AI chatbots have become valuable tools for productivity and research, as well as creative endeavors like image generation and brainstorming. However, the convenience they offer can draw attention away from the risks associated with sharing sensitive information with these chatbots.
As artificial intelligence continues to push the boundaries of technological innovation, it has also provided hackers with new tools to carry out more advanced social engineering attacks. Adding fuel to fire, the shift to remote and hybrid work has increased online interactions, expanding the attack surface for malicious activity.
The foundation of social engineering is in manipulating human emotions to trick people into revealing personal information or compromising security, like clicking on a malicious link. However, AI-powered social engineering goes a step further by creating highly personalized and convincing attacks that are difficult to detect.
Another year, and an even greater need for increased cybersecurity awareness for everyone. With the increased use of AI this year, a lot of companies are moving from cloud to on-premise and security should remain one of the top priorities for all. Read on for some tips and best practices that can protect organizations, consumers, and the everyday user.
What if we could replicate the brain’s capacity for pattern recognition, problem-solving, and decision making, and put it to work in a machine that can learn and improve over time?
Neural networks are that mechanism—modeled after the interconnected structure and reasoning function of the human brain. Just as the brain learns by engaging with the world through sensory input, neural networks learn by experiencing the world through data and algorithms.
*Read the full wrap up report on how Kaidan’s model performed here
When I was an intern at IG Labs, one of my projects was to create my own machine learning model. Being a huge football fan, I decided to create a model that would make inferences on upcoming NFL games.
My intern days are long past and I’m now a full-fledged member of the IG Labs team, but, my “passion project” has stood the test of time. I’ve continued to maintain my NFL predictor over the past few years and the model has demonstrated an accuracy rate of 65%.
RAG, or Retrieval Augmented Generation, is a technique that enables a large language model (LLM) to access information from external sources.
While LLMs like ChatGPT and Claude are incredibly powerful tools, they can only answer questions based on their specific training data, which has a cutoff date and relies on public information. This means they’re not helpful when it comes to your proprietary data or any information outside their training.
Large language models (LLMs) have become an indispensable tool in cybersecurity, offering significant solutions to the challenges facing Security Operations Center (SOC) analysts. By simulating human-level performance, these advanced AI models are truly transforming the field. At IntelliGenesis, our developers and data scientists are already incorporating LLM capabilities into tools like KrakenAI™, our cybersecurity-focused LLM platform that delivers real-time assistance to cybersecurity analysts and incident response teams defending critical networks.
If knowledge is power, then having the ability to quickly process and use data is paramount. Even more so is the accuracy and consistency of the data processing. This becomes more critical amidst the rapid promulgation of AI/ML models which rely heavily on data.
The data used in AI/ML models must be evaluated and curated. After all, bad data will yield bad results. For example, if a model is intended to calculate a specific course of action to be taken and the data that is input is corrupted, the likely result would be a course of action taken that could have negative impacts.
As many of us do, some more frequently than others, I recently upgraded my cell phone. In my case, it was not about racing to get the latest thing to hit the market, but rather the integrated AI translation capabilities. After studying the reviews and the features, I felt it was more of a necessity.
Why a necessity? There is a bit of a language barrier in our house as my father-in-law lives with us and speaks very little English. Though I have picked up a few keywords and phrases since he moved in seven years ago, I am far from being able to speak the Korean language and my wife is not always there to translate.
Whether it is Fitbit, Apple, Samsung, or Garmin, new wearable Bluetooth technology devices are constantly joining the market. Tracking everything from step count, distance traveled, and heart rate, even a basic cardiogram has been built into wearables as capabilities have advanced. As a result, it has become more convenient for people to track more of their routines such as diet and exercise since wearables communicate directly with apps on their phones, tablets, and computers. The advances have also made it easier to share this data with family, friends and, more importantly, their physicians to set goals for improving one’s health.