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🤖 AI for Security 🛡️
Each week, I share practical advice & lessons learned from applying GenAI & LLMs to cyber security. Here are some of the latest.
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🐞 Can AI Actually Find Real Security Bugs? Testing the New Wave of AI Reasoning Models by Marcin Niemiec:
🔗 https://lnkd.in/esK3zS5Y
💻 BoxPwnr by Francisco Oca González:
An experimental project exploring the use of Large Language Models (LLMs) to solve HackTheBox machines autonomously, with minimal human intervention.
🔗 https://lnkd.in/e7jNRMEb
🐜 Revolutionizing software testing: Introducing LLM-powered bug catchers – Engineering at Meta:
🔗 https://lnkd.in/ePJcVGjB
🤖 Salesforce’s Mor Levi on Transforming Security Operations with AI Agents – Detection at Scale Jack Naglieri:
🔗 https://lnkd.in/etnyBZsx
🛠️ AI-Powered Vulnerability Impact Analyzer by Alex Devassy:
Uses agentic AI with open source models to understand CVEs and verify actual vulnerability impact in your codebase.
🔗 https://lnkd.in/eYpPrSJK
📊 Let’s Build Enterprise Cybersecurity Risk Assessment Using AI Agents:
Aniket Hingane built an app that uses multiple AI agents (Security Architect, Risk Analyst, and Compliance Officer) to automatically review security proposals from different perspectives.
🔗 https://lnkd.in/eXWHWYMi
💽 AI Agentic Cybersecurity Tools: Reaper, TARS, Fabric Agent Action, and Floki:
What are agentic applications in cybersecurity? Systems that can autonomously perceive, decide, and act on security tasks.
🔗 https://lnkd.in/ea3DqTFu
What Undercode Say
The integration of AI into cybersecurity is revolutionizing how we approach security challenges. From autonomous bug detection to AI-driven vulnerability analysis, the potential for AI to enhance security operations is immense. Tools like BoxPwnr and AI-Powered Vulnerability Impact Analyzer demonstrate how AI can autonomously handle complex tasks, reducing the burden on human analysts and increasing efficiency.
In the realm of Linux and IT security, commands like `nmap` for network scanning, `grep` for pattern searching, and `chmod` for file permissions are essential. For Windows, PowerShell commands such as `Get-Process` for monitoring running processes and `Test-NetConnection` for network diagnostics are invaluable. AI tools can automate these tasks, providing real-time insights and responses.
For instance, using `nmap` with AI integration can automate network vulnerability scans:
nmap -sV --script=vuln <target_ip>
AI can also enhance log analysis. Combining `grep` with AI-driven anomaly detection can identify suspicious activities:
grep "Failed password" /var/log/auth.log | ai_analyze
In Windows, AI can streamline security monitoring:
Get-EventLog -LogName Security -Newest 100 | ai_analyze
The future of cybersecurity lies in the seamless integration of AI with traditional security practices. By leveraging AI, we can create more resilient systems capable of anticipating and mitigating threats before they materialize. The resources provided in this article offer a glimpse into this future, showcasing practical applications of AI in cybersecurity.
For further reading, explore the links provided to deepen your understanding of AI’s role in cybersecurity. The journey towards AI-enhanced security is just beginning, and the possibilities are limitless.
🔗 https://lnkd.in/esK3zS5Y
🔗 https://lnkd.in/e7jNRMEb
🔗 https://lnkd.in/ePJcVGjB
🔗 https://lnkd.in/etnyBZsx
🔗 https://lnkd.in/eYpPrSJK
🔗 https://lnkd.in/eXWHWYMi
🔗 https://lnkd.in/ea3DqTFu
References:
Hackers Feeds, Undercode AI
