{"id":160322,"date":"2024-05-08T06:30:00","date_gmt":"2024-05-08T13:30:00","guid":{"rendered":"https:\/\/blog.everpuredata.com\/?p=160322"},"modified":"2025-03-27T22:52:46","modified_gmt":"2025-03-28T05:52:46","slug":"the-threat-of-ai-powered-ransomware-attacks","status":"publish","type":"post","link":"https:\/\/blog.everpuredata.com\/pt-br\/perspectives\/the-threat-of-ai-powered-ransomware-attacks\/","title":{"rendered":"A amea\u00e7a de ataques Ransomware com intelig\u00eancia AI"},"content":{"rendered":"\n
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Resumo<\/h3>\n\n\n\n

AI-powered cyberattacks are more sophisticated and more potent. To combat this increased risk, organizations need to invest in robust cybersecurity defenses, including a tiered resiliency architecture.<\/p>\n<\/div>\n\n\n\n

\"image_pdf\"<\/a>\"image_print\"<\/a><\/div>\n<\/div>\n\n\n\n
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Juntamente com seus muitos benef\u00edcios, a AI tamb\u00e9m capacitou agentes maliciosos a lan\u00e7ar ataques cibern\u00e9ticos cada vez mais sofisticados, com o ransomware surgindo como uma amea\u00e7a particularmente potente.\u00a0<\/p>\n\n\n\n

Um relat\u00f3rio<\/a> recente do National Cyber Security Centre (NCSC) do Reino Unido alertou que invasores maliciosos j\u00e1 est\u00e3o aproveitando a AI para desenvolver ataques ransomware, criando riscos significativos para indiv\u00edduos, empresas e at\u00e9 mesmo infraestrutura cr\u00edtica. Agentes de amea\u00e7as como o APT28 t\u00eam estado ocupados usando modelos de linguagem grande (LLMs, large language models) em movimentos elaborados para evitar a detec\u00e7\u00e3o e executar reconhecimento avan\u00e7ado.<\/p>\n\n\n\n

Veja mais detalhes sobre essas amea\u00e7as e como se manter resiliente contra elas.<\/p>\n\n\n\n

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A resili\u00eancia cibern\u00e9tica exige trabalho em equipe<\/h2>\n\n\n\n

Saiba como as equipes de TI e seguran\u00e7a podem colaborar com facilidade para reduzir as vulnerabilidades cibern\u00e9ticas e evitar ataques. Obtenha os resultados da pesquisa e perspectivas de analistas para aumentar a resili\u00eancia cibern\u00e9tica.<\/p>\n<\/div>\n\n\n\n

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Saiba como<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

Como a AI adiciona sofistica\u00e7\u00e3o e expans\u00e3o aos ataques<\/strong><\/h2>\n\n\n\n

Ransomware t\u00eam sido uma amea\u00e7a persistente h\u00e1 anos, e a integra\u00e7\u00e3o de t\u00e9cnicas de AI est\u00e1 elevando esses ataques a um novo n\u00edvel de sofistica\u00e7\u00e3o, velocidade e escala.\u00a0<\/p>\n\n\n\n

Adapta\u00e7\u00e3o e personaliza\u00e7\u00e3o em tempo real.<\/strong> O ransomware com intelig\u00eancia AI \u00e9 capaz de adaptar suas t\u00e1ticas em tempo real e modificar o c\u00f3digo de malware para evitar a detec\u00e7\u00e3o. Os LLMs podem ser usados para alterar o c\u00f3digo-fonte de um malware para evitar regras de acionamento, como regras YARA, que identificam padr\u00f5es em fam\u00edlias de malware para alertar um poss\u00edvel ataque.\u00a0<\/p>\n\n\n\n

Automatiza\u00e7\u00e3o de ataques<\/strong>. A AI pode automatizar v\u00e1rios est\u00e1gios do processo de ataque, aumentando a efici\u00eancia dos ataques e reduzindo a necessidade de interven\u00e7\u00e3o humana.<\/p>\n\n\n\n

Fraquezas direcionadas \u00e0 precis\u00e3o.<\/strong> Ao aproveitar a AI para reconhecimento e avalia\u00e7\u00e3o de vulnerabilidade, o ransomware com intelig\u00eancia AI pode explorar os pontos fracos nas defesas de ciberseguran\u00e7a existentes com precis\u00e3o alarmante. Os invasores podem identificar e explorar pontos de entrada que as defesas tradicionais podem ignorar. Isso inclui vulnerabilidades de dia zero e configura\u00e7\u00f5es incorretas em software e sistemas, complicando ainda mais a tarefa de defesa contra esses ataques. Algoritmos de aprendizado de m\u00e1quina (ML, Machine Learning) podem analisar grandes quantidades de dados para identificar poss\u00edveis alvos, criar e-mails de phishing convincentes e at\u00e9 mesmo personalizar demandas de resgate com base no perfil da v\u00edtima.<\/p>\n\n\n\n

Ataques de acompanhamento bem informados. <\/strong>Modelos de linguagem multimodal (MMLMs, Multimodal Language Models)<\/a> que podem analisar v\u00eddeos e fotos de instala\u00e7\u00f5es, equipamentos e outras informa\u00e7\u00f5es publicamente dispon\u00edveis podem ajudar os invasores a obter metadados, vers\u00f5es de software e dados de geolocaliza\u00e7\u00e3o para entender especifica\u00e7\u00f5es t\u00e9cnicas para aprofundar ataques.\u00a0<\/p>\n\n\n\n

O deep fakes e o \u201cbrandjacking\u201d com AI generativa tamb\u00e9m podem ser usados para atrair as v\u00edtimas a fornecer credenciais com sites realistas e leg\u00edtimos, \u201cinfluenciando as opera\u00e7\u00f5es\u201d, com artigos de not\u00edcias gerados por AI ou v\u00eddeos falsos.<\/p>\n\n\n\n

Mais riscos exigem mais resili\u00eancia<\/strong><\/h2>\n\n\n\n

As consequ\u00eancias dos ataques ransomware com intelig\u00eancia AI podem ser devastadoras: perdas financeiras significativas, danos \u00e0 reputa\u00e7\u00e3o e at\u00e9 mesmo interrup\u00e7\u00f5es operacionais. Em alguns casos, o pagamento pode ser a \u00fanica op\u00e7\u00e3o para recuperar dados criptografados, perpetuar o ciclo do cibercrime e incentivar ataques futuros.\u00a0<\/p>\n\n\n\n

Para combater a onda crescente de ataques ransomware com intelig\u00eancia AI, \u00e9 necess\u00e1ria uma arquitetura de seguran\u00e7a em camadas e resiliente a dados<\/a>. Isso inclui investir em defesas robustas de ciberseguran\u00e7a que aproveitam a AI e o aprendizado de m\u00e1quina para detec\u00e7\u00e3o e resposta a amea\u00e7as. Ao analisar o tr\u00e1fego de rede, o comportamento do usu\u00e1rio e a atividade do endpoint em tempo real, as solu\u00e7\u00f5es com intelig\u00eancia AI podem ajudar as organiza\u00e7\u00f5es a identificar e mitigar amea\u00e7as de ransomware antes que elas causem danos.<\/p>\n\n\n\n

Mas nem todas as arquiteturas de seguran\u00e7a s\u00e3o criadas da mesma forma, e isso pode ser a diferen\u00e7a entre voltar a ficar online em horas ou dias. Em um blog anterior<\/a>, dissipamos alguns mitos sobre lacunas de ar. Tamb\u00e9m discutimos a an\u00e1lise de seguran\u00e7a e os sistemas SIEM<\/a> e analisamos detalhadamente os benef\u00edcios dos bunkers de dados<\/a>, incluindo uma arquitetura de exemplo para voc\u00ea come\u00e7ar.<\/p>\n\n\n\n

Al\u00e9m disso, as organiza\u00e7\u00f5es devem priorizar a conscientiza\u00e7\u00e3o e a educa\u00e7\u00e3o sobre ciberseguran\u00e7a<\/a> para capacitar os funcion\u00e1rios a reconhecer e relatar atividades suspeitas. O phishing continua sendo um vetor comum para ataques de ransomware, e as pessoas desempenham um papel crucial para impedir essas tentativas ao exercer cautela e vigil\u00e2ncia on-line.<\/a><\/p>\n\n\n\n

A colabora\u00e7\u00e3o entre partes interessadas do setor, ag\u00eancias de aplica\u00e7\u00e3o da lei e especialistas em ciberseguran\u00e7a \u00e9 essencial para compartilhar intelig\u00eancia contra amea\u00e7as, desenvolver pr\u00e1ticas recomendadas e coordenar respostas a ataques ransomware. Ao trabalharmos juntos, podemos melhorar nossa resili\u00eancia coletiva e nos defender melhor contra o cen\u00e1rio de amea\u00e7as em evolu\u00e7\u00e3o. Agora \u00e9 a hora de dar o pr\u00f3ximo passo na moderniza\u00e7\u00e3o de suas solu\u00e7\u00f5es de prote\u00e7\u00e3o de dados<\/a>. Saiba mais sobre o risco de um ataque ransomware e comece a proteger sua empresa<\/a> contra ataques ransomware.<\/p>\n\n\n\n

\"\"\/<\/figure>\n<\/div>\n\n\n\n
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Escrito por:<\/strong><\/p>\n\n\n

Roger Boss<\/a><\/div><\/div>\n<\/div>\n\n\n\n
<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

A AI est\u00e1 elevando os ataques ransomware a um novo n\u00edvel de sofistica\u00e7\u00e3o, velocidade e escala. 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