The hottest technology right now is AI - more specifically, generative AI. The trend is so popular that every conference and webinar speaker feels obligated to mention some form of AI, no matter their field.
The heavy focus on this technology overshadows an important component of artificial intelligence: machine learning.
Cybersecurity tools rely on ML to use predictions and patterns to find anomalies and sniff out potential threats.
The reason why we're talking about AI so much now is because generative AI is a game changer in the way we communicate with technology.
ML algorithms rely on historical data to detect patterns, from software codes to customer shopping behaviors.
Social media networks rely on ML algorithms to keep relevant information at the top of your feed.
Supervised learning trains ML to perform a specific task based on the presented data.
Unsupervised learning relies on relationships across the data.
Reinforcement learning is most similar to human learning, where the ML model learns to solve problems through trial-and-error formats.
As AI continues to advance, so does ML, and one of the most anticipated improvements to ML in 2024 will be no-code machine learning.
No-code ML relies heavily on behavioral data and plain English to get results.
One of the biggest benefits of no-code ML is that it allows companies of all sizes to implement ML and AI into their networks without the need to hire data analysts and engineers.
The downfall is that this type of ML technology is limited and won't be able to do deep-dive predictive analysis.
Unsupervised and reinforcement ML are both expected to expand in the coming year, in part because of no-code ML. As ML evolves, we will likely see growth in other technologies like augmented reality and quantum computing.
ML will play a role in improved facial recognition technology and interactions with generative AI. Machine learning and security - the good and bad. As mentioned earlier, ML benefits your overall cybersecurity program by automating what were once cumbersome manual tasks.
As with any technology, there are security risks involved.
Threat actors use ML and AI to launch attacks by poisoning or misleading the data to trick the system into providing false reports.
Threat actors use this to bypass security systems and hijack the network.
AI and its role in security is what everyone wants to talk about today, but the ways that AI can improve your company's security systems depend on machine learning.
The time has come to get back to the basics and recognize how ML fits into your security system and how to best train ML so that your AI is even more effective.
This Cyber News was published on securityintelligence.com. Publication date: Thu, 18 Jan 2024 15:13:04 +0000