This question examines the transformative impact of large language models (LLMs) on cybersecurity, looking at both the potential benefits—such as automating threat detection and refining incident response—and the emerging risks tied to AI-driven attacks. It prompts an exploration of how LLM capabilities, like natural language processing and real-time analytics, can bolster defense strategies, while also highlighting the novel threats these same technologies may introduce, including more convincing social engineering attempts and rapid development of malicious code.
Large language models (LLMs) may enhance cybersecurity by generating threat intelligence, detecting phishing, and automating incident response.
DeepSeek’s Popular AI App Is Explicitly Sending US Data to China
Amid ongoing fears over TikTok, Chinese generative AI platform DeepSeek says it’s sending heaps of US user data straight to its home country, potentially setting the stage for greater scrutiny...
“It shouldn’t take a panic over Chinese AI to remind people that most companies in the business set the terms for how they use your private data… And that when you use their services, you’re doing work for them, not the other way around.”
Security concerns raised over the fact that DeepSeek stores user data on servers in China should remind users not to disclose private information to any chatbots, notes John Scott-Railton, who studies cybersecurity, cyber warfare and disinformation...
https://www.wired.com/story/deepseek-ai-china-privacy-data/?__readwiseLocation=
Large language models (LLMs) can transform cybersecurity both in terms of new threats and groundbreaking benefits. On the bright side, through the use of real-time analytics and advanced natural language processing, LLMs can enhance threat detection and incident response to allow for faster vulnerability discovery and more efficient attack mitigation. To release human experts to work on more complex issues, they can also automate mundane security activities such as generating security policies or log analysis. LLMs can, however, also bring new risks in that malicious actors can use their capabilities to produce highly sophisticated-looking phishing emails, automate code generation of malware, and produce extremely realistic social engineering attacks. Due to its dualistic nature, it must be guarded firmly by AI-based defense systems and moral regulations.
we can use hash indexing and DES encryption techniques to protect and verify the the integrity of unauthorised user
LLMs are a powerful tool for cybersecurity, helping detect threats and educate users. But they also give hackers new ways to launch smarter attacks. The key is using AI responsibly, strengthening security, and staying vigilant—because in the end, it’s how we use AI that matters.
The increasing development of large language models could be an enormous game-changer in cybersecurity by simultaneously making it easier and harder. On one hand, this technology will make the detection of threats automatic, scan vulnerabilities, and prevent phishing, improving the response times and accuracy of those responses. Yet with them also come new risks that malicious actors could use to craft convincing phishing emails, create complex social engineering attacks, or automatically generate malware. As LLMs become increasingly available, the ability of cybersecurity professionals to evolve rapidly will mean making full use of advanced AI models in a way that effectively counters emerging threats.@Bikash Saha
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