In this blog, we’ll discuss machine learning (ML) and artificial intelligence (AI), and how it’s currently being used in cybersecurity. Keep an eye out towards the end as WorkDone CEO, Joe Rogers, will join us in the discussion of what the future holds for these technologies and why companies should leverage ML and AI in cybersecurity.
WorkDone is a best practices discernment tool that retains corporate memory and leverages it to provide automation. WorkDone’s supervised machine learning tools allow companies to elevate employees by employing target training in order to improve performance.
Check out WorkDone.ai for more in-depth information on machine learning and artificial intelligence.
What is Machine Learning and Artificial Intelligence?
Machine learning (ML) is a subset of artificial intelligence (AI) which allows machines, through methods of unsupervised and supervised machine learning, to optimize processes and learn from data without being explicitly programmed. On the other hand, AI is creating algorithms and neural networks that can think and make decisions like humans, hence the term “artificial intelligence”. Currently, AI operates at shallow processing levels, which means it can handle simple and specific tasks. The end goal is to reach deep processing levels, which some experts predict will be more intelligent than humans.
Current Uses of Machine Learning and Artificial Intelligence in Cybersecurity
ML and AI is not an unheard of term in the cybersecurity realm, in fact, it’s quite common. Cybersecurity systems utilize ML to recognize patterns in user behavior to flag suspicious activities and thwart threat actors. They also provide IT teams with active monitoring so they can respond to attacks instantly. When combined, ML and AI can allow teams to be proactive, reduce the time spent on routine tasks, and help allocate valuable resources efficiently. However, it’s not as easy as it sounds. ML and AI require vast amounts of detailed data in order to function effectively.
“Machine learning has many use-cases in cybersecurity, from detecting potential risks to even stopping them. I think this [machine learning] technology is a valuable asset to any business and will continue to prove itself in being a required core competency.” – Joe Rogers, CEO of WorkDone, on machine learning in cybersecurity.
So, if the good guys have access to this technology, does that mean the bad guys do as well? Yes, but not exactly. The common trend in the constant battle in cybersecurity is that the good guys have always been a few steps ahead – thankfully. But, threat actors do in fact take advantage of the weaknesses that ML and AI have, and an AI arms race could be on the horizon. For example, threat actors can breach into the training model for ML and AI and ‘trick’ it into ignoring certain behaviors. That way, threat actors can slip into network environments without being detected.
Future Uses of Machine Learning and Artificial Intelligence in Cybersecurity
“Companies that don’t execute on integrating AI in the next couple years will be out of business in ten.” – Joe Rogers, CEO of WorkDone, on the value of machine learning and AI technology.
Technology is evolving constantly, and the day for deep level processing is soon approaching. So, what should companies do to stay ahead of threat actors and the global business environment?
Government and company entities should be developing methods of integrating AI and ML into their cybersecurity and business operations in order to stay proactive, innovative, and efficient.
Additionally, AI and ML may potentially see heavy usage in ransomware detection, recovery, and protection. Each year, new ransomware variants are created, which is an issue, but future technology could allow cybersecurity teams to transform existing ransomware variant data into an artificially intelligent algorithm that analyzes weaknesses and strengths within its own network to properly allocate resources in defense to ransomware.
Click HERE to check out more information on ML and AI from WorkDone.
If you have any questions or concerns, or need help, please reach out to Alvaka Networks at (877) 662-6624 or sales@alvaka.net. We operate 24x7x365 with all US based personnel.
Smoke testing is a term used to describe the testing process for servers after patches are applied. 
Estimating an average time for patching servers and PCs can be a bit tricky. It can vary from one month to the next, depending upon the number and complexity of the patches released by your software vendors. You must consider all versions of operating systems and have a complete inventory of all your application software to do this job correctly. Our experience has shown that manual patching of systems takes on average of about 1.5 hours each.
This is a basic cost calculator for you to compute your typical monthly cost for patching your servers, PCs, laptops, tablets and associated application software. It also forms the basis for you to begin calculating your Return on Investment for software patching, or for comparison with alternatives to the manual process of patching operating systems and application software—such as Patch Management as a Service, also known as Vulnerability Management as a Service.