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CASE STUDIES - I MIGLIORI CASI DI SUCCESSO

VIDEO

Nel corso di un attacco informatico, il problema non riguarda solo la tecnologia. Anche il comportamento umano cambia. Sotto stress, infatti, attenzione, percezione del rischio e capacità decisionale possono ridursi, creando le condizioni che phishing e social engineering cercano di sfruttare. In questo intervento, lo psicologo Enrico Lorenzi spiega perché il cervello reagisce agli eventi improvvisi con meccanismi istintivi e quale impatto può avere questa risposta durante un incidente di cybersecurity. Nel video scoprirai: - perché lo stress influenza il processo decisionale; - come phishing e social engineering sfruttano le reazioni umane; perché il fattore umano è parte integrante della gestione del rischio cyber; - quale ruolo hanno formazione, consapevolezza e preparazione durante un incidente. Comprendere il comportamento umano significa comprendere una delle variabili che influenzano l'esito di un attacco informatico. ▶ Scopri di più sul mondo Cyberoo: https://www.cyberoo.com #Cybersecurity #SocialEngineering #Phishing #HumanRisk #CyberRisk #IncidentResponse #CyberAwareness #Cyberoo
Il cervello sotto attacco: perché phishing e social engineering funzionano
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLl9VbkdMRnJBZi1N

Il cervello sotto attacco: perché phishing e social engineering funzionano

5 Agosto 2026 9:00

A diferença entre uma avaliação estagnada e um contrato assinado muitas vezes se resume a uma única coisa: como você conduz o seu POV (Proof Of Value). Temos visto isso em diferentes regiões. Parceiros que estruturam seus POVs com critérios claros de sucesso, valor de negócio e acompanhamentos proativos fecham mais rápido e conquistam negócios maiores. E aqui está a ótima notícia: todos vocês podem fazer isso. Vocês já têm as ferramentas. Nós vamos mostrar exatamente como utilizá-las. É por isso que estamos convidando você para um webinar exclusivo para parceiros. O que você vai aprender: • A mudança de mentalidade de POC → POV que diferencia os parceiros de alta performance • Como qualificar e definir escopo de forma mais inteligente — caso de uso definido, critérios de sucesso acordados e as pessoas certas envolvidas • Como conduzir como um projeto, e não como um teste — processo passo a passo desde a reunião pré-POV até a implantação • Como manter os negócios avançando — o que o time de SE da Safetica espera de você, além de práticas de escalonamento que evitam POVs estagnados • Seu toolkit de POV pronto para uso — checklists, modelo de Scope of Work e documentos de POV Management & Scope • Onde encontrar materiais de Vendas e Marketing no Brandcloud, incluindo Battle Cards, apresentações e datasheets Isso não é teoria. É exatamente a abordagem que nossos parceiros com maior taxa de conversão estão usando hoje para transformar avaliações em receita. Queremos que todos os parceiros Safetica tenham essa mesma vantagem.
Safetica Ganhe Mais Negócios com um POV Mais Inteligente
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLi1pTmp3cXZCbTRj

Safetica Ganhe Mais Negócios com um POV Mais Inteligente

2 Agosto 2026 22:06

NIS2 & Incident Response | Andrea Coli e Massimiliano Testoni | Cyberoo Black Club 2026
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLkxPVnRFMWR6NkxB

NIS2 & Incident Response | Andrea Coli e Massimiliano Testoni | Cyberoo Black Club 2026

31 Luglio 2026 15:45

With July coming to a close, ESET Chief Security Evangelist Tony Anscombe (https://www.welivesecurity.com/en/our-experts/tony-anscombe/) looks back at some of the top cybersecurity stories that made the news over the past month and offers insights that they may hold for your own cyber-defenses. Here's some of what caught Tony's attention this month: • OpenAI models went rogue (https://www.darkreading.com/cyber-risk/openai-models-autonomously-hack-hugging-face) and autonomously broke into AI collaboration platform Hugging Face in what OpenAI described as "an unprecedented cyber incident", • Researchers at Sysdig (https://www.sysdig.com/blog/jadepuffer-agentic-ransomware-for-automated-database-extortion) have documented what they assess to be the first case of an end-to-end ransomware operation executed by an agentic threat actor, which the company named JADEPUFFER. (https://www.bleepingcomputer.com/news/security/jadepuffer-agentic-attacks-now-target-ai-model-data-with-ransomware/) • Cybercriminals are taking advantage of a new large language model (LLM)-driven attack vector called "phantom squatting" (https://www.darkreading.com/endpoint-security/phantom-squatting-ai-driven-supply-chain-threat) by purchasing domains linked to legitimate brands, then using them to intercept traffic directed there by AI systems. What can organizations do to stop phantom squatting from harming their brands, and what other lessons do these incidents hold for defenders? Watch Tony's video to find out and be sure to check out the June 2026 (https://www.welivesecurity.com/en/videos/month-security-tony-anscombe-june-2026/) edition of Tony's monthly security news roundup for more insights. Before you go, learn about the first AI-powered ransomware, named PromptLock (https://www.welivesecurity.com/en/ransomware/first-known-ai-powered-ransomware-uncovered-eset-research/) and discovered by ESET researchers last year. To learn more about cutting-edge AI defense layers, read the AI at ESET white paper. (https://web-assets.esetstatic.com/wls/en/papers/white-papers/artificial-intelligence-at-eset.pdf) All this – and more – on https://www.welivesecurity.com/en/
Month in security with Tony Anscombe – July 2026 edition
YouTube Video VVVrTjdJUFgtZFZFc0Fkam9MZEZkYTFnLkR6T1I1T1NWZ2RJ

Month in security with Tony Anscombe – July 2026 edition

31 Luglio 2026 15:24

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 1
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLkNzb2d4WC1vR3hv

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 1

29 Luglio 2026 15:04

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 2
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLktIcWV1Q3ZRT25F

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 2

29 Luglio 2026 15:04

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 4
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLmJzUC03eFNoYlYw

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 4

29 Luglio 2026 15:04

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 3
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLmQ1Y21JVk1xMlA4

Cyberoo - KEATRIX | Good habits don't go offline | Pillola 3

29 Luglio 2026 15:04

Dimentica l'immagine del ragazzino appassionato di informatica che agisce da solo. Nel 2026, il cybercrime si è trasformato in un'industria strutturata e pericolosa In questo video analizziamo l'evoluzione delle minacce informatiche. Oggi il crimine informatico è rappresentato da vere e proprie aziende organizzate che operano in modo speculare alle imprese legali, agendo in quello che possiamo definire un "sottosopra" economico Queste organizzazioni non sono spinte dal semplice gusto della sfida, ma hanno: - Motivazioni economiche chiare (denaro) - Mezzi tecnologici avanzati - Obiettivi precisi: attaccare le aziende normali per sottrarre loro valore Capire come sono strutturate queste "aziende del male" è il primo passo per implementare una strategia di cybersecurity efficace e proteggere gli asset della tua impresa. #cybercrime2026 #cybersecurity #sicurezzainformatica #cyberoo #attacchihacker #aziendecriminali #protezioneaziendale #crimineinformatico
Cos'è oggi il Cybercrime e com'è organizzato? #cybersecurity #cyberoo
YouTube Video VVU3QXdWREc5Tmdkd29oa196UGQyNXhRLmF2VXZYbGJicE13

Cos'è oggi il Cybercrime e com'è organizzato? #cybersecurity #cyberoo

29 Luglio 2026 10:40

Classifying sensitive data usually means manual tagging or writing rules. Safetica skips both. Safetica automatically detects sensitive data across files, emails, and cloud storage, no manual tagging, no rule writing, and no disruption to the people actually using the data. It runs in the background across Microsoft 365 and beyond, so classification happens without slowing anyone down. See how Safetica works here: safetica.com #DataClassification #DLP #Microsoft365 #DataSecurity #Safetica
How Safetica Classifies Sensitive Data Without Disrupting the People Who Use It
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLlczU0cyNzQyblU4

How Safetica Classifies Sensitive Data Without Disrupting the People Who Use It

29 Luglio 2026 0:58

In this short session, John Paist of @SafeticaVideo walks through five insider risk patterns that show up consistently in Safetica's platform data, and explains why traditional policy-based DLP is not built to catch them. The brief discussion covers sensitive data going into public AI tools, departing employees moving records during their notice period, abnormal after-hours access, privileged user misuse, and gradual, low-volume data exfiltration. It closes with research on how common and how costly insider incidents have become, and where behavioral context fits alongside DLP. Visit safetica.com to learn more Full Transcript: Hello, folks. This is John Paist with Safetica. Over the next few minutes, I'll walk you through patterns we consistently see in our platform data: blind spots that traditional policy-based DLP was never built to catch. Most of you already have a DLP tool in place. The question isn't whether it works, it's what actions it's missing. Our data keeps identifying five patterns that live outside what a traditional policy engine is designed to flag. First, sensitive data shared with AI tools, source code, customer PII, PHI, and more, pasted into ChatGPT and similar tools well outside any document-based rule. Second, departing employees taking data, records, and IP during a notice period, while access is still fully valid. Third is abnormal after-hours access: late-night activity that looks like real work but may not be. Fourth is privileged user misuse: legitimate credentials used to access finance, HR, or other files they shouldn't touch. Finally, gradual data exfiltration: small, repeated transfers sized to stay under alert thresholds. None of these are exotic attacks. They're everyday access, used in ways that would never trigger a traditional policy. Take the first blind spot, everybody's favorite topic: AI. In our H2 2025 data, ChatGPT alone accounted for just over 20% of all blocked AI activity, ahead of Gemini, Copilot, and other tools combined. This isn't hypothetical. A regional healthcare provider we work with found staff sharing PHI into public AI tools. With Safetica in place, they had full visibility immediately and applied controls within days, without shutting off the tools employees need day to day. The goal is never to block these tools outright. It's to see how they're used and act on that data. The second blind spot is departing employees. A manufacturing client had an employee resign, give standard two-week notice, and quietly begin exfiltrating data. This happens every day. Safetica flagged the abnormal movement early enough for the team to act before the employee's final day. The stakes aren't abstract. They include loss of business, eroded customer trust, and exposure of PII and payment data. A resignation letter doesn't revoke access, it starts a clock. Why does this matter now, not someday? IBM's Cost of a Data Breach report puts malicious insiders at roughly 9% of reported breaches, meaning 91% of insider breaches are non-malicious: ordinary people making decisions that create exposure without malicious intent. This is common. Gartner research found insider incidents affect 57% of organizations, more than half the room. It's also expensive: the same research puts the average annual cost of an insider event at $17 million. Normal, common, expensive: reason enough to treat insider risk as a first-class problem, not a side project bolted onto a DLP tool. Where does Safetica fit into closing this gap? Policy-based DLP is a necessity, but on its own it stops at the policy. It can't do four things that matter most in the scenarios above. It can't catch risk early, since it reacts only once a rule is triggered, not when behavior starts to drift. It can't reduce false alarms, since without behavioral context every alert looks the same. And it can't tell you whether you're facing an honest mistake, a compromised account, or something malicious, so it can't get you a faster, more accurate response. That's the layer Safetica adds. Other tools stop at DLP; we add the behavioral layer and combine it with insider risk management, so you get full context on every case, not just a violation count. We do it all in one platform for one price, without the complexity of stitching together separate modules for UEBA, data discovery, app and web filtering, device management, email security, and DLP. One pane of glass, simple and straightforward. A quick word on who we are. Safetica protects over a million devices across 120-plus countries as an intelligent data security platform. Five blind spots, real costs when unaddressed, and one platform built to close the gap.
The 5 Insider Risks Your DLP Tool Never Sees
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLkVwUnZoNTB1enBr

The 5 Insider Risks Your DLP Tool Never Sees

28 Luglio 2026 19:08

Takeaway 4 from Safetica's H2 2025 Data Protection Trends Report: email activity triggered 72% of all threat-level warnings, and USB policy violations surged in Q4. Neither pattern fits a malicious profile. Here's why behavioral analytics and risk scoring matter more than one-off alerts. Get the full report: https://hubs.li/Q04qWW9Y0 For the last takeaway: most insider risk is behavioral, not exceptional. Most people think that with data security tools, you're going to catch the "juicy" event. I'm sure that happens, but in reality most events are nothing special. We saw that in H2 2025, email activity triggered 72% of all threat-level warnings, and USB policy violations surged heavily in Q4 as well. Neither of these fits a malicious profile. It's just things that happen day-to-day, and filtering through that noise can be difficult. Most insider risk solutions are built to catch rare bad actors, but the real exposure is in normal employees doing these things thousands and thousands of times. That's exactly why it's so necessary to have behavioral analytics and risk scoring, so you can narrow down and find the real events. We need to reframe insider risk away from "bad actors," because some of these things hide in normal day-to-day activities. If you put a blanket over it and look at it through that lens, it's very difficult to uncover the actual malicious threats hiding in those unexceptional events.
Insider Risk Is Behavioral, Not Exceptional
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLm9fUlZCVHhvZXhn

Insider Risk Is Behavioral, Not Exceptional

27 Luglio 2026 22:10

Takeaway 4 from Safetica's H2 2025 Data Protection Trends Report: email activity triggered 72% of all threat-level warnings, and USB policy violations surged in Q4. Neither pattern fits a malicious profile. Here's why behavioral analytics and risk scoring matter more than one-off alerts. Get the full report: https://hubs.li/Q04qWW9Y0 For the last takeaway: most insider risk is behavioral, not exceptional. Most people think that with data security tools, you're going to catch the "juicy" event. I'm sure that happens, but in reality most events are nothing special. We saw that in H2 2025, email activity triggered 72% of all threat-level warnings, and USB policy violations surged heavily in Q4 as well. Neither of these fits a malicious profile. It's just things that happen day-to-day, and filtering through that noise can be difficult. Most insider risk solutions are built to catch rare bad actors, but the real exposure is in normal employees doing these things thousands and thousands of times. That's exactly why it's so necessary to have behavioral analytics and risk scoring, so you can narrow down and find the real events. We need to reframe insider risk away from "bad actors," because some of these things hide in normal day-to-day activities. If you put a blanket over it and look at it through that lens, it's very difficult to uncover the actual malicious threats hiding in those unexceptional events.
Insider Risk Is Behavioral, Not Exceptional
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLk16d1ZCWE9ObzVJ

Insider Risk Is Behavioral, Not Exceptional

27 Luglio 2026 22:07

Takeaway 3 from Safetica's H2 2025 Data Protection Trends Report: text files and screenshots ranked among the top blocked file types, and ChatGPT blocks climbed 86% from Q3 to Q4. Data protection has to cover more than documents now. Get the full report: https://hubs.li/Q04qWW9Y0 Let's move to the third of four takeaways. Data loss has moved beyond documents. Historically, blocked TXT files and screenshots are ranking amongst the top blocked files in H2 2025, not just PDFs and Word docs. A lot of folks think this primarily comprises PDFs, Word docs, PowerPoints, and Excel sheets, but text files are becoming increasingly popular. We believe a big push for that is AI-related tools. ChatGPT blocks surged 86% from Q3 to Q4, as people feed sensitive and unstructured information into these AI tools. Whether it's a company account or not, we're seeing a really large increase in what people are feeding into these tools. Think about how many people at your organization are working with these tools and putting data into them. Do you know exactly what people are feeding into them? That can be difficult to find out. That's why you need a tool that can uncover this information and dive into the context of what's occurring on the endpoints and in the environment.
Beyond Documents: How AI Tools Are Reshaping Data Loss
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLndOdkxiX1lRLVhN

Beyond Documents: How AI Tools Are Reshaping Data Loss

27 Luglio 2026 22:05

Takeaway 2 from Safetica's H2 2025 Data Protection Trends Report: when a channel gets blocked, users don't stop, they switch. Encrypted messaging use surged past 64% in Q4. Here's why behavior-aware, adaptive policies beat static rules. Get the full report: https://hubs.li/Q04qWW9Y0 Let's move on to the second takeaway. And what we're seeing is that static rules just can't keep up with real everyday behavior. If you block one channel, users aren't going to just stop and say "oh no, I can't do it." They're going to find another way. For example, we saw encrypted messaging app usage surge 64%+ in Q4, which is pretty crazy. And if you're blocking email attachments one day, the next day people go around that using WhatsApp or Signal or whatever else it may be. People adapt. It's a never-ending cat-and-mouse game between users and admins trying to restrict every channel they find, only to see people find another one the next day, and the next, and the next. That's why it's so important to have behavior-aware policies in place, not static ones. If you don't, you're playing catch-up forever. You need something that can learn and adapt on the fly.
Static Rules Can't Keep Up: Why Users Route Around Blocked Channels
YouTube Video VVU2V0ZYVXZlSzNSVEttbklTUGgwR0RnLmpaUEI1emV2VEJB

Static Rules Can't Keep Up: Why Users Route Around Blocked Channels

27 Luglio 2026 22:03

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