Topics of discussion at this year's Black Hat conference included the Internet of Things (IoT) in smart cities, the latest advancements in vulnerability management and more.
IBM developed a new AI-enabled voice assistant to respond to common questions across email, corporate contacts and calendar using natural language processing (NLP) capabilities.
According to a new study conducted by the Ponemon Institute and sponsored by IBM, organizations could save an average of $2.5 million in operating costs by deploying artificial intelligence (AI).
Despite the risks quantum computing might pose to organizations, this emerging technology also promises to enhance cybersecurity capabilities such as SIEM, incident response and data protection.
Today's security teams lack the time, talent and resources to keep up with the rapidly evolving threat landscape. AI can automate tedious processes and take some pressure off security analysts.
Instead of replacing humans, machine learning will free up overworked IT analysts to focus on other tasks. However, the rising adoption of AI could expose companies to a new breed of insider threats.
As the threat landscape evolves to target connected devices, artificial intelligence (AI) and machine learning will become increasingly crucial parts of any organization's endpoint security strategy.
When the machine learning technology cyber defenders use to stop DDoS attacks inevitably falls into the hands of malicious actors, which side will win?
A recent study conducted by NIST researchers found that face identification performance is optimal when insights from a human expert are combined with a top machine learning algorithm.
While studies reveal the majority of CTI adopters are dissatisfied with threat intelligence machine learning adoption, there's evidence the adversary is already using algorithms to their advantage.