In questo argomento
Training · Labs · Publications · Exercises · Careers · Communities
Build a cybersecurity learning path
How can cybersecurity progress be measured? Choose a task you must explain and carry out in an authorised lab. Define prerequisites, documentation, exercises and the expected outcome. Learning evidence can be an annotated configuration, log analysis or reproducible report. Distinguish observed results, assumptions and lab limitations before applying an exercise to a real environment.
Leggi la guida ENMonitoraggio delle notizie
Tutte le notizie internazionali →Titoli salvati · aggiornamento non disponibile · Ultimo recupero riuscito : 22 ott 2026, 21:27
Savarese (Salesforce): AI, passa dai Lab alle aziende - inno3
via Google News · aggregatorGoogle rilascia Harness2, gli agenti AI che migliorano da soli - Pasquale Pillitteri
via Google News · aggregatorAI nelle imprese, uso e ROI nel 2026: i dati Wharton e Istat - AI4Business
via Google News · aggregatorInfosys e Columbia University lanciano il Centro di Ricerca AI per le Imprese - Unite.AI
via Google News · aggregatorBeacon rileva Haize Labs per alimentare l'AI per la real economy - SecurityOpenLab
via Google News · aggregatorAnthropic afferma che Claude guida il 26% della sua ricerca e sviluppo IA - Unite.AI
via Google News · aggregatorRiferimenti: dati, usi e limiti
Documentazione consultata il .
Le note dettagliate di questa edizione sono disponibili in inglese.
| Fonte e finalità | Dato o riferimento verificato | Come utilizzarlo | Limite da conservare |
|---|---|---|---|
| MITRE · Common Weakness Enumeration Name classes of software and hardware weakness. |
A CWE describes a weakness that can contribute to vulnerabilities. It does not represent a particular incident. | Connect the weakness to the observed mechanism: input, processing and missing control. Use it to fix the cause and plan regression tests. | A keyword in a report alone does not establish the correct CWE classification. |
| W3C · WCAG 2.2 Evaluate accessibility criteria for web content. |
WCAG 2.2 is a W3C Recommendation. Success criteria have A, AA and AAA levels. | Define tasks, pages and states, then combine inspection, keyboard, zoom and relevant assistive technology. Keep reproduction steps for barriers. | An automated scanner does not establish conformity of a complete journey. Evaluation must cover interactive states and actual content. |
| Google · Core Web Vitals Distinguish loading, responsiveness and visual stability. |
Good thresholds are LCP ≤ 2.5 s, INP ≤ 200 ms and CLS ≤ 0.1 at the 75th percentile, segmented by mobile and desktop. | Separate field measurement from laboratory tests. Record period, URL, device, population and sample size before comparing changes. | These thresholds are not measured results for this site. A good lab score alone does not establish visitor experience. |

