise AI assistant.

Evaluate an AI vendor before purchase, test data and security claims, and set measurable approval conditions for organizational use.

Use an AI coding assistant without leaking secrets, accepting unsafe code, or importing vulnerable and unlicensed dependencies.

Run a coordinated response when an enterprise AI service exposes data, takes an unsafe action, or begins producing harmful outputs at scale.

Review an AI-assisted people decision for subgroup harms, weak proxies, accessibility barriers, and unsafe automation before it affects staff.

Fold polyhedron nets, trace face adjacency, and distinguish tesseract nets from projections and cross-sections.

Practice propositional, predicate, higher-order, modal, fuzzy, and temporal reasoning, then solve a constraint game.

Trace binary circuits, half-adders, universal gates, and a clearly defined three-valued gate algebra.

Read strand direction, compute reverse complements, inspect alignments, and interpret mapping evidence without overclaiming.

Trace chemical transmission and explore how release, inhibition, timing, conductance, and network state regulate signal flow.

Separate observation from interpretation, test causal claims, evaluate sources, and update conclusions when evidence changes.

Turn vague complaints into testable problem statements, distinguish symptoms from causes, and verify root causes before acting.

Use a repeatable diagnostic loop to reproduce, isolate, test, recover, and escalate problems without random changes.

Calibrate confidence, use base rates and expected outcomes, design reversible decisions, and recognize when new evidence should change the plan.

Map feedback loops, constraints, delays, and second-order effects so local fixes improve the whole system rather than move the problem.