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Cybersecurity professionals are in short supply. How can companies boost the efficiency of their existing cybersecurity staff?Â
Cybersecurity professionals are in short supply. How can companies boost the efficiency of their existing cybersecurity staff?Â
Automation and AI can help address the shortage of cybersecurity experts by analyzing complex issues, tracking malware infection paths, detecting insider attacks, and streamlining problem diagnosis and reporting data.
Automation and AI can help address the shortage of cybersecurity experts by analyzing complex issues, tracking malware infection paths, detecting insider attacks, and streamlining problem diagnosis and reporting data.
We need automation and AI to help with the shortage of cybersecurity experts.
There is a shortage of cybersecurity experts, so we need to use force multipliers like automation and artificial intelligence to be more efficient and effective.
AI can analyze complex issues using a knowledge graph as a data structure.
AI can be used to investigate issues by using a knowledge graph to represent information about the physical or logical world as a data structure.
A website's domain name leads to its IP address and URL directs to a specific file.
A website has a domain name that resolves to an IP address and a URL that links to a specific file on the file system.
A data structure can track malware infection paths and identify affected users and sites by connecting file system, URL, and user information.
Connecting information about a file system, URL, and user can create a data structure that shows the path of malware infection and identifies other affected users and sites.
AI systems can process and reason over information, while log records provide detailed event information on a system.
AI systems can represent information and do reasoning over it, while log records provide detailed information about events on a system.
Using machine learning and time decay, insider attacks can be detected by analyzing multiple records and identifying anomalous activities.
Using time decay function and machine learning, anomalous activities can be detected by looking at multiple factors across multiple records to identify potential insider attacks.
AI and machine learning can streamline problem diagnosis and reporting data, ensuring compliance with regulations.
AI and machine learning can be used to diagnose problems and enrich reporting data, saving time and ensuring compliance with regulatory requirements.
AI-powered chatbots can help with cybersecurity investigations and IBM's security software uses AI.
AI, specifically chatbots with natural language processing and knowledge bases, can assist in cybersecurity investigations and IBM's security software products include AI.
Key insights
Key insights
🤖 Artificial intelligence can be a force multiplier in cybersecurity, allowing us to work more intelligently and effectively.
🤖 AI can use knowledge graphs to investigate and identify issues, potentially speeding up the process of research and problem-solving in cybersecurity.
🔍 The use of knowledge graphs in cybersecurity can help identify the path of malware and potential affected users.
🔍 AI can help investigate cybersecurity issues by analyzing log records and doing reasoning over the information.
🔍 Using machine learning and time decay function can help identify anomalous activities and potential attack scenarios in cybersecurity.
đź”’ IBM includes AI in 100% of their security software products, highlighting the growing importance of AI in the cybersecurity space.
AI can detect and analyze complex patterns of malicious activity and quickly detect and respond to security threats. AI can also automate security processes, helping to reduce the time and effort needed to detect and respond to threats. Finally, AI can be used to detect and prevent malicious attacks before they can cause any damage. AI can also be used to identify and respond to new threats faster than traditional security processes. AI can continuously monitor for new security threats and alert system administrators when they are detected. As a result, organizations can be confident their systems are secure and up to date with the latest security protocols, protecting their data and applications from malicious actors. For instance, AI can identify unusual patterns in user behavior, flagging any suspicious activity for further investigation by security analysts. One concern with using AI for security is that it can create a false sense of security. If administrators rely too heavily on AI to secure their systems, they may become complacent and overlook other potential security threats. Additionally, AI-based security systems are not foolproof and can be fooled by sophisticated attackers. AI-based security systems provide improved accuracy and efficiency compared to traditional security solutions. As the French philosopher Henri Bergson once wrote: “Intelligence is the faculty of making artificial objects, especially tools to make tools.”
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Enterprise security architect here and, while this sounds good on paper, if you are not extremely careful with how you secure what information you are training the models on you will end up in a mess. To put more plainly, unless your security configuration is perfect, having an automated tool scan for information and load it into a LLM NLP system for people to "chat" with is going to turn ugly very quickly. After over 30 years of experience, I have never seen a perfectly secure network or system configuration. One mistake and false positive or negative reporting is going to be the least of your concerns when the FINRA / ESMA auditors ask why you included these tools on the network... there is a use case but the path is loaded with land-mines.
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What if I created Models which are domain specific ? Lets say for the example you illustrated there are multiple components like the Web Server,URI,IP Address , DNS etc. So let us assume the models are specifically trained on Web Server parameters (Mem/CPU/Web APplication Performance) , another model would be trained on the DNS data logs and so on . If the domain specific models could all work in tandem and a Parent model provides a final inference drawing from the inferences from the Domain Specific model. Do you think this is a possible option ? Additionally I would like to understand your views as to why LLM's prove an advantage for this use case .
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These AI tools will help speed up our Cybersecurity workflows
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I've been working as a software engineer for a few years. I'm getting my masters now and thinking about doing a pivot into security. My university does not offer much in security, so I have been learning ML/AI. Is it worth taking the CompTIA Security+ certification and then switching fields?
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How do you do pattern matching with ML? How do you train what is 'normal' and what might be a security issue? (4:20)
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This is very interesting. Personally I am thinking to go the ai/ml route, how can I become someone who can do ai in cyber security? Like do I need knowledge in both fields or just someone who can help in cyber sec? Just a beginner so I could be wrong.
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Great presentation.
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i want to know how to make AI more secure and how to block AI to take by himself decisions in cybersecurity.thanks
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Really Good, Jeff
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Is knowledge graph already assume in osint for cybersecurity
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How do I learn more
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guys i need some help i installed a crack version of video editing software , but when i start my laptop a command line terminal opened so i suspected and uninstalled the program but now a new pop up show that some .dll file in user folder is missing also that terminal at startup is still present , i have windows defender but on av scan it found nothing . Is my device still infected? as my cpu usage is very high and also i received an alert from google security alert on email.
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The speaker please?
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Did he just write in mirror?
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